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Report to the Subcommittee on Legislative Branch, Committee on 

Appropriations, U.S. Senate:



United States General Accounting Office:



GAO:



November 2002:



Technology Assessment:



Using Biometrics for Border Security:



Biometrics in Border Control:



GAO-03-174:



Contents:



Letter:



Technology Assessment Overview:



Purpose:



Background:



Results in Brief:



Border Control Overview:



Biometric Technologies:



Scenarios for Using Biometric Technologies for Border Security:



The Role of Biometrics in Border Security:



Chapter 1: Introduction:



The Federally Mandated Biometric Chimera System:



An Overview of This Report:



Chapter 2: Todays U.S. Border Control Procedures:



How U.S. Passports Are Issued:



How U.S. Visas Are Issued:



Inspection at U.S. Ports of Entry:



Chapter 3: Biometric Technologies for Personal Identification:



Biometrics Defined:



How the Technologies Work:



Leading Biometric Technologies:



Emerging Biometric Technologies:



Common Applications of Biometric Technologies:



Performance Issues:



Technologies Viable for U.S. Border Control:



Biometric Technology Applied to Border Control Today:



Chapter 4: Scenarios for Border Control with Biometrics:



Watch List Check before Issuing Travel Documents:



Watch List Check before Entering the United States:



U.S. Visas with Biometrics:



U.S. Passports with Biometrics:



Implementing Multiple Scenarios:



Chapter 5: Applying Biometrics to Border Control: Challenges

and Implications:



The Performance of Biometric Technologies:



How Introducing the Technology Affects People and Procedures:



Weighing Costs and Benefits:



Effects on Privacy and the Economy:



Chapter 6: Summary:



Key Considerations in Using Biometrics for Border Control:



High-Level Analysis of Four Scenarios Using Biometrics:



The Role of Biometrics in Border Security:



Agency Comments and Our Evaluation:



External Reviewers Comments:



Appendix I: Our Technology Assessment Methodology:



Appendix II: Fingerprint Recognition Technology:



How the Technology Works:



The Leading Vendors:



The Cost of Devices:



Performance Issues:



User Acceptance:



The Technologys Maturity:



Border Control Applications Piloted and Deployed:



Processing Issues:



Device Durability and Environmental Constraints:



Appendix III: Hand Geometry Technology:



How the Technology Works:



The Leading Vendors:



The Cost of Devices:



Performance Issues:



User Acceptance:



The Technologys Maturity:



Border Control Applications Piloted and Deployed:



Device Durability and Environmental Constraints:



Appendix IV: Facial Recognition Technology:



How the Technology Works:



The Leading Vendors:



The Cost of Devices:



Performance Issues:



User Acceptance:



The Technologys Maturity:



Border Control Applications Piloted and Deployed:



Processing Issues:



Device Durability and Environmental Constraints:



Appendix V: Iris Recognition Technology:



How the Technology Works:



The Leading Vendors:



The Cost of Devices:



Performance Issues:



User Acceptance:



The Technologys Maturity:



Border Control Applications Piloted and Deployed:



Processing Issues:



Device Durability and Environmental Constraints:



Appendix VI: Cost Estimates for Using Biometrics for Border

Security:



Initial Cost Elements:



Recurring Cost Elements:



Assumptions:



Estimated Costs for Conducting Watch List Checks with Biometrics:



Estimated Costs for Issuing Visas with Biometrics:



Estimated Costs for Issuing Passports with Biometrics:



Appendix VII: Comments from the U.S. Department of State:



Appendix VIII: Comments from the U.S. Department of Justice:



Appendix IX: GAO Contacts and Acknowledgments:



GAO Contacts:



Acknowledgments:



Bibliography:



Tables:



Table 1: Leading Biometric Technologies:



Table 2: Number of Inspections at U.S. Ports of Entry, Fiscal Year 

2001:



Table 3: Estimated Costs for Implementing Border Security Scenarios:



Table 4: Number of Inspections at U.S. Ports of Entry, Fiscal Year 

2001:



Table 5: Leading Biometric Technologies and Their Template Size:



Table 6: Emerging Biometric Technologies and Their Maturity:



Table 7: Independent Biometric Test Results, 1991-2002:



Table 8: Four Viable Biometric Technologies Compared:



Table 9: The Enrollment Size of Seven Operational Biometric Systems:



Table 10: Estimated Number of Biometric Matching Transactions in Four 

Border Control Scenarios:



Table 11: Security Risks and Mitigating Techniques:



Table 12: The Number and Type of Fraudulent Documents INS Inspectors 

Intercepted, Fiscal Year 2001:



Table 13: Estimated Costs for Watch List Checks:



Table 14: Estimated Costs for Issuing Visas with Biometrics:



Table 15: Estimated Consular Costs for Issuing Visas with Biometrics:



Table 16: Estimated Costs for Issuing Passports with Biometrics:



Table 17: Cost Estimate Uncertainty Analysis for Four Scenarios:



Table 18: Summary of Biometric Systems Privacy Guidelines:



Table 19: Estimated Costs for Implementing Border Security Scenarios:



Table 20: Leading Vendors of Fingerprint Recognition Biometrics:



Table 21: Summary of Results from the Fingerprint Verification 

Competition 2000:



Table 22: Summary of Results from the Fingerprint Verification 

Competition 2002:



Table 23: INSs IDENT Fingerprint Benchmark Test Results, 1998:



Table 24: Identix Airport Facial Biometric Pilot Results:



Table 25: Facial Recognition Product Usability Test:



Table 26: Estimated Costs for Watch List Checks before Issuing Travel 

Documents and before Entering the United States:



Table 27: Estimated Costs for Issuing Visas with Biometrics Using 

Fingerprint Recognition:



Table 28: Estimated Costs for Issuing Visas with Biometrics Using Iris 

Recognition:



Table 29: Estimated Costs for Issuing Visas with Biometrics Using 

Facial Recognition:



Table 30: Estimated Costs for Issuing Visas with Biometrics Using 

Fingerprint and Iris Recognition:



Table 31: Estimated Costs for Issuing Visas with Biometrics Using 

Fingerprint and Facial Recognition:



Table 32: Estimated Costs for Issuing Visas with Biometrics Using 

Fingerprint, Iris, and Facial Recognition:



Table 33: Estimated Costs for Issuing Passports with Biometrics Using 

Fingerprint Recognition:



Table 34: Estimated Costs for Issuing Passports with Biometrics Using 

Iris Recognition:



Table 35: Estimated Costs for Issuing Passports with Biometrics Using 

Facial Recognition:



Table 36: Estimated Costs for Issuing Passports with Biometrics Using 

Fingerprint and Iris Recognition:



Table 37: Estimated Costs for Issuing Passports with Biometrics Using 

Fingerprint and Facial Recognition:



Table 38: Estimated Costs for Issuing Passports with Biometrics Using 

Fingerprint, Iris, and Facial Recognition:



Figures:



Figure 1: The U.S. Passport Application Process:



Figure 2: A U.S. Passport Cover:



Figure 3: A U.S. Passports Biography Page:



Figure 4: The U.S. Visa Application Process:



Figure 5: A U.S. Visa Foil:



Figure 6: The U.S. Port of Entry Inspection Process:



Figure 7: Motor Vehicles Waiting for Inspection at the Paso del Norte 

Port of Entry, El Paso, Texas:



Figure 8: A Driver Being Questioned at a Port of Entry:



Figure 9: The Biometric Verification Process:



Figure 10: The Biometric Identification Process:



Figure 11: The General Relationship between FMR and FNMR:



Figure 12: Standards for Biometric Systems:



Figure 13: The Front of a Laser Visa:



Figure 14: The Back of a Laser Visa:



Figure 15: Issuing U.S. Visas by a Watch List Check Process:



Figure 16: Issuing U.S. Passports by a Watch List Check Process:



Figure 17: System Architecture for a Biometric Watch List Check before 

Issuing Travel Documents:



Figure 18: Entering the United States by a Watch List Check Process:



Figure 19: System Architecture for a Biometric Watch List Check before 

Entering the Country:



Figure 20: Issuing U.S. Visas with Biometrics:



Figure 21: Port of Entry Visa Inspection with Biometrics:



Figure 22: System Architecture for Issuing Visas with Biometrics:



Figure 23: Issuing U.S. Passports with Biometrics:



Figure 24: Port of Entry Passport Inspection with Biometrics:



Figure 25: System Architecture for Issuing Passports with Biometrics:



Figure 26: Using Fingerprint Biometrics for Physical Access:



Figure 27: Using Fingerprint Biometrics for Logical Access:



Figure 28: A Fingerprint Biometric Device for Personal Identification:



Figure 29: Common Fingerprint Features:



Figure 30: Established Fingerprint Types.



Figure 31: An IDENT Workstation:



Figure 32: Fingers Guided by Pegs in a Biometric Hand Geometry 

Measurement:



Figure 33: A Traveler Using an INSPASS Hand Geometry Device:



Figure 34: A Traveler Using Ben Gurion Airports Biometric Hand 

Geometry System:



Figure 35: A Typical Hand Geometry Recognition Device:



Figure 36: A Hand Geometry Recognition Device That Is Enclosed:



Figure 37: Local Feature Analysis: A Topographical Grid of Facial 

Regions:



Figure 38: Two-Dimensional, Gray-Scale Images of an Eigenface Template:



Figure 39: CCTV Surveillance Equipment:



Figure 40: Facial Recognition Distance Identification:



Figure 41: Facial Recognition Distance Verification:



Figure 42: Facial Recognition Expression Identification:



Figure 43: Facial Recognition Expression Verification:



Figure 44: Facial Recognition Media Identification: Digital to

35 mm:



Figure 45: Facial Recognition Media Verification: Digital to 35 mm:



Figure 46: Facial Recognition Pose Identification:



Figure 47: Facial Recognition Temporal Identification:



Figure 48: Facial Recognition Temporal Verification:



Figure 49: The Iris and Other Parts of the Eye:



Figure 50: Iris Recognition Physical Access Control System:



Figure 51: Iris Recognition System with Desktop Camera:



Figure 52: Mapping the Eye for Iris Recognition Systems:



Figure 53: Iris Recognition Device for Border Control at Londons 

Heathrow Airport:



Figure 54: Border Control Lane with Iris Recognition Device at Londons 

Heathrow Airport:



Abbreviations:



AAMVA: American Association for Motor Vehicle Administration:



AFIS: automated fingerprint identification system:



ANSI: American National Standards Institute

API: application programming interface

APIS: Advance Passenger Information System

ATM: automated teller machine

BAPI: biometric application programming interface

CBEFF: Common Biometric Exchange File Format

CCD: Consular Consolidated Database

CCTV: closed-circuit television

CLASS: Consular Lookout and Support System

DOD: Department of Defense

EER: equal error rate

EPPS: Expedited Passenger Processing System

ETT: enrollment timed test

FAA: Federal Aviation Administration

FBI: Federal Bureau of Investigation

FERET: Face Recognition Technology

FMR: false match rate

FNMR: false nonmatch rate

FRVT: 2000Facial Recognition Vendor Test 2000

FRVT: 2002Facial Recognition Vendor Test 2002

FTE: failure to enroll

FTER: failure to enroll rate

FVC 2000: Fingerprint Verification Competition 2000

FVC 2002: Fingerprint Verification Competition 2002

IAFIS: Integrated Automated Fingerprint Identification System:

IAFS: Immigration and Asylum Fingerprint System

IBG: International Biometric Group

IBIA: International Biometric Industry Association

IBIS: Interagency Border Inspection System

ICAO: International Civil Aviation Organization

IDENT: Automated Biometric Fingerprint Identification System

INCITS: InterNational Committee for Information Technology 

 Standards

INS: Immigration and Naturalization Service

INSPASSINS: Passenger Accelerated Service System

IRS: Internal Revenue Service

JPEG: Joint Photographic Experts Group

LFA: local feature analysis

NAFTA: North American Free Trade Agreement

NAS: National Academy of Sciences

NIST: National Institute of Standards and Technology

NPL: National Physical Laboratory

NSA: National Security Agency

OIDTT: old image database timed test

PALS: Portable Automated Lookout System

PFM: Passport Files Miniaturization

PIN: personal identification number

PRISM: Passport Records Imaging System Management:

RSI: Recognition Systems Inc. 

SENTRI: Secure Electronic Network for Travelers Rapid Inspection:

TECS: Treasury Enforcement Communications System

WSQ: wavelet scalar quantization:



United States General Accounting Office:



Washington, DC 20548:



November 15, 2002:



The Honorable Richard J. Durbin

Chairman

The Honorable Robert F. Bennett

Ranking Minority Member

Subcommittee on Legislative Branch

Committee on Appropriations

United States Senate:



As directed in the Fiscal Year 2002 Legislative Branch Appropriations 

Conference Report (House Report 107-259) and subsequent support letters 

from interested Members of the Congress, we conducted a pilot program 

in technology assessment that examined the use of biometric 

technologies for border control. This report discusses the current 

maturity of several biometric technologies and possible implementation 

of these technologies in current border control processes. Policy 

implications and key considerations for the use of biometric 

technologies are also discussed.



We are sending copies of this report to the Attorney General, the 

Secretary of State, and interested congressional committees. We will 

provide copies to others on request. In addition, the report will be 

available at no charge on the GAO Web site at http://www.gao.gov.



If you have questions concerning this report, you may contact me on 

(202) 512-2700 (kingsburyn@gao.gov) or Naba Barkakati, Senior Level 

Technologist, on (202) 512-4499 (barkakatin@gao.gov). Major 

contributors to this report are listed in appendix IX.



Nancy R. Kingsbury

Managing Director, Applied Research and Methods:



Signed by Nancy R. Kingsbury



[End of section]



Technology Assessment Overview:



Purpose:



One facet of the homeland security strategy focuses on border security-

-preventing the illegal entry of people and goods into the United 

States without impeding their legitimate flow. Security concerns need 

to be balanced with practical cost and operational considerations as 

well as political and economic interests. A risk-based approach can 

help identify and address security concerns. This is a challenging 

mission because:



* the nation shares a 5,525 mile border with Canada and a 1,989 mile 

border with Mexico and has a shoreline of about 95,000 miles,



* there are almost 400 official entry points along these borders, and:



* there were more than 500 million border crossings into the United 

States last year, two-thirds by travelers who were not citizens.



Part of the border security mission is controlling the passage of 

travelers through these official entry points into the United States. 

Biometric technologies, using one or more of a persons distinct 

physiological or behavioral characteristics, have been suggested as a 

way to help automate the identification of travelers to the United 

States at these ports of entry.



As directed in the Fiscal Year 2002 Legislative Branch Appropriations 

Conference Report (House Report 107-259) and subsequent support letters 

from interested Members of the Congress, this technology assessment 

focuses on four key questions:



1.	 What biometric technologies are currently deployed, currently 

available but not yet deployed, or in development that could be 

deployed in the foreseeable future for use in securing the nations 

borders?



2.	 How effective are these technologies now or likely to be in the 

future in helping provide security to the nations borders?



3.	 What are the economic and effectiveness trade-offs of implementing 

these technologies?



4.	 What are the implications of using biometric technologies for 

personal security and the preservation of individual liberties?



To answer these questions, we convened, with the assistance of the 

National Academy of Sciences, two meetings on biometrics and border 

control issues that included manufacturers of facial, fingerprint, and 

iris recognition and hand geometry technologies, as well as informed 

representatives from academia, government, and industry groups; privacy 

and civil liberty advocates; and other stakeholders such as 

representatives of border communities and trade organizations. We also 

interviewed certain users of biometric technologies, including the 

Federal Bureau of Investigation, Immigration and Naturalization Service 

(INS), National Security Agency, National Institute of Standards and 

Technology, the Department of State, and the Canada Customs and Revenue 

Agency. We reviewed test documentation to understand the performance of 

biometric technologies and visited a number of ports of entry where 

these technologies may be used. We interviewed manufacturers of 

biometric technologies and reviewed their publications to obtain 

descriptive information about their equipment. We interviewed officials 

from biometric industry organizations, including the Biometric 

Consortium and the Biometric Foundation. We also interviewed the 

International Biometric Group (IBG). We postulated four scenarios for 

using biometric technologies in border security and created cost models 

to estimate the rough order of magnitude costs of implementing 

biometric technologies. We provided our assessment report to the 

Department of Justice and the Department of State for their review. We 

also had the draft report reviewed by a number of external experts.



Our report starts with a description of the current border control 

procedures for admitting people into the United States--issuing visas 

to citizens of other nations and passports to U.S. citizens and 

inspecting travelers at the ports of entry. Next, the report describes 

how biometric technologies work, including the different types of 

biometric technologies, their levels of maturity, and their operating 

and performance characteristics. We present four possible scenarios in 

which biometrics might be applied to current U.S. border control 

procedures. For each scenario, we analyze some of the costs, benefits, 

and risks associated with implementation. Finally, the report sums up 

certain policy implications and challenges to be faced if a biometric 

system is to be designed and deployed for border security. A number of 

appendixes provide details on the major biometric technologies.



Background:



The United States essentially relies on a two-step approach to prevent 

inadmissible people from entering the country. The Bureau of Consular 

Affairs in the State Department is responsible for issuing 

international travel documents, such as passports in the United States 

and visas in other countries, and INS in the Department of Justice is 

responsible for inspecting travelers at the ports of entry.



The term biometrics covers a wide range of technologies that can be 

used to verify a persons identity by measuring and analyzing his or 

her characteristics. Identifying a persons physiological 

characteristics is based on data derived from measuring a part of the 

body directly. Technologies have been developed to measure peoples 

fingers, hands, faces, and eye retinas and irises. Identifying a 

persons behavioral characteristics is based on data derived from an 

individuals actions, such as how he or she talks, types, or signs his 

or her name. Biometric systems are essentially pattern recognition 

systems. They use electronic or optical sensors such as cameras and 

scanning devices to capture images, recordings, or measurements of a 

persons characteristics and computer hardware and software to extract, 

encode, store, and compare these characteristics.



Using biometrics as identifiers for border security purposes appears to 

be appealing because they can help tightly bind a traveler to his or 

her identity by using physiological or behavioral characteristics. 

Unlike other identification methods, such as identification cards or 

passwords, biometrics are less easily lost, stolen, or guessed.



Biometrics have been implemented to a limited degree in U.S. border 

control systems. For example, since 1993, the INS Passenger Accelerated 

Service System (INSPASS) has allowed for automated inspections of more 

than 35,000 frequent fliers at nine airports. The Congress has enacted 

laws in the past 6 years that require a more extensive use of 

biometrics in border control systems. These laws require that by the 

end of 2004, all ports of entry are to be able to perform biometric 

comparison and authentication of all U.S. visas and other travel and 

entry documents and that all systems of the State Department, INS, and 

federal law enforcement and intelligence agencies that contain 

information about aliens are to be interoperable.



Results in Brief:



Biometric technologies are available today and are being used for a 

variety of applications such as access control and criminal 

identification and surveillance. We considered a number of leading and 

emerging biometric technologies that could potentially be used for 

securing the nations borders. The seven leading biometric technologies 

include facial recognition, fingerprint recognition, hand geometry, 

iris recognition, retina recognition, signature recognition, and 

speaker recognition (see table 1). Of these, fingerprint recognition, 

facial recognition, iris recognition, and hand geometry appeared to be 

suitable for border security because all have been used in border 

control pilots and applications. However, hand geometry is not highly 

distinctive and cannot reliably pick out an individual from among many. 

Consequently, hand geometry is not suitable if there is a need to 

search the biometrics database to determine if a person has previously 

enrolled in the database or is in a watch list. However, hand geometry 

is viable for verifying claimed identity when another biometric 

technology is used for the identification check during enrollment. We 

also looked at emerging biometric technologies, such as ear shape 

recognition and odor sensing, and found that they are in various stages 

of development and have not yet been used in border control 

applications. Our assessment is based on a snapshot of biometric 

technologies as they existed in early 2002.



Table 1: Leading Biometric Technologies:



Technology: Facial recognition; How it works: Captures and compares 

facial patterns; Suitable for border control: Yes.



Technology: Fingerprint recognition; How it works: Captures and 

compares fingertip patterns; Suitable for border control: Yes.



Technology: Hand geometry; How it works: Measures and compares 

dimensions of hand and fingers; Suitable for border control: Yes 

(verification only).



Technology: Iris recognition; How it works: Captures and compares iris 

patterns; Suitable for border control: Yes.



Technology: Retina recognition; How it works: Captures and compares 

retina patterns; Suitable for border control: No.



Technology: Signature recognition; How it works: Captures and compares 

rhythm, acceleration, and pressure flow of signature; Suitable for

border control: No.



Technology: Speaker recognition; How it works: Captures and compares 

cadence, pitch, and tone of vocal tract; Suitable for

border control: No.



Source: GAO analysis.



[End of table]



To evaluate the effectiveness of biometrics in border control, it is 

important to recognize that the use of biometric technology would be 

but one component of the decision to support systems that determine who 

is allowed to enter the United States and who is not. Biometric 

technology can play a role in associating a person with travel 

documents such as visas and passports. When used at a border 

inspection, the biometric comparison can be used to help decide whether 

to admit a traveler into the United States.



When biometric technology is used in border control, the border control 

processes will have to be changed not only to use the new technology 

but also to compensate for its shortcomings. None of these technologies 

have been used in an application as large as that required for a border 

control system. Further, biometric technologies are not perfect--all 

have some measured rates of erroneously matching a person or 

erroneously not matching a person. The people involved, such as 

travelers, inspectors, and consular personnel, will have to be trained 

in how to use the new system and in the new border control processes.



Before any decision is made to implement biometrics in a border control 

system, the benefits of the system must be weighed against its costs. 

The purpose of any biometrics initiative is to prevent the entry of 

travelers who are inadmissible to the United States. For example, using 

a biometric watch list can provide an additional check to name-based 

checks and can help detect travelers trying to evade detection who have 

successfully established a separate name and identity. The use of 

passports and visas with biometrics can help positively identify 

travelers as they enter the United States and can limit the use of 

fraudulent documents, including counterfeit and modified documents, and 

impostors use of legitimate documents.



To analyze the costs of using three biometric technologies--facial, 

fingerprint, and iris recognition--we define four scenarios in which 

these technologies can be used to support border control operations. 

Two scenarios use a biometric watch list to identify travelers who are 

inadmissible to the United States (1) before issuing travel documents 

or (2) before travelers enter the country. To help bind the claimed 

identity of travelers to their travel documents, biometrics could be 

incorporated into (1) U.S. visas or (2) U.S. passports. As defined, 

these four scenarios are not mutually exclusive and could be 

implemented independently or in combination. The costs of a biometric 

border control system will not be trivial. For example, our rough order 

of magnitude cost estimates to implement visas with biometrics are 

between $1.3 billion and $2.9 billion initially and between $0.7 and 

$1.5 billion annually thereafter.



Finally, important policy implications must be addressed in trade-offs 

between increasing security and the impact on areas such as privacy, 

economy, traveler convenience, and international relations. Civil 

liberties groups and privacy experts have expressed concern about the 

adequacy of protections under current law for biometric data and an 

absence of clear criteria governing data sharing. Requiring biometric-

enabled visas could potentially affect the travel and tourism industry 

adversely. Increased inspection times because of biometric 

identification checks could result in longer waiting times, especially 

at land crossings, causing local merchants on both sides of the border 

to lose sales. International relations could be affected as other 

countries reciprocate when the United States asks visitors from those 

countries to provide biometric identifiers when they apply for visas.



Whether the financial and nonfinancial costs are warranted by the 

benefits of greater security is a policy issue that should be 

determined before biometric technologies are implemented in a border 

control system. This report provides useful information that can help 

serve as the basis for these decisions. As our report describes, 

biometric technology is not a panacea for the border security problem. 

It is only one component of the decision support systems that determine 

who is allowed to enter the United States and who is not. A risk-based 

approach would be helpful in addressing the overall border security 

problem and the high-level goals that can be achieved with biometric 

technologies. The approach could rely on establishing what is being 

protected, who the adversaries are, what the vulnerabilities are, what 

the priorities are, and what mitigation strategies can be implemented. 

Answering these questions should help determine the proper role of 

biometric technologies in border security.



We provided a draft of this report to the Department of Justice and the 

Department of State for their review. The Department of Justice 

expressed some concerns, but the State Department stated that it 

appreciated the thorough and balanced approach we took in our 

assessment of the use of biometrics for border security. We include 

States and Justices comments in appendixes VII and VIII, 

respectively, and summarize them in chapter 6. State and Justice also 

provided technical comments on the draft, which we incorporated as 

appropriate.



We also provided a draft of this report to 16 different organizations, 

representing government, industry, and academia, for their review. We 

received comments and suggestions from 10 reviewers. The comments 

included the correction of technical inaccuracies and the highlighting 

of certain aspects of the assessment that reviewers considered 

important. We have incorporated these comments, where appropriate, in 

the report. We summarize these comments in chapter 6.



Border Control Overview:



The United States relies essentially on two primary procedures to 

facilitate the entry of people authorized to enter the country and to 

ensure that inadmissible people are prevented from entering. The State 

Departments Bureau of Consular Affairs issues international travel 

documents, including passports to U.S citizens and visas to people who 

are not U.S. citizens and are traveling to the United States. INS 

inspects travelers entering the United States through official ports of 

entry. In addition, INSs Border Patrol is responsible for securing the 

borders and apprehending travelers entering through other than official 

ports of entry.



Passport Processing:



Passports are issued to U.S. citizens to permit their travel abroad and 

to facilitate their entry back into the United States. U.S. citizens 

can apply for passports at one of more than 4,500 passport acceptance 

offices. Few of these offices are State Department offices--most are 

offices in facilities such as U.S. post offices or state, county, 

township, and municipal government offices. Passport acceptance agents 

review application packages for completeness and complete a checklist 

regarding their impressions of applicants and their applications. After 

the applications are sent to the central application processing center, 

they are run through a State Department computer system that checks to 

see (1) whether the applicant has been identified as someone who is not 

eligible to receive a passport, (2) whether the individual already has 

an active passport, and (3) whether the individual has multiple 

applications in process. Passport examiners review the results of these 

checks and the applications and decide whether to issue passports. If 

an application is approved, a passport is generated and sent to the 

applicant.



Visa Processing:



With some exceptions, visitors to the United States are required to 

have a visa to enter. Worldwide, travelers can apply for a visa at 210 

embassies and consulates. Visa applications are entered into a State 

Department computer system and are checked to determine items such as 

whether an applicant has been identified as someone who is not eligible 

to receive a visa, whether the applicants passport matches a passport 

that has been reported as lost or stolen, or whether the applicant has 

been refused a visa in the past. In some cases, an interview with the 

visa applicant or a security advisory opinion from State headquarters 

is required. In determining whether to grant the visa, the consular 

officer reviews the data provided in the application and the computer 

system and, if applicable, the interview and security advisory opinion. 

If the application is approved, a visa foil is generated and provided 

to the traveler.



Port of Entry Inspections:



All people legally entering the United States must be processed through 

an air, land, or sea port of entry. As shown in table 2, about 82 

percent of border crossings occurred at land ports of entry last year. 

An individual entering the country through an official port of entry 

first enters a process called primary inspection. Inspectors determine 

whether travelers qualify for admission or additional review is 

necessary. If additional review is necessary, the individual is 

referred to secondary inspection, where a final decision on whether to 

admit the traveler is made. During fiscal year 2001, about 1.7 percent 

of the more than 500 million border crossers entering the country were 

referred to secondary inspection, where 707,920 were denied admission.



Table 2: Number of Inspections at U.S. Ports of Entry, Fiscal Year 

2001:



Type of port: Sea; Number of ports: 86; Number of inspections: 

11,952,501.



Type of port: Air; Number of ports: 155; Number of inspections: 

79,598,681.



Type of port: Land; Number of ports: 154; Number of inspections: 

414,364,965.



Type of port: Total; Number of ports: 395; Number of inspections: 

505,916,147.



Source: GAO analysis of INS data.



[End of table]



The processes used for primary inspection vary, depending on the mode 

of travel--air, land, or sea--and the travelers nationality. INS uses 

a combination of methods to inspect travelers, including a brief 

interview with the travelers, an inspection of their travel or 

identification documents, and computer checks of their names or the 

license plates of their vehicles. The travelers nationality also 

dictates the documentation requirements. For example, U.S. citizens do 

not require passports unless they are returning from outside the 

Western Hemisphere. In general, aliens must present their passport and 

a U.S.-issued visa. Citizens of countries participating in the visa 

waiver program do not require a visa to enter the United States.



Biometric Technologies:



Biometric technologies have been used in a wide array of applications, 

including access control to buildings and computers, criminal 

identification and surveillance, licensing and voter applications, and 

fraud reduction. Biometric technologies can be used in a verification 

or identification mode. Regardless of the method used, an enrollment 

process is required to capture a biometric sample, extract and encode 

the sample as a biometric template, and store the data in a database 

for future comparisons. In verification mode (e.g., access control to a 

building with an identity card), the biometric system verifies the 

validity of a claimed identity, answering the question Is this person 

who she claims to be? In identification mode (e.g., criminal 

surveillance), the biometric system compares the individuals biometric 

with all stored biometric records to answer the question Who is this 

person?:



We considered seven leading biometric technologies: facial recognition, 

fingerprint recognition, hand geometry, iris recognition, retina 

recognition, signature recognition, and speaker recognition. Four--

facial recognition, fingerprint recognition, hand geometry, and iris 

recognition--appear to be suitable for border control applications. All 

four have been used in border control pilots and applications. The 

three other technologies have key problems that inhibit their use for 

border control. Retina recognition is considered to be too intrusive 

because many users experience discomfort in using the devices, which 

operate close to their eyes. Signature recognition has a high error 

rate because it has been found that people do not always sign their 

name the same way each time. Speaker recognition has been piloted in a 

border control environment but has been found to be unreliable. Also, 

speaker recognition systems do not perform well in noisy environments 

such as would be encountered at ports of entry.



The emerging technologies we considered--facial thermography, gait 

recognition, ear shape recognition, DNA matching, odor sensing, blood 

pulse measurement, skin pattern recognition, vein scan, and nailbed 

identification--are in various stages of development and have not yet 

been used in border control applications.



Fingerprint recognition has been widely used and accepted, primarily in 

law enforcement, for four decades. Facial recognition can be used to 

compare either a live facial scan to a stored biometric template or a 

static image to a digitized photograph. Facial images are already 

prevalent in travel documents, and people are accustomed to having 

their picture taken. Hand geometry has been widely used in access 

control applications and is relatively easy to use. Iris recognition 

identifies people by numerous characteristics of the colored ring 

surrounding the pupil of the eye, some of which tend to remain stable 

throughout life.



In order to differentiate between biometric technology products, they 

are often characterized by factors such as accuracy, testing, 

standards, and user acceptance. The accuracy of a biometric technology 

is usually measured by three key error statistics: the rate at which a 

system erroneously matches a person, the rate at which a system 

erroneously does not match a person, and the rate at which people are 

unable to enroll in a system. To evaluate biometric technologies, the 

results of independent tests should be consulted. In addition, tests 

have been conducted in which researchers have successfully fooled 

biometric systems with artificial characteristics such as a latex 

finger or a facial picture. Adherence to standards enhances the ability 

of a biometric device to store and exchange data. Another factor to 

consider in selecting a biometric technology is the ease of use. Some 

people find biometric technologies difficult, if not impossible, to 

use. Still others resist biometrics in general as intrusive, inherently 

offensive, or just uncomfortable to use.



No biometric technology is best for every situation, but it is possible 

to determine the most accurate, easiest to use or deploy, or cheapest, 

depending on the objectives to be achieved. For example, hand geometry 

requires the least data storage, fingerprint and iris recognition have 

the lowest error rates, and facial recognition is the easiest to use. 

However, each technology also has its limitations. For example, about 2 

to 5 percent of people cannot be easily fingerprinted because their 

fingerprints have become dry or worn from age, extensive manual labor, 

or exposure to corrosive chemicals. Facial recognition systems have not 

performed particularly well in independent testing. Iris recognition is 

a relatively new technology and has not been used in any large 

operational applications as fingerprint and facial recognition systems 

have. Hand geometry is not highly distinctive and thus is not suitable 

for identification applications. These limitations and others would 

have to be considered if these technologies were to be deployed within 

a border control system. (More details on the biometric technologies 

can be found in chapter 3 and appendixes II to V.):



Scenarios for Using Biometric Technologies for Border Security:



We developed and analyzed four different scenarios in which 

fingerprint, facial, or iris recognition biometric technologies or some 

combination of them could be used to improve current border control 

procedures. Two scenarios use a biometric watch list to identify 

travelers who are inadmissible to the United States (1) before issuing 

travel documents or (2) before travelers enter the country. To help 

bind travelers to their travel document, two other scenarios could be 

used to incorporate biometrics into (1) U.S. visas or (2) U.S. 

passports. These four scenarios can be implemented independently or in 

combination.



The first scenario involves the use of facial recognition to help 

identify people ineligible to receive a passport or a visa. The 

biometric identification check would be conducted at the same time as 

other computer checks are conducted on each travel document 

application. The second scenario uses an automated facial recognition 

system at the ports of entry that can observe a persons face and check 

the observed facial features against a watch list of people who should 

be denied access to the country. Both scenarios require the creation of 

a biometric-based watch list that stores photographs of individuals 

selected according to criteria determined by border security and other 

law enforcement agencies. While both scenarios require a centralized 

facial recognition server to perform matches, performing checks at the 

ports of entry would also require the purchase of facial recognition 

systems for the almost 4,000 inspection stations at the ports of entry.



The two other scenarios introduce biometrics to visas and passports. In 

both of these scenarios, travel document applicants would be required 

to have their biometric sample collected--at 1 of 210 embassies and 

consulates for visa applicants or at 1 of 4,500 passport acceptance 

offices for passport applicants. As part of the enrollment and document 

issuance process, an additional identification check of applicants 

would be made against the database of issued documents to ensure that a 

person does not receive multiple documents under different identities. 

Biometric scanners would also have to be installed at the ports of 

entry to verify the identity of travelers with biometrically enabled 

travel documents.



The Effect on Border Control Processes:



The successful implementation of any technology depends not only on the 

performance of the technology but also on the operational processes 

that employ the technology and the people who execute them. The 

implementation of biometrics in border security is no exception. 

Further, the use of technology alone is not a panacea for the border 

security problem. Instead, biometric technology is just a piece of the 

overall decision support system that helps determine whether to allow a 

person into the United States. The first decision is whether to issue 

travelers a U.S. travel document. The second decision, made at the 

ports of entry, is whether to admit travelers into the country. 

Biometrics can play a role in both decisions. Sorting the admissible 

travelers from inadmissible ones is now done by using information 

systems for checking names against watch lists and by using manual 

human recognition capabilities to see if the photograph on a travel 

document matches the person who seeks entry to the United States. When 

enabled with biometrics, automated systems can verify the identity of 

the traveler and assist inspectors in their decision making.



The four biometric scenarios will affect key border security processes. 

A key factor is the performance of the biometric technology. For 

example, if the biometric technology that is used to perform watch list 

checks before travel documents are issued has a high rate of false 

matches, workload could increase at the embassies and consulates for 

visas and at the passport centers for passports. If the same biometric 

solution were used at the ports of entry, it could lead to increased 

delays in the inspection process and an increase in the number of 

secondary inspections.



Exception processing will have to be carefully considered. Exceptions 

include people who fail to enroll in a system or are not correctly 

matched by a verification system. Exception processing that is not as 

good as biometric-based primary processing could be exploited as a 

security hole. Failure of equipment must also be considered and planned 

for. Further, for issuing visas or passports with biometrics, an 

appropriate transition strategy must be devised to simultaneously 

handle biometric travel documents and the current travel documents that 

could remain valid without biometrics for the next 10 years.



Maintaining Information Security:



Implementing biometrically enabled travel documents requires a strong 

binding and verification process to tie individuals to their identities 

using their biometrics. A process that does not have strong binding 

mechanisms can provide little improvement over existing procedures. A 

failure in the enrollment or the verification process could undermine 

the use of biometric technologies. For example, procedures must be 

developed to handle individuals who could not be enrolled in the 

system. Even if individuals are properly enrolled, they might not be 

properly matched during inspection. Adequate procedures have to be in 

place to properly differentiate between system problems and persons who 

are impostors or otherwise inadmissible to the United States. 

Information security also is important in ensuring strong binding. If 

rogue individuals can modify the biometric database or the token on 

which individual biometric records are stored, a persons bond to his 

or her biometric data can be compromised.



Weighing Costs and Benefits:



Before any significant project investment is made, the benefit and cost 

information of the project should be analyzed and assessed in detail. 

The project concept should be based on high-level system goals, which 

for a border control system would include items such as binding a 

biometric feature to a persons identity on a travel document, 

identifying undesirable persons on a watch list, checking for duplicate 

enrollments in the system, verifying identities at the borders, 

ensuring the security of the biometric data, and ensuring the adequacy 

of privacy protections.



The desired benefit of all the scenarios we describe--the use of 

biometric watch lists or biometrically enabled travel documents--is the 

prevention of the entry of travelers who are inadmissible to the United 

States. More specifically, the use of a biometric watch list can 

provide an additional check to name-based checks and can help detect 

travelers who are trying to evade detection and have successfully 

established separate names and identities. The use of passports and 

visas with biometrics can help positively identify travelers as they 

enter the United States and can limit the use of fraudulent documents, 

including counterfeit and modified documents and impostors use of 

legitimate documents.



These benefits have several limitations. First, the benefit achieved in 

each scenario is directly related to the performance of the biometric 

technology. The performance of facial, fingerprint, and iris 

recognition is unknown for systems as large as a biometric visa system 

that would require the storage and comparison against 100 million to 

240 million records. The largest facial, fingerprint, and iris 

recognition systems contain 60 million, 40 million, and 30,000 records, 

respectively.



For the watch list scenarios, the population of the watch list is 

critical to the systems effectiveness. Issuing passports and visas 

with biometrics will only assist in identifying those currently 

required to obtain passports or visas to enter this country. For 

example, U.S. citizens do not have to have a passport to return from 

Canada or Mexico. Canadians, Mexicans with border crossing cards, and 

aliens participating in the visa waiver program do not have to have a 

visa to enter the United States. The issuance of passports and visas 

with biometrics is also dependent on establishing the correct identity 

during enrollment. This process will typically be dependent on the 

presentation of identification documents. If the documents do not 

specify the applicants true identity, then the travel document will 

still be linked to a false identity.



Further, biometric technology is not a solution to all border security 

problems. Biometric technology can address only problems associated 

with identifying travelers at official locations such as embassies, 

passport acceptance offices, and ports of entry. While the technology 

can help reduce the number of illegal immigrants who cross with 

fraudulent documents, it cannot help with illegal immigrants who cross 

between the borders and not at a port of entry. INS has previously 

estimated that up to 60 percent of the 275,000 new illegal immigrants a 

year do not present themselves at a port of entry to enter the United 

States. In addition, biometrics cannot help with aliens who enter 

through ports of entry and are properly admitted by an inspector but 

may overstay their visit.



The security benefits gained from the use of biometrics must be weighed 

against the cost of implementing the scenario. For each of the four 

scenarios, we created cost models to estimate the cost of developing, 

implementing, and maintaining various biometric processes. We included 

the costs of both the technology and the effects on people and 

processes. Table 3 summarizes the initial and annual recurring costs of 

implementing each scenario. The initial costs include elements such as 

development, installation, training, biometric hardware and software, 

and consular facility renovation. The recurring costs include elements 

such as biometric hardware and software maintenance, system support and 

operational personnel, consular personnel, facility maintenance, and 

annual supplies. While the costs of people and space required to enroll 

travelers in biometric systems at embassies and consulates are 

included, the costs of people and space required to verify the 

biometrics at ports of entry are not included. Consular staff and space 

are major cost drivers. For example, for issuing visas with biometrics, 

these costs make up between 21 percent and 31 percent of the systems 

total initial cost and between 23 percent and 29 percent of its total 

recurring cost.



Table 3: Estimated Costs for Implementing Border Security Scenarios:



Scenario: Watch list check before issuing travel documents; Initial

cost: $53; Annual recurring cost: $73.



Scenario: Watch list check before entering the United States; Initial

cost: 330; Annual recurring cost: 237.



Scenario: Issuing visas with biometrics; Initial

cost: 1,399-2,845; Annual recurring cost: 698-1,482.



Scenario: Issuing passports with biometrics; Initial

cost: 4,446-8,766; Annual recurring cost: 1,555-2,363.



Note: Dollars are in millions.



Source: GAO analysis.



[End of table]



The watch list scenarios assume the use of facial recognition 

technology, because faces from photographs are often the only biometric 

available for individuals who may be inadmissible to the United States. 

Travel documents with biometrics can use facial, fingerprint, or iris 

recognition or some combination of the three.



Protecting Privacy and Civil Liberties:



The Privacy Act of 1974 limits federal agencies collection, use, and 

disclosure of personal information, including personal information such 

as finger or voice print and photographs. Accordingly, the Privacy Act 

generally covers federal agency use of personal biometric information. 

However, as a practical matter, the act is likely to have a more 

limited application for border security. First, the act applies only to 

U.S. citizens and lawfully admitted permanent resident aliens. Second, 

the act includes exemptions for law enforcement and national security 

purposes. Representatives of civil liberties groups and privacy experts 

have expressed concerns regarding (1) the adequacy of protections for 

security, data sharing, identity theft, and other identified uses and 

(2) secondary uses and function creep. The Internal Revenue Service, 

the RAND Corporation, and IBG have developed privacy frameworks that 

establish guidelines on issues with the scope and capabilities of 

biometric systems, the protection of data, the protection of users, and 

the disclosure, auditing, accountability, and oversight of biometric 

systems.



The Effect on Convenience, the Economy, and International Relations:



Any lengthening in the process of obtaining travel documents or 

entering the United States could affect travelers significantly. At 

some consular posts, visas are issued the day applications are 

received. Even without biometrics, the busiest ports of entry regularly 

have delays of 2 to 3 hours. Increases in inspection times could 

compound these delays. Delays inconvenience travelers and could result 

in fewer visits to the United States or lost business to the nation. 

Further studies will be necessary to measure what the potential effect 

could be on the American economy and, in particular, on the border 

communities. These communities depend on trade with Canada and Mexico, 

which totaled $653 billion in 2000.



Finally, the use of biometrics in the United States could affect the 

number of international visitors and how other countries treat visitors 

from the United States. Visitors from some countries may not want to 

come to the United States if it is less convenient to do so. In 

addition, because much of visa issuance policy is based on reciprocity-

-the process for allowing a nations citizens to enter the United 

States is similar to the process followed by that nation for visitors 

from the United States--other nations may start requiring biometric 

samples from U.S. citizens if the United States requires biometric 

samples from their citizens. (More details on costs and benefits, as 

well as the potential implications, of using biometrics are provided in 

chapter 5.):



The Role of Biometrics in Border Security:



People are identified by three basic means: by something they know, 

something they have, or something they are. Current U.S. border 

security processes identify travelers by using travel documents such as 

passports and visas and asking travelers questions--things the 

travelers have and know. The travel document also establishes a 

travelers eligibility to enter the country.



The use of biometrics--things the travelers are--can more securely bind 

a persons identity to a travel document. Two processes are keys to 

achieving this binding. First, a strict and thorough enrollment step is 

necessary to bind a person to an identity. The identity claimed by the 

traveler is based on documents such as a birth certificate, passport, 

or other government-issued documents. If processes are not in place to 

ensure the validity of the travelers claimed identity, the person 

could be linked with a false identity. Second, an effective matching 

process is required to link the person to the travel document. If a 

person can bypass the biometric check or can deceive the biometric 

system, the person may be erroneously granted admission to the United 

States. The performance of the biometric technology is also important 

to the execution of these processes. Effective enrollment and matching 

processes could allow for the use of biometric-enabled travel documents 

that will establish not only the travelers eligibility to enter the 

country but also that the traveler is indeed the individual depicted on 

the document.



However, biometric technology is just one component of the decision 

support systems that help determine who is allowed to enter the United 

States and who is not. For example, the technologies may be able to 

reduce document fraud but may not be able to detect illegal entry to 

the United States through other than official ports of entry. A risk-

based approach would be helpful in addressing the overall border 

security problem and the high-level goals that can be achieved with 

biometric technologies. The approach could rely on answering five basic 

questions: What are we protecting? Who are the adversaries? What are 

the vulnerabilities? What are the priorities? What mitigation 

strategies can be used? A decision to implement our four scenarios or 

any others should be based on an approach that answers these questions. 

The scenarios could be partially implemented or combined in different 

ways. New scenarios could be defined in which travelers voluntarily 

enroll in a biometric identification system similar to INSPASS for 

expedited border crossing. Trade-offs should be made to determine the 

best implementation of biometrics for border security. For example, a 

partial implementation may be less costly without sacrificing any of 

the security benefits.



Regardless of how biometric technology is used in border security, 

using a risk-based approach should help in developing the high-level 

goals of a system and its concept of operation. The answers should also 

help point out the limitations of such a system and what it will not be 

able to provide. They can also play a role in the analysis and 

weighting of the benefits in a cost-benefit analysis, as well as the 

trade-off analysis between greater security and issues such as privacy 

and the economy. With these answers, the proper role of biometric 

technology in border security can be determined.



[End of section]



Chapter 1 Introduction:



A primary element of the homeland security strategy is the improvement 

of U.S. border security--preventing the illegal entry of people and 

goods into the United States while facilitating their legitimate flow. 

Security concerns need to be balanced with practical cost and 

operational considerations as well as political and economic interests. 

The United States shares a 5,525 mile border with Canada and a 1,989 

mile border with Mexico. Its maritime border includes 95,000 miles of 

shoreline. There were more than half a billion border crossings into 

the United States last year; about two-thirds were not by U.S. 

citizens. The number of distinct travelers into the country each year 

is unknown because some people enter the country many times in one 

year, some daily.



Facilitating the flow of people while preventing illegal border 

crossings is a matter of identifying travelers. People are identified 

by three basic means: by something they know, something they have, or 

something they are. People and systems regularly use these means to 

identify people in everyday life. For example, members of a community 

routinely recognize one another by how they look or how their voices 

sound--by something they are. Automated teller machines (ATM) recognize 

customers from their presentation of a bank card--something they have-

-and their entering a personal identification number (PIN)--something 

they know. Using keys to enter a locked building is another example of 

using something you have. More secure systems may combine two or more 

of these approaches.



Generally, identifying travelers at the borders is performed by 

inspecting their travel documents, such as passports and visas, and 

asking them questions--things the travelers have and know. The U.S. 

Department of State issues passports to U.S. citizens and visas to 

others who are not U.S. citizens. The Immigration and Naturalization 

Service (INS) inspects these travel documents at officially designated 

air, land, and sea ports of entry.



Technologies called biometrics can automate the identification of 

individual travelers by one or more of their distinct physical or 

behavioral characteristics. Biometrics have been suggested as a way of 

improving the nations ability to positively determine whether people 

are admissible to the United States. The term biometrics covers a wide 

range of technologies that can be used to verify identity by measuring 

and analyzing human characteristics--relying on attributes of the 

individual instead of things the individual may have or know.[Footnote 

1]



Identifiable physiological characteristics include fingerprints, 

irises and retinas, hand geometry, and facial geometry. How a person 

signs his or her name is an example of an identifiable behavioral 

characteristic while speech combines both physiological and behavioral 

characteristics. To be effective identifiers, biometrics should be 

universally present, unique to the individual, and stable over time. 

Biometrics theoretically represent a more effective approach to 

security because each persons biometric characteristics are distinct 

and, when compared with identification cards and passwords, are less 

easily lost, stolen, or guessed.



The Federally Mandated Biometric Chimera System:



Biometrics have already been implemented to a limited degree in U.S. 

border control systems. For example, the INS Passenger Accelerated 

Service System (INSPASS) has identified travelers and expedited their 

inspections at nine North American airports for almost 10 years. The 

Congress has mandated a more extensive use of biometrics in automated 

border control systems. A series of laws enacted between 1996 and 

spring 2002 requires the federal government to develop Chimera, an 

automated information system, to gather and share information among 

agencies about aliens seeking to enter or stay in the United 

States.[Footnote 2] The major requirements for the Chimera system are 

(1) biometric identifiers; (2) machine-readable visas, passports, and 

other travel and entry documents; and (3) interoperability among all 

State Department, INS, and federal law enforcement and intelligence 

agency systems that contain information about aliens. Chimera will be 

used to screen applicants for visas and admission to the United States, 

identify inadmissible and deportable aliens, track lost and stolen 

passports, monitor foreign students studying in the United States, and 

help administer law enforcement and national security.[Footnote 3]



The State Department, the Justice Department, and the National 

Institute of Standards and Technology (NIST) were to report jointly to 

the Congress by November 10, 2002, to assess the action needed to 

implement machine-readable, tamper-resistant travel and entry 

documents and the biometric comparison and authentication of such 

documents. By October 26, 2004, State and Justice are to issue to 

aliens only machine-readable, tamper-resistant visas and other travel 

and entry documents that use biometric identifiers. At the same time, 

Justice is to install at all ports of entry equipment and software that 

allow the biometric comparison and authentication of all U.S. visas and 

other travel and entry documents issued to aliens and machine-readable 

passports.



To provide the technological basis for Chimera by January 26, 2003, as 

well as its supporting systems and databases, NIST is to develop a 

technology standard, including biometric identifier standards for 

verifying individual identities.



To address concerns about how information in the system will be used, 

particularly with regard to privacy protection and security, the law 

mandates that several steps be taken by October 26, 2002. First, the 

plan for sharing law enforcement and intelligence information with the 

State Department and INS must establish conditions for States and 

INSs use of the information that include their:



* limiting its redissemination;



* ensuring that it is used solely for authorized purposes, with 

criminal penalties for its misuse;



* ensuring its accuracy, security, and confidentiality;



* protecting privacy rights;



* providing data integrity by removing obsolete and incorrect 

information; and:



* protecting intelligence sources and methods.[Footnote 4]



Second, the Department of State and the Department of Justice are to 

report jointly on the development, implementation, efficacy, and 

privacy implications of a cross-agency, cross-platform electronic 

system for sharing law enforcement and intelligence information 

regarding aliens seeking to enter the United States.[Footnote 5]



Third, the president is to establish a commission on interoperable data 

sharing to oversee Chimera.[Footnote 6] The commissions duties include 

monitoring the protections outlined above and considering 

recommendations regarding security innovations, the adequacy of privacy 

protections, the adequacy of mechanisms for correcting errors, and 

other protections against the unauthorized use of data in the system.



An Overview of This Report:



This technology assessment focuses on four key questions:



5.	 What biometric technologies are currently deployed, currently 

available but not yet deployed, or in development that could be 

deployed in the foreseeable future, for use in securing the nations 

borders?



6.	 How effective are these technologies now or likely to be in the 

future in helping provide security to our borders?



7.	 What are the economic and effectiveness trade-offs of implementing 

these technologies?



8.	 What are the implications of using biometric technologies for 

personal security and the preservation of individual liberties?



To answer these questions, we first describe current border control 

procedures for admitting people to the United States--issuing visas to 

citizens of other nations and passports to U.S. citizens and inspecting 

travelers at the ports of entry. Second, we describe how biometric 

technologies work, including the different types of biometric 

technologies, their levels of maturity, and their operating and 

performance characteristics. We also describe current applications of 

various biometric technologies.



We present four possible scenarios in which biometrics might be applied 

to current U.S. border control procedures. For each scenario, we 

analyze some of the costs, benefits, and risks associated with 

implementation. Finally, we sum up the implications and challenges to 

be faced if a biometric system is to be designed and deployed for 

border security.



[End of section]



Chapter 2 Todays U.S. Border Control Procedures:



Last year, there were more than half a billion border crossings into 

the United States at almost 400 designated ports of entry. Many of 

these border crossings were by travelers who crossed the border many 

times in 1 year, some daily. Table 4 shows that the vast majority of 

inspections--those at border crossings--are at land ports. At land 

ports of entry in fiscal year 2001, more than 414 million border 

crossers entered the United States as one of more than 56 million 

pedestrians or in one of more than 140 million vehicles.



Table 4: Number of Inspections at U.S. Ports of Entry, Fiscal Year 

2001:



Type of port: Sea; Number of ports: 86; Number of inspections: 

11,952,501.



Type of port: Air; Number of ports: 155; Number of inspections: 

79,598,681.



Type of port: Land; Number of ports: 154; Number of inspections: 

414,364,965.



Type of port: Total; Number of ports: 395; Number of inspections: 

505,916,147.



Source: GAO analysis of INS data.



[End of table]



The laws and regulations governing entry into the United States and the 

conditions of stay vary by citizenship and method of travel.[Footnote 

7] In general, entry must be accompanied by the appropriate travel 

documents. U.S. citizens generally must have a U.S. passport to leave 

or enter the United States. Immigrants generally must have either a 

U.S. permanent resident card or an immigrant visa and a passport from 

their own country. Nonimmigrants generally must have a passport from 

their country and a nonimmigrant visa. The numerous exceptions to these 

rules include the following:



* Passports are not required of U.S. citizens returning from Canada or 

Mexico.[Footnote 8]



* Passports are not required of Canadian citizens unless they are 

returning from outside the Western Hemisphere. Visas are generally not 

required for Canadian citizens.



* Passports and visas are not required of Mexican citizens who possess 

a border crossing card issued by the U.S. government allowing them to 

enter for business or pleasure.



* Visas are not required of citizens of countries participating in the 

visa waiver program who enter for business or pleasure.[Footnote 9]



The United States relies on two primary procedures to facilitate the 

entry of people authorized to enter the country and to ensure that 

inadmissible people are prevented from entering. The State Departments 

Bureau of Consular Affairs issues international travel documents, 

including passports to U.S citizens and visas to people who are not 

U.S. citizens. INS inspects travelers entering the United States 

through official ports of entry. In addition, INSs Border Patrol is 

responsible for securing the borders and apprehending travelers 

entering through other than official ports of entry.



How U.S. Passports Are Issued:



U.S. citizens can apply for a passport at more than 4,500 passport 

acceptance offices (see figure 1). Few of these are State Department 

offices; most are offices in facilities such as U.S. post offices or 

state, county, township, and municipal government offices. All first-

time applicants for a passport must appear before a passport acceptance 

agent at one of these offices.



Figure 1: The U.S. Passport Application Process:



Source: GAO adaptation of State Department data.



[See PDF for image]



[End of figure]



Passport applicants must submit a passport application, proof of U.S. 

citizenship, proof of identity, two passport photographs, and the 

application fee. Passport acceptance agents, trained by the State 

Department to look for potential fraud, review application packages and 

may ask for additional documentation at their discretion. The agents 

fill out an observation checklist that includes any concerns they have 

about the validity of an applicants identity or citizenship documents. 

Passport acceptance agents also are to ensure that the photographs 

match the applicant. The acceptance agents send the application 

packages to a central application processing center.



Applicants submitting renewal applications may mail them directly to 

the central application processing center. The old passport, which can 

serve as proof of identity and citizenship, is sent with the renewal 

application. About 25 percent of the passport applications the State 

Department receives arrive through the mail.



At the central application processing center, the application 

information is electronically keyed into States computer system, and 

the application package is forwarded to 1 of 16 State Department 

passport centers. States computer systems conduct the following 

checks:



* A name check, using the Consular Lookout and Support System (CLASS). 

CLASS, which is used also before U.S. visas are issued, contains 

lookout records of people who may be ineligible to receive a passport 

and is populated from a variety of sources, including intelligence, 

immigration, and child support enforcement data. CLASS also includes 

information on passports and visas reported lost and stolen. Passport 

applicants are checked against about 3.2 million records in CLASS.



* A check to determine if the applicant already has an active U.S. 

passport. An estimated 55 million U.S. passports are currently valid.



* A check to determine if the applicant has multiple passport 

applications in progress.



At the passport centers, passport examiners review each application, 

including the results of the computer checks, and determine whether to 

issue passports. A passport may be refused to an applicant for a 

variety of reasons: The applicant may be subject to an outstanding 

federal warrant for a felony, subject to a court order committing the 

applicant to a mental institution, or in arrears for child support 

payments in excess of $5,000.[Footnote 10]



A passport examiner looks at an entire application as a whole. A hit 

on one of the computer checks does not necessarily result in a rejected 

application. For example, some government officials who apply may have 

both a personal passport and an official passport. The passport 

examiner may resolve name check hits with other data such as place of 

residence or Social Security number to differentiate between people who 

may have the same name but are not the same person. If the examiner 

suspects a problem with the application package, the case can be given 

to a fraud program manager, who can perform a more detailed 

investigation, such as verifying the authenticity of the identification 

or citizenship documents.



If the passport examiner is satisfied that the applicants documents 

are authentic and that there is no reason to deny a passport, then the 

examiner approves the application and the applicant is issued a U.S. 

passport. Normally, the process takes about 6 weeks. Annually, the 

State Department issues about 7 million passports that are valid for 

either 5 or 10 years, depending on the type of passport and the age of 

the applicant. U.S. passports are depicted in figures 2 and 3.



Figure 2: A U.S. Passport Cover:



Source: State Department.



[See PDF for image]



[End of figure]



Figure 3: A U.S. Passports Biography Page:



Source: State Department.



[See PDF for image]



[End of figure]



How U.S. Visas Are Issued:



With some exceptions, foreign visitors must present a visa to enter the 

United States. Applicants can apply in person for an immigrant or 

nonimmigrant visa at 210 American embassies or consulates (see figure 

4). The vast majority of issued U.S. visas are nonimmigrant visas. An 

applicant for a nonimmigrant visa must submit an application, passport, 

and photograph.[Footnote 11] Some applications may be submitted by mail 

or in a drop box outside the embassy or consulate. About 37 percent are 

submitted this way.



Figure 4: The U.S. Visa Application Process:



Source: GAO adaptation of State Department data.



[See PDF for image]



[End of figure]



After the data are keyed into the State Departments visa computer 

system, a consular officer reviews the application package. The officer 

may interview the applicant, depending on the consular post and the 

type of visa being applied for. Computer checks are conducted:



* A name check, using CLASS, looks for any matches with individuals who 

may be ineligible to receive a visa. Visa applications are checked 

against about 6.5 million records in CLASS.[Footnote 12] CLASS also 

includes records of lost and stolen passports reported by other 

countries.



* A check, using the Consular Consolidated Database (CCD), determines 

whether the applicant has previously applied for a visa or currently 

has a valid U.S. visa. CCD stores information about visa applications, 

issuances, and refusals and obtains information about visa cases every 

5 to 10 minutes from each consular post. CCD has about 58 million visa 

records.



The consular officer makes a decision on whether to issue a visa, based 

on information gathered from the visa application, passport, supporting 

documentation, interview (if applicable), and computer checks. In some 

cases, such as a name-check hit in CLASS, a security advisory opinion 

from State Department headquarters may also be required. Visas may be 

denied for a variety of reasons, including health-related reasons, 

certain criminal offenses, and immigration violations.[Footnote 13] 

Using fraudulent documents to obtain a U.S. visa is also grounds for 

denial.



For nonimmigrant visas, the consular officer must be satisfied that an 

applicant is not intending to become an immigrant. If the consular 

officer is satisfied that the applicants documents are authentic and 

that there is no reason to deny a visa, then the officer approves the 

application and a visa is issued (see figure 5). The process can take 

from a day to several weeks to complete. Last year, of the 10.5 million 

applications received, about 7.5 million nonimmigrant visas were 

issued. Depending on the type of visa and the nationality of the 

applicant, visas can be issued for up to 10 years.



Figure 5: A U.S. Visa Foil:



Source: State Department.



[See PDF for image]



[End of figure]



Inspection at U.S. Ports of Entry:



Lawful entry into the United States generally must be completed through 

an official air, land, or sea port of entry. Nearly 82 percent of the 

more than 500 million inspections occur at land ports. Travelers 

nationalities and how they enter dictate the primary inspection 

procedures (see figure 6). A primary inspector is to question each 

traveler regarding his or her identity and purpose for entering the 

United States. In addition, the nspector can inspect a travelers 

travel documents and perform computer checks on the travelers name or 

motor vehicle license plate. While the State Department is responsible 

for initially granting or denying permission to come to the United 

States, inspectors ultimately decide whether to allow the traveler into 

the country at the ports of entry. The issuance of a U.S. visa does not 

guarantee permission to enter.



Figure 6: The U.S. Port of Entry Inspection Process:



Source: GAO adaptation of INS data.



[See PDF for image]



[End of figure]



At primary inspection, the INS inspector either permits travelers to 

enter or refers them to secondary inspection, where a more detailed 

review of the travel documents or further questioning can be conducted 

by another INS inspector. People may be refused entry for the same 

reasons they can be denied a visa. For U.S. citizens, once an inspector 

is convinced that a traveler is a citizen, the inspection is considered 

complete for immigration purposes. However, checks can still be 

conducted to determine whether the person is wanted by law enforcement 

authorities.



Overall, in fiscal year 2001, about 1.7 percent of travelers entering 

the United States were referred to secondary inspection. Of those 

referred, about 8 percent were denied admission to the United States. 

The numbers in fiscal year 2001 were:



* primary inspections: 505,916,147,



* secondary inspections: 8,838,624, and:



* travelers denied admission: 707,920.



At air ports of entry, commercial carriers are required to submit 

passenger and crew manifests to INS through the Advance Passenger 

Information System (APIS) for flights into the United States. For each 

passenger, the first and last name, date of birth, nationality, and 

passport number are transmitted. With information from APIS, INS 

passenger analysis units can analyze intelligence on passengers before 

flights arrive and identify passengers who will require referral to 

secondary inspection.



Primary inspectors are to examine travel documents from all travelers 

at air ports of entry. A name check is also to be conducted on all 

travelers, using the Interagency Border Inspection System (IBIS). 

Machine-readable passports are read with IBIS; the primary inspector 

manually types in the names of travelers who do not have machine-

readable passports. IBIS is a multiagency database of lookout 

information that alerts inspectors of conditions that may make 

travelers inadmissible to the United States. It also provides 

information about warrants for U.S. citizens who may be wanted by U.S. 

law enforcement agencies. IBIS contains data from law enforcement and 

other agencies with inspection responsibilities at the ports of entry, 

including the Animal Plant Health Inspection Service, the Drug 

Enforcement Administration, and the Federal Bureau of Investigation 

(FBI).



At sea ports of entry, some commercial carriers submit passenger 

manifests to INS through APIS before docking.[Footnote 14] As at 

airports, INSs passenger analysis units identify passengers who 

require further examination when they enter the United States. At sea 

ports of entry equipped with IBIS, the operation is very similar to 

that at an airport. However, at most sea ports of entry, inspections 

are conducted aboard a vessel. When the vessel docks, it is sealed so 

that no goods or persons can be offloaded until it has been inspected.



INS inspectors board ships with the Portable Automated Lookout System 

(PALS) housed on a laptop computer. PALS contains lookout information 

but does not have as many records as IBIS and is not updated as often. 

INS inspectors use PALS to perform name checks and examine documents of 

all aliens aboard a vessel. For U.S. citizens, only documents are 

checked. The inspection process on some of the larger cruise ships can 

take up to 6 hours to complete.



At land ports of entry, the procedures differ for pedestrians and those 

in vehicles. In addition, at land ports, INS shares primary inspection 

responsibilities with the Customs Service of the Treasury Department. 

INS and Customs inspectors are cross-designated to perform each others 

primary inspection duties so that either inspector may conduct the 

primary inspection, following both INS and Customs procedures. INS has 

established procedures to examine travelers expeditiously at many land 

ports of entry because of the large volume of traffic at land 

crossings. Figure 7 shows vehicles waiting at a U.S. land port of 

entry.



Figure 7: Motor Vehicles Waiting for Inspection at the Paso del Norte 

Port of Entry, El Paso, Texas:



Source: GAO.



[See PDF for image]



[End of figure]



For pedestrians at land ports of entry, generally all travelers 

documents are to be checked. If IBIS is available, a travelers name is 

either machine-read from the machine-readable passport or manually 

keyed in by an inspector. U.S. citizens are not required to have a 

passport when entering at a land port. Usually, they need only make an 

oral declaration of U.S. citizenship. Similarly, at land ports of 

entry, Canadians are not required to have a passport. Mexicans who 

possess a border crossing card are not required to present either a 

Mexican passport or a U.S. visa.[Footnote 15] Approximately 5 million 

border crossing cards have been issued to Mexican nationals.



For vehicles at land ports of entry, license plates of all vehicles are 

to be checked through IBIS. Some ports are equipped with automated 

license plate readers. At others, an inspector manually keys license 

plate information into IBIS as vehicles approach the inspection booth. 

As with a name check, IBIS contains lookout information that alerts 

inspectors of conditions that may make the occupants of a vehicle 

inadmissible. Documents and names of the vehicles occupants are 

checked randomly or when an inspector suspects that something is wrong. 

Figure 8 shows a driver being questioned at a land port of entry.



Figure 8: A Driver Being Questioned at a Port of Entry:



Source: U.S. Customs Service.



[See PDF for image]



[End of figure]



At land borders, aliens who require additional documentation, such as 

an Arrival/Departure Record, are to be referred to secondary inspection 

and queried through IBIS. This includes aliens in possession of a 

nonimmigrant visa and those traveling under the visa waiver program.



Some land ports of entry have implemented a program called Secure 

Electronic Network for Travelers Rapid Inspection (SENTRI) to expedite 

the inspection of vehicles and their occupants. With SENTRI, border 

crossers register their vehicles and up to eight occupants, who are 

checked against the IBIS database. Vehicles are identified when 

approaching a SENTRI-equipped port of entry, using a transponder 

installed on the vehicles. Pictures taken of each potential vehicle 

occupant at registration are presented to the primary inspector on a 

computer screen in the inspection booth when a vehicle drives up. The 

inspector visually compares the pictures against the people in the 

vehicle. SENTRI has reduced the average inspection time for each 

vehicle to about 10 seconds from the earlier 30 to 40 seconds.



Similar to SENTRI, other vehicle ports of entry have implemented a 

program called NEXUS that is run jointly by the United States and 

Canada. Instead of issuing a transponder to a vehicle, a proximity card 

is issued to each registered traveler that is detected as a vehicle 

approaches the inspection booth of a NEXUS-equipped port of entry. 

Photographs of travelers detected by their proximity cards are 

presented to the primary inspector, who can then verify the identity of 

each vehicles occupants.



Regardless of the method of entry, secondary inspection gives 

inspectors more time with travelers to determine their admissibility 

than primary inspection. In deciding whether to admit a traveler, the 

inspector reviews the travelers documents for accuracy and validity 

and checks INSs and other agencies databases for any information that 

could affect the travelers entry, including criminal history 

information from the FBI and nonimmigrant visa issuance data from the 

State Department. A fingerprint identification system is also available 

in secondary inspection to determine whether INS has apprehended the 

person for immigration offenses or whether other law enforcement 

agencies are looking for the person.



[End of section]



Chapter 3: Biometric Technologies for Personal Identification:



In this chapter, we define biometrics and explain how they work, 

describe leading and emerging biometrics, and briefly introduce a few 

of the most common applications of biometric technologies. In 

considering how to apply biometrics to border control, we summarize 

data related to accuracy, the lack of applications-dependent 

evaluations, systems susceptibility to deception, the status of 

standards, and users acceptance. After briefly comparing performance 

data on the technologies now considered most viable for U.S. border 

control--facial, fingerprint, and iris recognition and hand geometry--

we end the chapter with a short list of biometric systems in border 

control situations today, here and in other countries.



Biometrics Defined:



When used for personal identification, biometric technologies measure 

and analyze human physiological and behavioral characteristics. 

Identifying a persons physiological characteristics is based on direct 

measurement of a part of the body--fingertips, hand geometry, facial 

geometry, and eye retinas and irises. The corresponding biometric 

technologies are fingerprint recognition, hand geometry, and facial, 

retina, and iris recognition. Identifying behavioral characteristics is 

based on data derived from actions, such as speech and signature, the 

corresponding biometrics being speaker recognition and signature 

recognition.



Biometrics are theoretically very effective personal identifiers 

because the characteristics they measure are thought to be distinct to 

each person. Unlike conventional identification methods that use 

something you have, such as an identification card to gain access to a 

building, or something you know, such as a password to log on to a 

computer system, these characteristics are integral to something you 

are. Because they are tightly bound to an individual, they are more 

reliable, cannot be forgotten, and are less easily lost, stolen, or 

guessed.



How the Technologies Work:



Biometric technologies vary in complexity, capabilities, and 

performance, but all share several elements. Biometric identification 

systems are essentially pattern recognition systems. They use 

acquisition devices such as cameras and scanning devices to capture 

images, recordings, or measurements of an individuals characteristics 

and computer hardware and software to extract, encode, store, and 

compare these characteristics. Because the process is automated, 

biometric decision making is generally very fast, in most cases taking 

only a few seconds in real time.



Depending on the application, biometric systems can be used in one of 

two modes: verification or identification. Verification--also called 

authentication--is used to verify a persons identity--that is, to 

authenticate that individuals are who they say they are. Identification 

is used to establish a persons identity--that is, to determine who a 

person is. Although biometric technologies measure different 

characteristics in substantially different ways, all biometric systems 

involve similar processes that can be divided into two distinct stages: 

enrollment and verification or identification.



Enrollment:



In enrollment, a biometric system is trained to identify a specific 

person. The person first provides an identifier, such as an identity 

card. The biometric is linked to the identity specified on the 

identification document. He or she then presents the biometric (e.g., 

fingertips, hand, or iris) to an acquisition device. The distinctive 

features are located; one or more samples are extracted, encoded, and 

stored as a reference template for future comparisons. Depending on the 

technology, the biometric sample may be collected as an image, a 

recording, or a record of related dynamic measurements. How biometric 

systems extract features and encode and store information in the 

template are based on the system vendors proprietary algorithms.



Template size also varies, depending on the vendor and the technology. 

Although templates can range from 9 to 20,000 bytes, most are smaller 

than 1,000 bytes. Such small sizes allow for rapid comparison. 

Templates can be stored remotely in a central database or within a 

biometric reader device itself; their small size also allows for 

storage on smart cards or tokens.



Minute changes in positioning, distance, pressure, environment, and 

other factors influence the generation of a template, making each 

template likely to be unique, each time an individuals biometric data 

are captured and a new template is generated. Consequently, depending 

on the biometric system, a person may need to present biometric data 

several times in order to enroll. Either the reference template may 

then represent an amalgam of the captured data or several enrollment 

templates may be stored. The quality of the template or templates is 

critical in the overall success of the biometric application. Because 

biometric features can change over time, people may have to reenroll to 

update their reference template. Some technologies can update the 

reference template during matching operations.



The enrollment process also depends on the quality of the identifier 

the enrollee presents. The reference template is linked to the identity 

specified on the identification document. If the identification 

document does not specify the individuals true identity, the reference 

template will be linked to a false identity.



Verification:



In verification systems, the step after enrollment is to verify that a 

person is who he or she claims to be (i.e., the person who enrolled). 

After the individual provides whatever identifier he or she enrolled 

with, the biometric is presented, which the biometric system captures, 

generating a trial template that is based on the vendors algorithm. 

The system then compares the trial biometric template with this 

persons reference template, which was stored in the system during 

enrollment, to determine whether the individuals trial and stored 

templates match (see figure 9).



Figure 9: The Biometric Verification Process:



Source: GAO.



[See PDF for image]



[End of figure]



Verification is often referred to as 1:1 (one-to-one) matching. 

Verification systems can contain databases ranging from dozens to 

millions of enrolled templates but are always predicated on matching an 

individuals presented biometric against his or her reference template. 

Nearly all verification systems can render a match-no-match decision in 

less than a second. A system that requires employees to authenticate 

their claimed identities before granting them access to secure 

buildings or to computers is a verification application.



Identification:



In identification systems, the step after enrollment is to identify who 

the person is. Unlike verification systems, no identifier need be 

provided. To find a match, instead of locating and comparing the 

persons reference template against his or her presented biometric, the 

trial template is compared against the stored reference templates of 

all individuals enrolled in the system (see figure 10). Identification 

systems are referred to as 1:N (one-to-N, or one-to-many) matching 

because an individuals biometric is compared against multiple 

biometric templates in the systems database.



Figure 10: The Biometric Identification Process:



Source: GAO.



[See PDF for image]



[End of figure]



There are two types of identification systems: positive and negative. 

Positive identification systems are designed to ensure that an 

individuals biometric is enrolled in the database. The anticipated 

result of a search is a match. A typical positive identification system 

controls access to a secure building or secure computers by checking 

anyone who seeks access against a database of enrolled employees. The 

goal is to determine whether a person seeking access can be identified 

as having been enrolled in the system.



Negative identification systems are designed to ensure that a persons 

biometric information is not present in a database. The anticipated 

result of a search is a nonmatch. Comparing a persons biometric 

information against a database of all who are registered in a public 

benefits program, for example, can ensure that this person is not 

double dipping by using fraudulent documentation to register under 

multiple identities.



Another type of negative identification system is a surveillance system 

that uses a watch list. Such systems are designed to identify people on 

the watch list and alert authorities for appropriate action. For all 

other people, the system is to check that they are not on the watch 

list and allow them normal passage. The people whose biometrics are in 

the database in these systems may not have provided them voluntarily. 

For instance, for a surveillance system, the biometrics may be faces 

captured from mug shots provided by a law enforcement agency.



No match is ever perfect in either a verification or an identification 

system, because every time a biometric is captured, the template is 

likely to be unique. Therefore, biometric systems can be configured to 

make a match or no-match decision, based on a predefined number, 

referred to as a threshold, that establishes the acceptable degree of 

similarity between the trial template and the enrolled reference 

template. After the comparison, a score representing the degree of 

similarity is generated, and this score is compared to the threshold to 

make a match or no-match decision. For algorithms for which the 

similarity between two templates is calculated, a score exceeding the 

threshold is considered a match. For algorithms for which the 

difference between two templates is calculated, a score below the 

threshold is considered a match. Depending on the setting of the 

threshold in identification systems, sometimes several reference 

templates can be considered matches to the trial template, with the 

better scores corresponding to better matches.



Leading Biometric Technologies:



A growing number of biometric technologies have been proposed over the 

past several years, but only in the past 5 years have the leading ones 

become more widely deployed. Some technologies are better suited to 

specific applications than others, and some are more acceptable to 

users. Table 5 lists the seven leading biometric technologies we 

describe in this section.



Table 5: Leading Biometric Technologies and Their Template Size:



Technology: Facial recognition; How it works: Captures and compares 

facial patterns; Template size in bytes: 84 or 1,300[A].



Technology: Fingerprint recognition; How it works: Captures and 

compares fingertip patterns; Template size in bytes: 250-1,000.



Technology: Hand geometry; How it works: Measures and compares 

dimensions of hand and fingers; Template size in bytes: 9.



Technology: Iris recognition; How it works: Captures and compares iris 

patterns; Template size in bytes: 512.



Technology: Retina recognition; How it works: Captures and compares 

retina patterns; Template size in bytes: 96.



Technology: Signature recognition; How it works: Captures and compares 

rhythm, acceleration, and pressure flow of signature; Template size in 

bytes: 1,000-3,000.



Technology: Speaker recognition; How it works: Captures and compares 

cadence, pitch, and tone of vocal tract; Template size in bytes: 

10,000-20,000.



[A] Depending on the algorithm.



Source: GAO analysis of manufacturer data.



[End of table]



Facial Recognition:



Facial recognition technology identifies people by analyzing features 

of the face not easily altered--the upper outlines of the eye sockets, 

the areas around the cheekbones, and the sides of the mouth. The 

technology is typically used to compare a live facial scan to a stored 

template, but it can also be used in comparing static images such as 

digitized passport photographs. Facial recognition can be used in both 

verification and identification systems. In addition, because facial 

images can be captured from video cameras, facial recognition is the 

only biometric that can be used for surveillance purposes.



The two primary algorithms used in facial recognition systems are based 

on the eigenface method and local feature analysis (LFA). The eigenface 

method looks at the face as a whole and represents a persons face as a 

set of templates that require 1,300 bytes. LFA breaks down the face 

into feature-specific fields, such as the eyes, nose, mouth, and 

cheeks, creating an 84 byte template.



Fingerprint Recognition:



Fingerprint recognition is one of the best known and most widely used 

biometric technologies. Automated systems have been commercially 

available since the early 1970s, and there are currently more than 75 

fingerprint recognition technology companies. Until recently, it was 

used primarily in law enforcement applications.



Fingerprint recognition technology extracts features from impressions 

made by the distinct ridges on the fingertips. The fingerprints can be 

either flat or rolled. A flat print captures only an impression of the 

central area between the fingertip and the first knuckle; a rolled 

print captures ridges on both sides of the finger.



An image of the fingerprint is captured by a scanner, enhanced, and 

converted into a template. Scanner technologies can be optical, 

silicon, or ultrasound technologies. Ultrasound, while potentially the 

most accurate, has not been demonstrated in widespread use. Optical 

scanners are the most commonly used. During enhancement, noise caused 

by such things as dirt, cuts, scars, and creases or dry, wet, or worn 

fingerprints is reduced, and the definition of the ridges is enhanced. 

Template size ranges from 250 bytes up to 1,000 bytes, depending on 

which vendors proprietary algorithm the system uses. Approximately 80 

percent of vendors base their algorithms on the extraction of minutiae 

points relating to breaks in the ridges of the fingertips. Other 

algorithms are based on extracting ridge patterns.



Hand Geometry:



Hand geometry systems have been in use for almost 30 years for access 

control to facilities ranging from nuclear power plants to day care 

centers. Hand geometry technology measures the width, height, and 

length of the fingers, distances between joints, and shapes of the 

knuckles.



Hand geometry systems use an optical camera and light-emitting diodes 

with mirrors and reflectors to capture two orthogonal two-dimensional 

images of the back and sides of the hand. Ninety-six measurements are 

then extracted and a 9 byte template is derived, making it the smallest 

in the biometric industry.



Although the basic shape of an individuals hand remains relatively 

stable over his or her lifetime, natural and environmental factors can 

cause slight changes.



Iris Recognition:



Iris recognition technology is based on the distinctly colored ring 

surrounding the pupil of the eye. Made from elastic connective tissue, 

the iris is a very rich source of biometric data, having approximately 

266 distinctive characteristics. These include the trabecular meshwork, 

a tissue that gives the appearance of dividing the iris radially, with 

striations, rings, furrows, a corona, and freckles. Iris recognition 

technology uses about 173 of these distinctive characteristics. Formed 

during the eighth month of gestation, these characteristics reportedly 

remain stable throughout a persons lifetime, except in cases of 

injury.



Iris recognition systems use a small, high-quality camera to capture a 

black-and-white, high-resolution image of the iris. They then define 

the boundaries of the iris, establish a coordinate system over the 

iris, and define the zones for analysis within the coordinate system. 

The visible characteristics within the zones are then converted into a 

512 byte template that is used to identify or verify the identity of an 

individual.



Retina Recognition:



Retina recognition technology captures and analyzes the patterns of 

blood vessels on the thin nerve on the back of the eyeball that 

processes light entering through the pupil. Retinal patterns are highly 

distinctive traits. Every eye has its own totally unique pattern of 

blood vessels; even the eyes of identical twins are distinct. Although 

each pattern normally remains stable over a persons lifetime, it can 

be affected by disease such as glaucoma, diabetes, high blood pressure, 

and autoimmune deficiency syndrome.



The fact that the retina is small, internal, and difficult to measure 

makes capturing its image more difficult than most biometric 

technologies. An individual must position the eye very close to the 

lens of the retina-scan device, gaze directly into the lens, and remain 

perfectly still while focusing on a revolving light while a small 

camera scans the retina through the pupil. Any movement can interfere 

with the process and can require restarting. Enrollment can easily take 

more than a minute. The generated template is only 96 bytes, one of the 

smallest of the biometric technologies.



One of the most accurate and most reliable of the biometric 

technologies, it is used for access control in government and military 

environments that require very high security, such as nuclear weapons 

and research sites. However, the great degree of effort and cooperation 

required of users has made it one of the least deployed of all the 

biometric technologies. Newer, faster, better retina recognition 

technologies are being developed.



Signature Recognition:



Signature recognition authenticates identity by measuring handwritten 

signatures. The signature is treated as a series of movements that 

contain unique biometric data, such as personal rhythm, acceleration, 

and pressure flow. Unlike electronic signature capture, which treats 

the signature as a graphic image, signature recognition technology 

measures how the signature is signed.



In a signature recognition system, a person signs his or her signature 

on a digitized graphics tablet or personal digital assistant. The 

system analyzes signature dynamics such as speed, relative speed, 

stroke order, stroke count, and pressure. The technology can also track 

each persons natural signature fluctuations over time.



The signature dynamics information is encrypted and compressed into a 

template that can range from slightly larger than 1,000 bytes to 

approximately 3,000 bytes. These templates are large by biometric 

standards and reflect the variety of data available in a typical 

signature.



Speaker Recognition:



Differences in how different peoples voices sound result from a 

combination of physiological differences in the shape of vocal tracts 

and learned speaking habits. Speaker recognition technology uses these 

differences to discriminate between speakers.



During enrollment, speaker recognition systems capture samples of a 

persons speech by having him or her speak some predetermined 

information into a microphone or telephone a number of times. This 

information, known as a passphrase, can be a piece of information such 

as a name, birth month, birth city, or favorite color or a sequence of 

numbers. Text independent systems are also available that recognize a 

speaker without using a predefined phrase.



This phrase is converted from analog to digital format, and the 

distinctive vocal characteristics, such as pitch, cadence, and tone, 

are extracted, and a speaker model is established. A template is then 

generated and stored for future comparisons. Voice templates are much 

larger than templates generated from other biometric technologies, 

typically 10,000 to 20,000 bytes.



Speaker recognition can be used to verify a persons claimed identity 

or to identify a particular person. It is often where voice is the only 

available biometric identifier, such as telephone and call centers.



Emerging Biometric Technologies:



Newer biometric technologies using diverse physiological and behavioral 

characteristics are in various stages of development. Some are 

commercially available, some may emerge over the next 2 to 4 years, and 

others are many years from implementation. Table 6 lists the 9 we 

describe in this section and their current maturity. Each techniques 

performance can vary widely, depending on how it is used and its 

environment in which it is used.



Table 6: Emerging Biometric Technologies and Their Maturity:



Technology: Vein scan; How it works: Captures images of blood vessel 

patterns; Maturity: Commercially available.



Technology: Facial thermography; How it works: Infrared camera detects 

heat patterns created by the branching of blood vessels and emitted 

from the skin; Maturity: Initial commercialization attempts failed 

because of high cost.



Technology: DNA matching; How it works: Compares actual samples of DNA 

rather than templates generated from samples; Maturity: Many years 

from implementation.



Technology: Odor sensing; How it works: Captures the volatile chemicals 

that the skins pores emit; Maturity: Years away from commercial 

release.



Technology: Blood pulse measurement; How it works: Infrared sensors 

measure blood pulse on a finger; Maturity: Experimental.



Technology: Skin pattern recognition; How it works: Extracts distinct 

optical patterns by spectroscopic measurement of light scattered by the 

skin; Maturity: Emerging.



Technology: Nailbed identification; How it works: An interferometer 

detects phase changes in back-scattered light shone on the fingernail; 

reconstructs distinct dimensions of the nailbed and generates a one-

dimensional map; Maturity: Emerging.



Technology: Gait recognition; How it works: Captures a sequence of 

images to derive and analyze motion characteristics; Maturity: 

Emerging; requires further development.



Technology: Ear shape recognition; How it works: Is based on 

distinctive ear shape and the structure of the cartilaginous, 

projecting portion of the outer ear; Maturity: Still a research 

topic.



Source: GAO analysis.



[End of table]



Vein scan biometric technology can automatically identify a person from 

the patterns of the blood vessels in the back of the hand. The 

technology uses near-infrared light to detect vein vessel patterns. 

Vein patterns are distinctive between twins and even between a persons 

left and right hand. Developed before birth, they are highly stable and 

robust, changing throughout ones life only in overall size. The 

technology is not intrusive, and works even if the hand is not clean. 

It is commercially available.



Facial thermography detects heat patterns created by the branching of 

blood vessels and emitted from the skin. These patterns, called 

thermograms, are highly distinctive. Even identical twins have 

different thermograms. Developed in the mid-1990s, thermography works 

much like facial recognition, except that an infrared camera is used to 

capture the images. The advantages of facial thermography over other 

biometric technologies are that it is not intrusive--no physical 

contact is required--every living person presents a usable image, and 

the image can be collected on the fly. Also, unlike visible light 

systems, infrared systems work accurately even in dim light or total 

darkness. Although identification systems using facial thermograms were 

undertaken in 1997, the effort was suspended because of the cost of 

manufacturing the system.



DNA matching is a type of biometric in the sense that it uses a 

physiological characteristic for personal identification. It is 

considered to be the ultimate biometric technology in that it can 

produce proof-positive identification of a person, except in the case 

of identical twins. However, DNA differs from standard biometrics in 

several ways. It compares actual samples rather than templates 

generated from samples. Also, because not all stages of DNA comparison 

are automated, the comparison cannot be made in real time. DNAs use 

for identification is currently limited to forensic applications. The 

technology is many years away from any other kind of implementation and 

will be very intrusive.



Researchers are investigating a biometric technology that can 

distinguish and measure body odor. This technology would use an odor-

sensing instrument (an electronic nose) to capture the volatile 

chemicals that skin pores all over the body emit to make up a persons 

smell. Although distinguishing one person from another by odor may 

eventually be feasible, the fact that personal habits such as the use 

of deodorants and perfumes, diet, and medication influence human body 

odor renders the development of this technology quite complex.



Blood pulse biometrics measure the blood pulse on a finger with 

infrared sensors. This technology is still experimental and has a high 

false match rate, making it impractical for personal identification.



The exact composition of all the skin elements is distinctive to each 

person. For example, skin layers differ in thickness, the interfaces 

between the layers have different undulations, pigmentation differs, 

collagen fibers and other proteins differ in density, and the capillary 

beds have distinct densities and locations beneath the skin. Skin 

pattern recognition technology measures the characteristic spectrum of 

an individuals skin. A light sensor illuminates a small patch of skin 

with a beam of visible and near-infrared light. The light is measured 

with a spectroscope after being scattered by the skin. The measurements 

are analyzed, and a distinct optical pattern can be extracted.



Nailbed identification technology is based on the distinct 

longitudinal, tongue-in-groove spatial arrangement of the epidermal 

structure directly beneath the fingernail. This structure is mimicked 

in the ridges on the outer surface of the nail. When an interferometer 

is used to detect phase changes in back-scattered light shone on the 

fingernail, the distinct dimensions of the nailbed can be reconstructed 

and a one-dimensional map can be generated.



Gait recognition, recognizing individuals by their distinctive walk, 

captures a sequence of images to derive and analyze motion 

characteristics. A persons gait can be hard to disguise because a 

persons musculature essentially limits the variation of motion, and 

measuring it requires no contact with the person. However, gait can be 

obscured or disguised if the individual, for example, is wearing loose 

fitting clothes. Preliminary results have confirmed its potential, but 

further development is necessary before its performance, limitations, 

and advantages can be fully assessed.



Ear shape recognition is still a research topic. It is based on the 

distinctive shape of each persons ears and the structure of the 

largely cartilaginous, projecting portion of the outer ear. Although 

ear biometrics appears to be promising, no commercial systems are 

available.



Common Applications of Biometric Technologies:



Reduced cost, smaller size, greater accuracy, and greater ease of use 

are making biometrics increasingly feasible for international travel 

documentation, citizenship identification, automated banking, and 

benefits dispersal. Biometrics have either been adopted or are being 

contemplated for adoption in dozens of applications, ranging from 

modest--providing time and attendance reports for small companies--to 

expansive--ensuring the integrity of a registration database of 10 

million voters.



Access Control:



Biometric systems have long been used to complement or replace badges 

and keys in controlling access to entire facilities or specific areas 

within a facility. The entrances to more than half the nuclear power 

plants in the United States employ biometric hand geometry systems. 

They protected athletes housed in Olympic Village at the 1996 games in 

Atlanta.



Recent reductions in the price of biometric hardware have spurred 

logical access control applications. Fingerprint, iris, and speaker 

recognition are replacing passwords to authenticate individuals 

accessing computers and networks. The Office of Legislative Counsel of 

the U.S. House of Representatives, for example, is installing an iris 

recognition system to protect confidential files and working documents. 

Other federal agencies, including the Department of Defense (DOD), 

Department of Energy, and Department of Justice, as well as the 

intelligence community, are adopting similar technologies.



Fraud Reduction:



Leading banks and other financial service companies are experimenting 

with facial, iris, and speaker recognition systems to authenticate ATM 

users and to combat credit and debit card fraud. Hand geometry and iris 

and facial recognition have been deployed at ATMs in North America, 

Europe, and Asia. The JPMorgan Chase Bank allows some customers to 

access accounts by speaker recognition. To address concerns about 

security and fraud, organizations that offer Internet shopping are also 

considering biometric technologies to authorize various types of 

transactions.



Biometrics can also be used in monitoring applications. Adding 

biometrics to time and attendance processes, for example, helps prevent 

hourly employees from punching time cards for their absent friends, a 

practice that is estimated to cost employers hundreds of millions of 

dollars annually. Biometrics are also being applied to prevent prison 

inmates from swapping identities with visitors as they leave prisons.



In addition, biometric technologies are being used in large-scale 

identification systems to determine whether applicants are already 

enrolled under a different identity. One specific application has been 

to prevent individuals from cheating public sector benefits programs by 

collecting benefits under multiple identities. A number of states have 

made fingerprinting a requirement for registration for welfare and 

other types of public aid. Since biometric systems were deployed, the 

number of individuals claiming benefits has dropped dramatically in 

several states that use such systems. Internationally, in the 

Philippines, South Africa, and Spain, programs to streamline or 

legitimize issuing government benefits have enrolled millions of 

citizens.



Licensing and Voter Applications:



Several states have implemented biometric systems to stop drivers, 

particularly truck drivers, from maintaining duplicate licenses or 

swapping licenses when crossing state lines or national borders. Large-

scale identification systems are also being used to register voters for 

national and local elections to prevent voter fraud. Mexico, for 

example, uses facial recognition technology to check voter rolls for 

duplicates in its national elections. Brazil, Costa Rica, the Dominican 

Republic, Panama, and Italy use fingerprints to verify voters at 

polling stations.



Criminal Identification and Surveillance:



Criminal identification is far and away the oldest, most widespread, 

large-scale identification use of biometric systems. Automated 

fingerprint recognition systems are employed around the world to 

identify suspects within local, state, or federal databases of known 

offenders. Facial recognition is also being used for criminal 

identification, although the technology does not provide the same high 

degree of accuracy as the older technology. Employee background checks 

are another application of large-scale systems. The governments of 

Argentina, China, Nigeria, and Yemen are all planning to implement 

biometrics in their national identification programs.



Surveillance is one of the most recent applications of biometric 

systems. Although the majority of the major casinos in North America 

have deployed facial recognition surveillance systems for some time to 

spot known cheaters, systems are now publicly deployed in Newham 

Borough, England; Tampa, Florida; and Canadas Lester B. Pearson 

International Airport in Toronto. More recently, they have been used 

sporadically at such major events as the 2001 Super Bowl in Tampa, 

Florida, and the winter Olympics at Salt Lake City in 2002.



Performance Issues:



Biometric technologies are maturing but are still not widespread or 

pervasive because of performance issues, including accuracy, the lack 

of applications-dependent evaluations, their potential susceptibility 

to deception, the lack of standards, and questions of users 

acceptance. These issues should be kept in mind when considering 

biometrics for U.S. border control.



Accuracy:



Biometrics is a very young technology, having only recently reached the 

point at which basic matching performance can be acceptably deployed. 

It is necessary to analyze several metrics to determine the strengths 

and weaknesses of each technology and vendor for a given application.



The three key performance metrics are false match rate (FMR), false 

nonmatch rate (FNMR), and failure to enroll rate (FTER). A false match 

occurs when a system incorrectly matches an identity, and FMR is the 

probability of individuals being wrongly matched. In verification and 

positive identification systems, authorized people can be granted 

access to facilities or resources as the result of incorrect matches. 

In a negative identification system, the result of a false match may be 

to deny access. For example, if a new applicant to a public benefits 

program is falsely matched with a person previously enrolled in that 

program under another identity, the applicant may be denied access to 

benefits. The FMR, sometimes called the false positive rate, is 

sometimes confused with the false accept rate. The FMR is the 

probability of an erroneous match in a single template comparison while 

the false accept rate is a system measure that a person is erroneously 

matched, combining the results of all template comparisons. For 

example, in an identification match, the FMR would be the probability 

that the trial template erroneously matches a single selected reference 

template. The false accept rate would be the probability that the trial 

template erroneously matches any of the reference templates.



A false nonmatch occurs when a system rejects a valid identity, and 

FNMR is the probability of valid individuals being wrongly not matched. 

In verification and positive identification systems, people can be 

denied access to some facility or resource as the result of a systems 

failure to make a correct match. In negative identification systems, 

the result of a false nonmatch may be that a person is granted access 

to resources to which she should be denied. For example, if a person 

who has enrolled in a public benefits program under another identity is 

not correctly matched, she will succeed in gaining fraudulent access to 

benefits. The FNMR, sometimes called the false negative rate, is 

sometimes confused with the false reject rate. The relationship between 

FNMR and the false reject rate is similar to the relationship between 

the FMR and the false accept rate. The FNMR is the probability of an 

erroneous nonmatch for a single template comparison, while the false 

reject rate is a system measure that a person is erroneously not 

matched, combining the results of all template comparisons.



False matches may occur because there is a high degree of similarity 

between two individuals characteristics. False nonmatches occur 

because there is not a sufficiently strong similarity between an 

individuals enrollment and trial templates, which could be caused by 

any number of conditions. For example, an individuals biometric data 

may have changed as a result of aging or injury. If biometric systems 

were perfect, both error rates would be zero. However, because 

biometric systems cannot identify individuals with 100 percent 

accuracy, a trade-off exists between the two.



False match and nonmatch rates are inversely related; they must 

therefore always be assessed in tandem, and acceptable risk levels must 

be balanced with the disadvantages of inconvenience. For example, in 

access control, perfect security would require denying access to 

everyone. Conversely, granting access to everyone would result in 

denying access to no one. Obviously, neither extreme is reasonable, and 

biometric systems must operate somewhere between the two.



For most applications, how much risk one is willing to tolerate is the 

overriding factor, which translates into determining the acceptable 

FMR. The greater the risk entailed by a false match, the lower the 

tolerable FMR. For example, an application that controlled access to a 

secure area would require that the FMR be set low, which would result 

in a high FNMR. However, an application that controlled access to a 

banks ATM might have to sacrifice some degree of security and set a 

higher FMR (and hence a lower FNMR) to avoid the risk of irritating 

legitimate customers by wrongly rejecting them. This is displayed in 

figure 11.



Figure 11: The General Relationship between FMR and FNMR:



Note: Equal error rate is the point at which FMR equals FNMR.



Source: GAO.



[See PDF for image]



[End of figure]



As figure 11 shows, selecting a lower FMR increases the FNMR. Perfect 

security would require setting the FMR to 0, in which case the FNMR 

would be 1. At the other extreme, setting the FNMR to 0 would result in 

an FMR of 1.



The expectations regarding FMR and FNMR are very different for 

verification and identification systems. In a verification system, a 

user is checked against one or a few reference templates to confirm the 

users claimed identity. A much higher standard is required for 

identification systems where checks are made against all reference 

templates in the database. Consequently, a much lower FMR is required 

for a large-scale positive identification system than for a similar 

size verification system, simply because even a small percentage of 

false matches for a system that performed billions of comparisons a day 

would overwhelm the resources dedicated to investigating positive 

matches. The larger the identification database, the lower the false 

match rate needs to be to maintain the number of false positives at a 

manageable amount.



Vendors often use equal error rate (EER), an additional metric derived 

from FMR and FNMR, to describe the accuracy of their biometric systems. 

EER refers to the point at which FMR equals FNMR (see figure 11). 

Setting a systems threshold at its EER will result in the probability 

that a person is falsely matched equaling the probability that a person 

is falsely not matched. However, this statistic tends to oversimplify 

the balance between FMR and FNMR, because in few real-world 

applications is the need for security identical to the need for 

convenience.



FTER is a biometric systems third critical accuracy metric. FTER 

measures the probability that a person will be unable to enroll. 

Failure to enroll (FTE) may stem from an insufficiently distinctive 

biometric sample or from a system design that makes it difficult to 

provide consistent biometric data. The fingerprints of people who work 

extensively at manual labor are often too worn to be captured. A high 

percentage of people are unable to enroll in retina recognition systems 

because of the precision such systems require. People who are mute 

cannot use voice systems, and people lacking fingers or hands from 

congenital disease, surgery, or injury cannot use fingerprint or hand 

geometry systems. Although between 1 and 3 percent of the general 

public does not have the body part required for using any one biometric 

system, they are normally not counted in a systems FTER.



Multimodal Biometrics:



Because biometric systems based solely on a single biometric may not 

always meet performance requirements, the development of systems that 

integrate two or more biometrics is emerging as a trend. Multiple 

biometrics could be two types of biometrics, such as combining facial 

and iris recognition. Multiple biometrics could also involve multiple 

instances of a single biometric, such as 1, 2, or 10 fingerprints, 2 

hands, and 2 eyes. One prototype system integrates fingerprint and 

facial recognition technologies to improve identification. A 

commercially available system combines face, lip movement, and speaker 

recognition to control access to physical structures and small office 

computer networks. Depending on the application, both systems can 

operate for either verification or identification. Experimental results 

have demonstrated that the identities established by systems that use 

more than one biometric could be more reliable, be applied to large 

target populations, and improve response time.



The Lack of Applications-Dependent Evaluations:



Biometric companies have primarily been concerned with testing the 

accuracy of their technologies in highly controlled environments, using 

static or artificially generated templates, images, and data. The 

results of their tests, as quoted by vendors, are quite extraordinary, 

such as claims of FMRs of 1 in 100,000, 1 in a billion, or even 1 in 

1078 and FNMRs in the vicinity of 1 percent, 0.1 percent, and 0.01 

percent. However, because the performance of a technology depends 

greatly on how and where it is deployed, such numbers have proven to be 

far more impressive than real-life performance data.



Until recently, there was no set methodology for testing the same 

technologies in different applications. A recently developed 

methodology uses a three-step evaluation protocol: a technology 

evaluation, followed by a scenario evaluation and an operational 

evaluation of biometric systems.[Footnote 16] Each of the methodologys 

three types of evaluation requires a different protocol and produces 

different results. A technology evaluation compares competing 

algorithms from a single technology to identify the most promising 

approaches. A scenario evaluation tests overall system performance for 

a class of applications under conditions that model real-world 

applications. An operational evaluation measures performance for a 

specific biometric system for a specific application in the actual 

operating environment with actual users of the system. The Facial 

Recognition Vendor Test 2000 (FRVT 2000), which assessed the 

capabilities of commercially available facial recognition systems, was 

based on this evaluation methodology and included elements of 

technology and scenario evaluations.[Footnote 17]



Studies by respected organizations in the United States and the United 

Kingdom have provided a number of effective measures of the actual 

performance of biometric systems in different real-world environments. 

Sandia National Laboratories 1996 evaluation of an iris recognition 

identification system in an access-control environment included FNMR-

FMR results. The International Biometric Group (IBG) has since 1999 

conducted side-by-side comparative performance testing of leading 

biometric identity verification systems under real-world operating 

conditions. Test results have included FNMRs, FMRs, and FTERs for 

fingerprint, iris, facial, voice, keystroke, and signature systems. In 

2000, the British National Physical Laboratory (NPL) tested biometric 

identity verification systems, including fingerprint, hand, iris, 

facial, voice, and vein, in real-world environments.[Footnote 18] 

FNMRs, FMRs, and FTERs were reported. The U.S. Army Research 

Laboratorys pilot study of iris and facial recognition systems in 

2000-01 reported performance results that included error rates as well 

as user perception and acceptability. Table 7 lists the significant 

independent tests and their results since 1991.



Table 7: Independent Biometric Test Results, 1991-2002:



Test name: Test of Biometric Technologies; Who conducted: Sandia 

National Laboratories; Date: 1991; Technology: Fingerprint, hand, 

retina, signature, speaker; Type: Technology; Performance measure: FMR, 

FNMR, accept time.



Test name: Hand Geometry Field Application; Who conducted: Sandia 

National Laboratories; Date: 1995; Technology: Hand; Type: Scenario; 

Performance measure: Varied lighting, maintenance.



Test name: IriScan Prototype Identifier; Who conducted: Sandia 

National; Laboratories; Date: 1996; Technology: Iris; Type: Scenario; 

Performance measure: FMR, FNMR, enrollment time, transaction time.



Test name: Speaker Recognition Evaluations; Who conducted: National 

Institute of Standards and Technology; Date: 1996 to present; 

Technology: Speaker; Type: Technology; Performance measure: Handset 

variation, test segment duration, speaker tracking, 1-speaker and 2-

speaker, cellular data.



Test name: Philippine AFIS Benchmark Test; Who conducted: National 

Biometric Test Center; Date: 1997; Technology: Fingerprint; Type: 

Technology; Performance measure: FMR, FNMR.



Test name: SENTRI Test; Who conducted: INS; Date: 1998; Technology: 

Facial, speaker; Type: Scenario; Performance measure: FNMR.



Test name: Comparative Biometric Testing; Who conducted: IBG; Date: 

1999 to present; Technology: Facial, fingerprint, iris, keystroke, 

signature, speaker; Type: Scenario; Performance measure: FMR, FNMR, 

enrollment rate, ergonomics, ease of use, temporal.



Test name: Biometric Product Testing; Who conducted: NPL; Date: 2000; 

Technology: Facial, fingerprint, hand, iris, vein, speaker; Type: 

Scenario; Performance measure: Failure to enroll and acquire, FMR, 

FNMR, transaction time, male versus female.



Test name: FRVT 2000; Who conducted: DOD, National Institute of 

Justice, NIST; Date: 2000; Technology: Facial; Type: Scenario, 

technology; Performance measure: Probability of identity, probability 

of verification, distance, temporal, expressions, pose, resolution, 

media.



Test name: Fingerprint Verification Competition 2000; Who conducted: 

University of Bologna, Michigan State University, San Jose State 

University; Date: 2000; Technology: Fingerprint; Type: Technology; 

Performance measure: Enrollment time, matching time, EER.



Test name: Facial Recognition Technology; Who conducted: Department of 

State, Bureau of Consular Affairs; Date: 2001; Technology: Facial; 

Type: Technology; Performance measure: FNMR.



Test name: Personnel Identification Pilot Study; Who conducted: Army 

Research Laboratory; Date: 2001; Technology: Facial, iris; Type: 

Operational; Performance measure: FMR, FNMR.



Test name: Fingerprint Identification Device; Who conducted: Federal 

Aviation Administration and Safe Skies; Date: 2001; Technology: 

Fingerprint; Type: Operational; Performance measure: FMR, FNMR, 

enrollment and transit time, abnormal conditions (oil, grease, powder, 

injury, moist or dry skin, offset angle, contact pressure, 

backlighting, attempts to defeat).



Test name: Hand Geometry Identification Device; Who conducted: FAA and 

Safe Skies; Date: 2001; Technology: Hand; Type: Operational; 

Performance measure: FMR, FNMR, enrollment and transit time, abnormal 

conditions (rings, injuries, backlighting, attempts to defeat).



Test name: Facial Recognition Device; Who conducted: FAA and Safe 

Skies; Date: 2002; Technology: Facial; Type: Operational; Performance 

measure: FMR, FNMR, enrollment and transit time, abnormal conditions 

(glasses, facial hair, backlighting, bandages, false photograph).



Test name: Iris Recognition Device; Who conducted: FAA and Safe Skies; 

Date: 2002; Technology: Iris; Type: Operational; Performance measure: 

FTE, FNMR, enrollment and transit times.



Test name: Biometric Security Test; Who conducted: ct Magazine; Date: 

2002; Technology: Iris, fingerprint, facial; Type: Technology; 

Performance measure: Attempts to defeat.



Test name: Fingerprint Verification Competition 2002; Who conducted: 

University of Bologna, Michigan State University, San Jose State 

University; Date: 2002; Technology: Fingerprint; Type: Technology; 

Performance measure: Enrollment time, matching time, EER, FMR.



Test name: Facial Recognition Vendor Test 2002; Who conducted: 15 

agencies and organizations, including DOD, National Institute of 

Justice, and NIST; Date: In progress; Technology: Facial; Type: 

Scenario, technology; Performance measure: In progress.



Source: GAO analysis of independent biometric test results.



[End of table]



A rash of new tests of biometric systems has recently been initiated. 

The results are likely to provide more sound means of evaluating the 

strengths and weaknesses of the different technologies and vendors 

products.



Susceptibility to Deception:



Can biometric systems be defeated? Many vendors claim that their 

systems cannot be fooled because they are able to detect whether or not 

an individuals presented biometric is a live sample. Many biometric 

devices can, in principle, determine whether a live characteristic is 

being presented. Some fingerprint systems, for example, test for 

liveness by relying on the unique conductive nature of live fingers. 

Others measure blood flow or ensure that the ridges at the periphery of 

a print are arrayed the same as in normal finger placement.



Although hand geometry systems do not actually check for a live 

biometric, fingers have to be positioned so that they put pressure on 

the correct pegs. Facial recognition checks for liveness by requiring 

users to change their facial expression--by blinking their eyes or 

smiling, for example--in order to successfully generate a template. 

With iris recognition, light shone on the eye can be varied for 

recording pupil dilation. Some speaker recognition systems can generate 

a random sequence of numbers for each verification to ensure that a 

recorded voice is not being played back. Moreover, low-fidelity 

recording devices are generally not able to capture the high and low 

frequencies necessary for verification.



Nevertheless, recent tests are casting doubt on vendors claims 

regarding the maturity and security of their technologies. German 

technology magazine ct carried out tests on 11 commercially available 

biometric systems used to control access to computers.[Footnote 19] 

Facial, fingerprint, and iris recognition systems were defeated by 

testers using photographs and videos, reactivated latent images, and 

forgeries.



They spoofed one fingerprint recognition system by reactivating latent 

fingerprints left on the surface of its capacitive sensor, simply by 

breathing on the prints, placing a thin-walled water-filled plastic bag 

on the sensors surface, and dusting the prints with graphite powder 

and gently applying pressure to an adhesive film stretched over them. 

They outfoxed another fingerprint recognition system whose optical 

scanner required that an object be resting on its surface by creating a 

silicone copy of a fingerprint of an enrolled person from a candle wax 

mold.



They spoofed an iris recognition system by using a high-resolution 

printed picture of an enrolled persons iris with a live persons pupil 

shining through a miniature hole cut out of the pictures pupil. They 

beat a well-known facial recognition system by using a laptop computer 

to play back live images of an enrolled person to the camera. They 

fooled another facial system by holding up a photograph of an enrolled 

person.



In another recent test, an engineering professor demonstrated how 11 

commercially available fingerprint biometric systems could all be 

fooled with a molded gelatin finger. A further recent test revealed 

that biometric systems could be defeated by cracking the code of the 

templates stored inside them. Using manufactured images that displayed 

the characteristics required by the matching software, the tester 

defeated commercially available fingerprint and retina recognition 

systems. These tests certainly call into question the claim that 

biometric systems cannot be deceived.



The Development of Biometric Standards:



Identifying, exchanging, and integrating information from different and 

perhaps unfamiliar sources and functions are essential to an effective 

biometrics application. Without predefined standards, system 

developers may need to define in detail the precise steps for 

exchanging information, a potentially complex, time-consuming, and 

expensive process. The risks associated with not adopting standards for 

a system are significant, because of the length of time the system must 

remain operational and the rapid pace of technological change. The 

proprietary technology of choice today may not be cost-effective or 

even supported tomorrow.



Attempts to standardize biometrics are under way in various areas, such 

as the mechanics of image capture, the accuracy of data as they are 

extracted, and device interoperability. However, the majority of 

biometric devices and their software are still proprietary in many 

respects. For example, the method for extracting features from a 

biometric sample such as a fingerprint differs among most, if not all, 

vendors. Templates containing biometric data, time stamps, encryption 

features, and device information are also not standard. Devices from 

company A do not necessarily work compatibly with devices from 

companies B and C. Incompatibility is also an issue for communication 

between devices and host computers, since programs are developed from 

vendors software development kits. Each vendor designs a software 

development kit for its own products, so that the programs developed 

for one vendors product generally cannot be used with another vendors 

products.



The biometrics community does employ several standards, however. We 

list seven:



* The wavelet scalar quantization (WSQ) gray-scale fingerprint image 

compression algorithm is the standard for exchanging fingerprint images 

within the criminal justice community. WSQ defines a class of encoders 

and a single decoder with sufficient generality to decode compressed 

image data produced by any compliant encoder.



* The National Institute of Standards and Technology (NIST) issued the 

Common Biometric Exchange File Format (CBEFF) on January 3, 2001. The 

standard is designed to (1) facilitate biometric data interchange 

between different system components or between systems, (2) promote the 

interoperability of biometric-based application programs and systems, 

(3) provide forward compatibility for technology improvements, and (4) 

simplify the integration of software and hardware from different 

vendors.



* BioAPI Consortium has developed BioAPI, a specification for a high-

level generic biometric authentication model suited for any form of 

biometric technology. It covers the basic functions of enrollment, 

verification, and identification and includes a database interface to 

allow a biometric service provider to manage the identification 

population for optimum performance. It also provides methods that allow 

an application to manage the capture of samples on a client and the 

enrollment, verification, and identification on a server. While it does 

not define security requirements for biometric applications and service 

providers, it does explain how the application programming interface 

(API) is intended to support good security practices.



* In May 2000, Microsoft Corp. and I/O Software Inc. announced that 

they would cooperate to foster the widespread growth of biometrics 

through the integration of biometric authentication technology in 

future versions of the Microsoft Windows operating system. The 

resulting biometric application programming interface (BAPI) is 

expected to define a standard software protocol and API for 

communication between software applications and biometric devices 

running on Microsoft Windows platforms. BAPI is expected to standardize 

the way different biometric devices, such as fingerprint scanners and 

facial recognition devices, communicate with the application software 

that uses them. It is also expected to be a comprehensively modular 

architecture that covers a variety of hardware interfaces, encryption, 

biometric algorithms, and application interfaces.



* Established by the Joint Photographic Experts Group (JPEG), the JPEG 

specification can be used in facial recognition systems.[Footnote 20] 

It describes an image compression system that allows great flexibility 

not only for the compression of images but also for access to the 

compressed data. The specification is designed for compressing either 

full-color or gray-scale images of natural, real-world scenes, although 

the decompressed images are not quite the same as the originals. JPEGs 

algorithm is designed to exploit known limitations of the eye, notably 

that the eye perceives small color changes less accurately than small 

changes in brightness. This is a limitation if an application uses a 

JPEG image to machine-analyze images, since the small errors JPEG 

introduces may be a problem even if they are invisible to the 

eye.[Footnote 21]



* In February 2001, the American National Standards Institute (ANSI) 

approved the Biometric Information Management and Security (ANSI X9.84-

2001) standard. This standard specifies the minimum security 

requirements for effective management of biometric data. The standard 

defines message formats for carrying biometric data in a secure way and 

also defines many concepts and procedures for the creation of a secure 

biometric system. The message formats specified by X9.84 are more 

flexible than the BioAPI data format because they allow a richer 

description of the biometric data and are extensible. Moreover, the 

X9.84 standard addresses the issue of integrity and privacy of 

biometric samples and templates in a flexible way, by providing several 

different security mechanisms among which the user can choose.



* The American Association for Motor Vehicle Administration (AAMVA) 

included a format for fingerprint minutiae data in its Driver License 

and Identification (DL/ID-2000) Standard, which provides a uniform 

means to identify issuers and holders of drivers license cards within 

the United States and Canada. The standard describes required and 

optional data elements to be placed on a drivers license card. 

Required elements include the name, address, and photograph of the 

driver. While fingerprints are classified as an optional data element, 

the standard describes a way to record minutiae data based on the type, 

position, angle, and quality of the minutiae point. A field is also 

provided for recording vendor-specific data about the fingerprint. The 

biometric portions of this standard are compatible with the BioAPI 

specification and CBEFF.



Figure 12 shows the relationship of these standards to the individual 

functional components necessary to make up a comprehensive biometric 

system.



Figure 12: Standards for Biometric Systems:



Note: AAMVA = The American Association for Motor Vehicle 

Administrations Driver License and Identification (DL/ID-2000) 

Standard. WSQ = wavelet scalar quantization. JPEG = a specification of 

the Joint Photographic Experts Group. CBEFF = the National Institute of 

Standards and Technologys Common Biometric Exchange File Format. 

BioAPI = the BioAPI Consortiums BioAPI specification for a high-level 

generic biometric authentication model. BAPI = biometric application 

programming interface. X9.84 = the American National Standards 

Institutes ANSI X9.84-2001 standard.



Source: GAO analysis of biometric standards.



[See PDF for image]



[End of figure]



Although a number of such standards have been developed, those required 

for integrating all vendors products are not yet available for all 

types of applications. For example, the standard for how to store 

biometric templates is not yet available. While the AAMVA standard 

describes a common way to record fingerprint minutiae, it still allows 

for including data in a vendor-specific format. Biometric templates, 

which capture only the critical data needed to make a positive 

confirmation, are small and can be stored on smart cards, but the 

template one vendor uses cannot generally be used by another for some 

biometric technologies, such as fingerprints. Working with other 

groups--the Biometric Consortium, the BioAPI Consortium, the Biometric 

Foundation, and the International Biometric Industry Association 

(IBIA), among othersthe InterNational Committee for Information 

Technology Standards (INCITS) is reviewing draft project proposals for 

standardizing biometric templates[Footnote 22] Without a biometric 

template standard, it could be necessary to store the larger biometric 

sample as well as the biometric template for each user during 

enrollment. Such a standard would also allow for changes to the 

biometric capture device (i.e., a change in equipment) or algorithms 

without reenrolling all system users.



In November 2001, the executive board of INCITS established Technical 

Committee M1, Biometrics, for the rapid development and approval of 

formal national and international generic biometric standards. The goal 

of M1s work is to accelerate the deployment of significantly better, 

standards-based security solutions for purposes such as homeland 

defense and the prevention of identity theft, as well as other 

government and commercial applications based on biometric personal 

authentication. INCITS approved the BioAPI Specification, Version 1.1, 

as the ANSI/INCITS 358-2002--Information technology--BioAPI 

Specification, on February 13, 2002. It is now considering CBEFF for 

fast track processing in the near future. Additionally, M1 is now 

reviewing contributions of draft project proposals for the 

standardization of biometric templates. M1 is also anticipating 

contributions of draft project proposals for the development of 

application profiles and implementation profiles, as required for 

homeland defense applications, for example, as well as for financial 

services, health care, civil aviation, and the use of biometrics for 

preventing identity theft.



User Acceptance:



The overall success of biometric systems depends on how well people who 

use biometric systems accept them and how easy they are to use. If 

enrollment and matching procedures are too cumbersome, data-capture 

errors can lead to high error rates, including FMRs and FNMRs. 

Moreover, if people perceive a technology as being too intrusive, their 

lack of cooperation or even resistance can affect a systems 

performance. Privacy concerns may be a barrier to the widespread 

adoption of biometric technologies.



Some people find biometric technologies difficult, if not impossible, 

to use. Still others resist biometrics in general as intrusive, 

inherently offensive, or just uncomfortable to use. They consider it to 

be physically intrusive to have to pause and position themselves in 

relation to a capture device while presenting their biometric. Or they 

even consider being required to verify their identity through a 

hardware device rather than a human interaction to be too impersonal. 

Fingerprint systems, in particular, face even stronger opposition 

because of their association with criminal applications.



Some biometric devices also carry concerns about hygiene. For example, 

some people object to hand geometry scanners because they do not like 

to put their palms on the same surfaces where many other people have 

placed theirs. Other people fear that devices that scan particularly 

sensitive areas of the body, such as the eyes, will damage them. 

Generally, the less intrusive people perceive a biometric to be, the 

more readily they accept it.



Much public concern about biometrics arises from fears that the 

technology can be misused to invade or violate personal privacy. Among 

these fears are that biometric information will be:



* gathered without permission or knowledge or without explicitly 

defined purposes,



* used for a variety of purposes other than those for which it was 

originally acquired (sometimes called function creep),



* shared without explicit permission, or:



* used to track people across multiple databases to amalgamate 

information for the purpose of surveillance or social control.



Technologies Viable for U.S. Border Control:



No biometric technology is best for every situation, but it is possible 

to determine which technologies are more accurate and easier to deploy 

for border control applications. Last year, the International Civil 

Aviation Organization (ICAO) assessed fingerprint, facial, and iris 

recognition as the top three biometrics meeting the requirements for 

biometric identification in machine-readable travel documents. Table 8 

summarizes the performance characteristics of the four technologies 

that are most viable for border control. The performance factors such 

as error rates, template sizes, and transaction times can vary greatly, 

depending on whether the biometric technology is being used for 1:1 

verification or 1:N identification.



Table 8: Four Viable Biometric Technologies Compared:



Characteristic: False nonmatch rate (FNMR); Facial: 3.3-70%; 

Fingerprint: 0.2-36%; Iris: 1.9-6%; Hand: 0-5%.



Characteristic: False match rate (FMR); Facial: 0.3-5%; Fingerprint: 0-

8%; Iris: Less than 1%; Hand: 0-2.1%.



Characteristic: User acceptance issues; Facial: Potential for privacy 

misuse; Fingerprint: Associated with law enforcement; hygiene concerns; 

Iris: User resistance; usage difficulty; Hand: Hygiene concerns.



Characteristic: Enrollment time; Facial: About 3 minutes; Fingerprint: 

About 3 minutes 30 seconds; Iris: About 2 minutes 15 seconds; Hand: 

About 1 minute.



Characteristic: Transaction time; Facial: 10 seconds; Fingerprint: 9-19 

seconds; Iris: 12 seconds; Hand: 6-10 seconds.



Characteristic: Template size; Facial: 84-1,300 bytes; Fingerprint: 

250-1,000 bytes; Iris: 512 bytes; Hand: 9 bytes.



Characteristic: Number of major vendors; Facial: 2; Fingerprint: More 

than 25; Iris: 1; Hand: 1.



Characteristic: Cost of device; Facial: Moderate; Fingerprint: Low; 

Iris: High; Hand: Moderate.



Characteristic: Factors affecting performance; Facial: Lighting, 

orientation of face, or sunglasses; Fingerprint: Dirty, dry, or worn 

fingertips; Iris: Poor eyesight, glare, or reflections; Hand: Hand 

injuries, arthritis, or swelling.



Characteristic: Demonstrated vulnerability; Facial: Notebook computer 

with digital photo or false photographs; Fingerprint: Artificial 

fingers or reactivated latent prints; Iris: High-resolution picture of 

iris; Hand: None.



Characteristic: Variability with age; Facial: Affected by aging; 

Fingerprint: Stable; Iris: Stable; Hand: Stable.



Characteristic: Commercially available; Facial: 1990s; Fingerprint: 

1970s; Iris: 1997; Hand: 1970s.



Source: GAO analysis.



[End of table]



Recognizing that technology performance is least supported by 

substantive real-life test data, ICAO has asked its member states to 

perform scenario and operational evaluations with fingerprint, facial, 

and iris recognition technologies. It plans to evaluate the results of 

the testing and to select one or two biometric technologies for 

standardization in machine-readable travel documents.



Retina, speaker, and signature recognition have certain drawbacks that 

make them impractical for border control. Retina recognition is 

considered too intrusive because the systems require users to position 

their eyes very close to devices, which some users find very 

discomforting. Also, because using these systems requires prolonged 

effort and concentration, a high percentage of people are unable to 

enroll. Speaker recognition was piloted for border control use but has 

been found unreliable. In fact, this technology has several 

disadvantages. Speech quality is affected by a persons health, such as 

a cold or sore throat, stress, and emotions. In addition, speaker 

recognition systems do not perform well in noisy environments because 

surrounding noise interferes with their ability to extract the 

distinctive characteristics of an individuals speech. Moreover, 

because speaker recognition technologies have large templates, they 

require longer processing times and use more storage. Finally, the 

voice does not appear to be sufficiently distinctive to permit 

identifying one individual within a large database of identities. 

Signature recognition has a high FNMR because most people do not sign 

their names consistently. Since the resulting nonmatches would require 

many secondary inspections, signature recognition is probably not 

practical for border control. Moreover, travelers from some countries 

may not be accustomed to signing their names, to writing their names in 

roman letters, or to writing at all.



Facial Recognition Performance:



The two leading vendors of facial recognition technology have their own 

methods for analyzing a facial image and converting it to a digital 

template. Enrolling in a facial recognition system seems relatively 

easy. Results from Britains NPL product testing produced a 0 percent 

FTER. But the performance of facial recognition technology appears to 

depend on the operational setting and specific application. Pilots of 

facial recognition surveillance at airports have resulted in FMRs 

between 0.3 percent and 5 percent and FNMRs between 5 percent and 45 

percent. In a State Department Bureau of Consular Affairs test 

involving data sets of 10,000 to 100,000 images, fewer than 30 percent 

of intentionally seeded duplicate images were correctly matched--an 

FNMR of around 70 percent. Although facial recognition performs much 

worse than fingerprint and iris recognition, it remains attractive 

because facial images are used in a wide variety of identification 

documents.



The performance of facial recognition technology is affected greatly by 

environmental factors, especially lighting conditions. Variations in 

camera performance and facial position, expression, and features 

(hairstyle, eyeglasses, beards) further affect performance. Accurate 

image alignment is necessary for the leading facial recognition 

algorithms, which rely on identifying eye positions. One algorithm is 

rendered ineffective when a person tilts the head from a direct frontal 

pose to more than about 25 degrees horizontally or more than about 15 

degrees vertically.



Performance is also degraded significantly as the stored facial 

recognition template ages. When a match was attempted a year after 

initial enrollment, some facial recognition technologies correctly 

verified as little as 41 percent of the faces; this translates to an 

FNMR of 59 percent.



In tests conducted by the Federal Aviation Administration (FAA) from 

November 2001 through January 2002, the average enrollment time was 3 

minutes and 2 seconds. When the device was in use, the time increased 

by approximately 9.5 seconds to pass through a door.



Facial recognition systems can be quite costly. A facial recognition 

server controlling access at a facility with up to 30,000 persons would 

cost about $15,000. Depending on the number of entrances installed with 

facial recognition devices, the cost of software licenses would range 

from about $650 to $4,500. As the size of the database and the number 

of attempted matches increased, so would a systems cost. In addition 

to the server and software licenses, a live-scan facial recognition 

surveillance system includes closed-circuit television (CCTV) 

surveillance. A fully integrated CCTV system for physical access 

surveillance can cost from $10,000 to $200,000, depending on the size 

of the entrance and the degree of monitoring required. For additional 

CCTV equipment, cameras can cost between $125 and $500. Cameras with 

advanced features can cost up to $2,300.



Although users typically consider facial recognition technology less 

intrusive than other biometric technologies, some are concerned that it 

can track them without their consent. Successful attempts to spoof 

live-scan facial recognition systems would not work in a border 

inspection where a border inspector is monitoring the equipment. (See 

appendix IV for more details on facial recognition technology.):



Fingerprint Recognition Performance:



The majority of the leading vendors of fingerprint recognition 

technology sell scanners based on optical or silicon technology. The 

companies techniques for converting a fingerprint image to a digital 

template are proprietary. The basic performance of fingerprint 

recognition technology depends on the type of application and the type 

of scanner capturing the fingerprint image. For about 2 to 5 percent of 

people, fingerprints cannot be captured because they are dirty or have 

become dry or worn from age, extensive manual labor, or exposure to 

corrosive chemicals.



The time to enroll a person in a fingerprint recognition system depends 

on the number of fingerprints used and the details of the enrollment 

process. For example, in FAA testing, enrollment averaged 3 minutes and 

30 seconds. In contrast, in the first 7 months of the CANPASS-Airport 

pilot at Vancouver International Airport, roughly 1,000 travelers 

registered in an average of 15 minutes.



The time required to match a fingerprint and verify an individuals 

identity can vary from sensor to sensor and from one application to 

another. For example, in FAA testing, users took an average of about 10 

seconds to pass through the door, compared to an average of about 2 

seconds before the device was installed. NPL found that an optical 

fingerprint system had a mean transaction time of 9 seconds, while a 

silicon sensor system had a time of 19 seconds.



A fingerprint recognition device can typically be set for different 

security levels, with higher FMRs at lower levels of security. For 

example, the FBIs Integrated Automated Fingerprint Identification 

System (IAFIS) has a 1.5 x 10-12 FMR with an FNMR between 1.5 and 2 

percent. In contrast, FAA testing from September 2000 to February 2001 

produced FNMRs that ranged from about 6 percent to about 17 percent for 

closely controlled test subjects. For actual airport employees 

accessing the door in a less-controlled environment, the FNMR ranged 

from about 18 percent to about 36 percent. The FMR ranged from 0 

percent at the highest security level to about 8 percent at the lowest 

security level.



The cost of each fingerprint reader designed for physical access 

control ranges from about $1,000 to about $3,000. Software licenses are 

listed for about $4 per enrolled user. For smaller fingerprint 

scanners, maintenance is between 15 percent and 18 percent of cost. A 

larger live-scan 10-print fingerprint reader costs about $25,000. 

Maintenance of the larger machines is approximately 14 percent of the 

cost of the reader.



Because law enforcement agencies have used fingerprints to identify 

criminals, the technologys similarity to forensic fingerprinting 

causes some people discomfort. Privacy advocates fear that fingerprint 

recognition systems may collect data for one purpose but then use the 

data to track peoples private activities or for other purposes. Also, 

people may have hygiene issues with having to touch the plate of the 

scanner that many other people have touched.



The fingerprint recognition technologies have been shown to be 

susceptible to deception, but this can be prevented if fingerprints are 

scanned in a monitored environment. (See appendix II for more details 

on fingerprint recognition technology.):



Iris Recognition Performance:



The sole provider of iris recognition technology developed the first 

commercially viable system in 1997. Enrolling in an iris recognition 

system requires a person to gaze steadily at a camera for a short time. 

Some people find this difficult to do and therefore fail to enroll. The 

FTER in an NPL test was 0.5 percent. While iris technology does not 

require touching any device, some people resist the scanning of their 

eyes.



However, iris recognition technology has good performance 

characteristics. Testing at the U.S. Army Research Laboratory resulted 

in FMRs of less than 1 percent and an FNMR of 6 percent. In 1996, 

Sandia National Laboratories, testing a prototype iris recognition 

system, found that the FNMR was 10.2 percent and the average enrollment 

time was 2 minutes and 15 seconds. In a more recent test by NPL, the 

iris recognition system showed an FMR of 0 percent, FNMR of 0.2 

percent, and a mean transaction time of 12 seconds.



Colored or bifocal contact lenses can affect system performance, as can 

exceptionally strong glasses. Poor eyesight may also hinder some people 

from lining their eyes up with the camera. Glare and reflection can 

also cause interferences. People with glaucoma or cataracts may not be 

reliably identified by iris recognition systems.



Iris recognition systems cost approximately $2,000 for physical access 

units. The overall cost of a comprehensive iris recognition system 

would be much higher.



Certain iris recognition devices have been spoofed by holding up to the 

camera a high-resolution picture of an iris with a tiny hole cut out to 

allow the pupil of a live eye to shine through. Such deceptions could 

be prevented at a border inspection station monitored by inspectors. 

(See appendix V for more details on iris recognition technology.):



Hand Geometry Performance:



Hand geometry, in use for almost 30 years, is a relatively mature 

biometric technology with only one primary vendor. The shape and size 

of our hands are reasonably diverse but not highly distinctive. Thus, 

hand geometry is not suitable for identifying one individual among 

many. Because border control applications require checking for 

duplicate enrollment before travel documents are issued, hand geometry 

is not viable for that aspect of border control. However, hand geometry 

can be used to verify identity after performing the enrollment checks 

with a more distinctive biometric technology.



Typically, everyone with a hand can enroll in a system--FTER is 0 

percent. In FAA testing from March through July 2001, time for 

enrolling with a hand geometry device averaged 57 seconds. The FNMR for 

airport employees using the system ranged from approximately 5 percent 

at a high security-level setting to less than 1 percent at a low 

security-level setting. The FMR ranged from 0 percent at the high 

security-level setting to about 2 percent at the low security-level 

setting. The FAA test also found that using the hand geometry device 

increased the time to open a door by 6 seconds. However, an NPL test 

found a mean transaction time of 10 seconds for a hand geometry system. 

The performance of hand geometry technology is affected by jewelry, 

arthritis, water retention, and swelling from pregnancy or hand injury.



Hand geometry devices generally cost between $2,000 and $4,000. Staff 

training is minimal, with no personnel costs, since most hand geometry 

devices are unattended. It is considered easy to use, although a 

minimal amount of training may be required for individuals to learn to 

align their hands in the device. Hand geometry is generally perceived 

as not intrusive, not threatening, and not invasive, and it bears very 

little of the stigma of other biometric technologies. (See appendix III 

for more details on hand geometry technology.):



Biometric Technology Applied to Border Control Today:



Applying biometric technologies to customs and immigration in the 

United States and other nations is growing rapidly. Fingerprint, 

facial, and iris recognition and hand geometry systems are being 

planned or have been implemented to different degrees, ranging from 

piloted tests to operational usage. We summarize some of these projects 

and their applications, particularly to trusted air travel, land border 

crossing, obtaining and verifying travel documents, and surveillance.



Trusted Air Travel:



Trusted air travel programs permit frequent travelers to circumvent 

customs procedures and immigration lines. To participate, users undergo 

a background screening and registration. Once enrolled, they can 

present their biometric at an airport kiosk for comparison against a 

template stored either on a storage card in their possession or in a 

central database.



INSPASS, a pilot program in place since 1993, has more than 35,000 

frequent fliers enrolled at nine airports, with more than 250,000 

transactions every year. It is open to citizens of the United States, 

Canada, Bermuda, and visa waiver program countries who travel to the 

United States on business three or more times a year.



A hand recognition system similar to INSPASS at Ben Gurion Airport in 

Tel Aviv, Israel, since 1998 verifies international travelers and all 

Israeli citizens. By April 2002, more than 100,000 travelers had 

enrolled in the program, and the system was processing about 50,000 

passengers each month.



The Expedited Passenger Processing System (EPPS), based on iris 

recognition technology, is being launched at eight major international 

airports in Canada. Positive verification against the template at an 

airport kiosk entitles travelers to circumvent customs and immigration 

lines. The first kiosks are expected to be installed in Vancouver and 

Toronto airports in 2003.



In July 2001, frequent travelers on British Airways and Virgin Atlantic 

Airways transatlantic flights began clearing immigration through iris 

recognition verification at Londons Heathrow Airport. Once registered 

and enrolled, landing passengers can proceed directly to special lanes 

to verify their identity against an iris template stored in a central 

database. If successful, they are issued a ticket that admits them 

directly to the United Kingdom.



A program to expedite immigration processing for frequent travelers at 

Amsterdams Schiphol Airport, the Netherlands, is based on a 

combination of iris recognition and smart card technology. About 2,000 

smart cards have been issued to nationals from 18 different European 

countries.



Land Border Crossing:



In a joint INS and State Department effort to comply with the Illegal 

Immigration Reform and Immigrant Responsibility Act of 1996, every 

border crossing card issued after April 1, 1998, contains a biometric 

identifier and is machine-readable. The cards, also called laser visas, 

allow Mexican citizens to enter the United States without being issued 

further documentation for the purpose of business or pleasure and stay 

for 72 hours or less, going no farther than 25 miles from the border. 

If a Mexican citizen plans to stay for longer than 72 hours or to go 

more than 25 miles from the border, additional documentation is 

required. Consular staff in Mexico photograph applicants and take 

prints of the two index fingers and then electronically forward 

applicants data to INS. Both State and INS conduct checks on each 

applicant, and the fingerprints are compared with prints of previously 

enrolled individuals to ensure that the applicant is not applying for 

multiple cards under different names. The cards store a holders 

identifying information along with a digital image of his or her 

picture and the minutiae of the two index fingerprints. Figures 13 and 

14 show the front and back of the laser visa. As of May 2002, State had 

issued more than 5 million cards. However, INS has not yet deployed 

fingerprint readers or card readers, so inspectors examine cards at the 

points of entry as they would a travel document.



Figure 13: The Front of a Laser Visa:



Source: INS.



[See PDF for image]



[End of figure]



Figure 14: The Back of a Laser Visa:



Source: LaserCard Systems Corporation, Mountain View, California.



[See PDF for image]



[End of figure]



The government of Israel is implementing a biometrics system that uses 

hand and facial scans to facilitate passage through border checkpoints 

between the Gaza Strip and other areas of Israel. The system will 

verify the identity of 60,000 Palestinian workers who cross the border 

at 42 automated checkpoints daily. The workers biometrics will be 

compared with templates stored on a central server and backed up on 

smart cards that the workers can present.



An iris recognition system in Singapore processes motorbike passengers 

crossing the border from Malaysia each day to work. Approximately 

50,000 travelers cross this border each day.



Hong Kong plans to introduce a fingerprint scanning system in 2003 at 

the Shenzhen border in China to accelerate immigration for the 250,000 

people who cross the border every day. Travelers will be able to swipe 

a smart card bearing personal data along with a photograph and the 

template of a thumbprint through an optical reader while presenting the 

thumb to a scanner.



Obtaining and Verifying Travel Documents:



The Department of State has been running pilots of facial recognition 

technology at 23 overseas consular posts for several years. As a visa 

applicants information is entered into the local system at the posts 

and replicated in States CCD, the applicants photograph is compared 

with the photographs of previous applicants stored in CCD to prevent 

fraudulent attempts to obtain visas. Some photographs are also being 

compared to a watch list.



Australias Sydney Airport is assessing facial recognition technologies 

in one-to-one comparisons of individuals facial features with their 

passport pictures to identify people traveling with false passports.



Saudi Arabia installed iris scanning and fingerprinting devices in the 

King Abdul Aziz Airport in the Red Sea port city of Jeddah during this 

years annual Hajj pilgrimage to Mecca to verify that individuals 

entered and exited the country under the same travel documents.



Surveillance:



Sydney Airport in Australia is using facial recognition technology to 

identify wanted faces within the airports crowds. Icelands main 

international airport at Keflavik scans passengers with facial 

recognition technology as they pass through boarding gates, comparing 

their facial characteristics with a watch list of suspected terrorists 

and criminals.



[End of section]



Chapter 4 Scenarios for Border Control with Biometrics:



In the previous chapter, we described how biometric technologies work, 

their performance, and some of their applications. In this chapter, we 

outline how fingerprint, facial, and iris recognition technologies 

could help improve the procedures now used to secure U.S. borders. We 

identify four possible scenarios:



* Making a watch list check before issuing travel documents.



* Making a watch list check before travelers enter the United States.



* Issuing U.S. visas with one or more of these biometrics.



* Issuing U.S. passports with one or more of these biometrics.



These scenarios do not represent all the ways to use biometrics for 

border control, but they do reflect some elements of pilots that have 

implemented biometric technologies for border control, as well as 

options discussed in legislation and by agencies responsible for border 

security. While hand geometry cannot be used for conducting a watch 

list check, it can be used in conjunction with one of the other 

technologies to verify identities using visas or passports.



The first two scenarios could help identify individuals who are 

ineligible to receive a U.S. visa or passport or who cannot be admitted 

to the United States. Both of these scenarios use an identification 

match to compare the travelers biometric against a database of stored 

biometrics. The two other scenarios could help link an individuals 

identity to U.S. travel documents and could help reduce document 

counterfeiting and impostors fraudulent use of legitimate documents. 

The four scenarios are not mutually exclusive; they could be 

implemented individually or in combination. In the next chapter, we 

analyze costs, benefits, and implications associated with implementing 

the scenarios.



Watch List Check before Issuing Travel Documents:



Making a watch list check before issuing travel documents could 

identify individuals ineligible to receive a U.S. visa or passport when 

their biometric was compared during the application process against a 

database of the biometrics of individuals on a watch list. This 

scenario would have the least effect on current operations and would 

require the least development of new systems. As depicted in figure 15 

for visas and figure 16 for passports, the watch list check would 

essentially be an additional computer check conducted much as the name 

check that is conducted through CLASS today.



Figure 15: Issuing U.S. Visas by a Watch List Check Process:



Source: GAO analysis.



[See PDF for image]



[End of figure]



Figure 16: Issuing U.S. Passports by a Watch List Check Process:



Source: GAO analysis.



[See PDF for image]



[End of figure]



Policies for the contents of the watch list would have to be developed, 

including criteria for names to place on the watch list--whether those 

of terrorists, criminals, violators of immigration law, or others. The 

biometric technology would most likely be based on facial recognition 

from photographs that applicants for documents submit. Often, a 

photograph is the only biometric available for certain people who are 

not admissible to the United States. Criteria for the quality of the 

stored biometric for those on the watch list would probably have to be 

developed in order to enhance the performance of the matching process.



Implementing this scenario would probably require two additional 

computer system units to house the watch list and to match applicants 

photographs and the photographs on the watch list. Figure 17 depicts 

one possible construct for this scenarios architecture. Existing 

systems, such as CCD and Passport Files Miniaturization (PFM), could 

require significant changes and corresponding time and resources to 

accommodate this scenario.



Figure 17: System Architecture for a Biometric Watch List Check before 

Issuing Travel Documents:



Source: GAO analysis.



[See PDF for image]



[End of figure]



Depending on the watch list criteria, it might be possible to use 

fingerprint or iris recognition to perform the match. Using 

fingerprints or the iris as the watch list biometric would complicate 

data collection. Instead of just submitting a photograph, applicants 

would have to submit fingerprint or iris biometrics. This information 

would then have to be stored centrally and read by readers installed at 

embassies, consulates, and passport acceptance offices.



Watch List Check before Entering the United States:



Individuals who are not eligible to enter the United States could be 

identified before they could enter if, during inspection, their 

biometrics are checked against a database of the biometrics of people 

on a watch list. As depicted in figure 18, this watch list check--which 

would be similar to the IBIS check at ports of entry--would be an 

additional computer check conducted during inspection.



Figure 18: Entering the United States by a Watch List Check Process:



Source: GAO analysis.



[See PDF for image]



[End of figure]



As with the watch list scenario for issuing travel documents, policies 

would have to be developed for a watch list for entering the country, 

including the lists contents and the quality of the stored biometric. 

Facial recognition based on images collected as travelers presented 

themselves before INS inspectors would be the likely biometric 

technology. Often, a photograph is the only biometric available for 

certain people not admissible to the United States.



As with the scenario we described above, a database to store the watch 

list would have to be developed. The primary difference in cost between 

these two scenarios would be the cost of biometric readers for the 

ports of entry and the corresponding infrastructure and personnel to 

use the readers. The readers would require access to the database of 

the biometrics of the individuals on the watch list. Figure 19 depicts 

one possible construct for this scenarios architecture. Existing 

systems, such as IBIS and the Treasury Enforcement Communications 

System (TECS), could require significant changes and corresponding time 

and resources to accommodate this scenario.



Figure 19: System Architecture for a Biometric Watch List Check before 

Entering the Country:



Source: GAO analysis.



[See PDF for image]



[End of figure]



U.S. Visas with Biometrics:



In a scenario in which U.S. visas contained biometrics, two of the 

border control processes would be affected. First, applicants for U.S. 

visas would submit a biometric with their applications at American 

embassies and consulates. During the application process, the 

applicants biometric data would be stored and an identification match 

would be conducted to compare the biometric information stored from 

other issued visas, as well as rejected visa applications, to check for 

duplicate and fraudulent applications. Second, at the ports of entry, 

the travelers biometric would be verified as a part of the inspection 

process. The verification match compares the biometric data collected 

during the visa application process with the data collected during the 

inspection process.



Figure 20 shows how collecting the biometric would change current visa 

issuing procedures and the additional computer check necessary to 

determine whether the new biometric had been previously enrolled. 

Figure 21 shows how port of entry inspection would change--essentially 

by adding a computer check to confirm travelers identities.



Figure 20: Issuing U.S. Visas with Biometrics:



Source: GAO analysis.



[See PDF for image]



[End of figure]



Figure 21: Port of Entry Visa Inspection with Biometrics:



Source: GAO analysis.



[See PDF for image]



[End of figure]



This scenario would require buying biometric readers for the embassies, 

consulates, and ports of entry. A database would be required for 

storing biometric information. This database could be integrated with 

CCD, which stores visa application and issuance information. To 

properly link a biometric with an individual, live capture of the 

biometric would be required, eliminating some, if not all, of the 

benefit of mail-in and drop-box visa applications. Figure 22 shows one 

possible construct for this scenarios architecture. Fingerprint, 

facial, or iris recognition could be used for this scenario. Hand 

geometry can be used only in combination with another technology 

because it is not effective in identification matches. Existing 

systems, such as IBIS, TECS, and CCD, could require significant changes 

and corresponding time and resources.



Figure 22: System Architecture for Issuing Visas with Biometrics:



Source: GAO analysis.



U.S. Passports with Biometrics:



[See PDF for image]



[End of figure]



Two border control processes would be affected also in a scenario that 

issued U.S. passports containing biometrics. First, passport applicants 

would submit a biometric with their applications. The applicants 

biometric data would be stored and an identification match would be 

conducted to compare the biometric information stored from other issued 

passports, as well as rejected passport applications, to check for 

duplicate and fraudulent applications. Second, the travelers biometric 

would be verified during inspection at ports of entry. The verification 

match compares the biometric data from the passport application with 

the data collected during inspection. Figure 23 shows the biometric 

collection and the computer check to determine whether travelers 

biometrics had been previously enrolled. Figure 24 shows the port of 

entry inspection, essentially adding a computer check to confirm 

travelers identities.



Figure 23: Issuing U.S. Passports with Biometrics:



Source: GAO analysis.



[See PDF for image]



[End of figure]



Figure 24: Port of Entry Passport Inspection with Biometrics:



Source: GAO analysis.



[See PDF for image]



[End of figure]



This scenario would require purchasing biometric readers for passport 

acceptance offices and ports of entry. It would require the database 

for storing biometric information. The database could be integrated 

with the State Departments Passport Records Imaging System Management 

(PRISM) and PFM, which store passport application and issuance 

information. To properly link biometrics with individuals, live capture 

of biometrics would be required, and this might eliminate some of the 

benefits of mail-in renewal applications. Figure 25 shows one possible 

construct for this scenarios architecture. Fingerprint, facial, or 

iris recognition could be used for this scenario. Hand geometry can be 

used only in combination with one of the other technologies because it 

is not effective in performing identification matches. Existing 

systems, such as IBIS, TECS, and PFM, could require significant changes 

and corresponding time and resources to accommodate this scenario.



Figure 25: System Architecture for Issuing Passports with Biometrics:



Source: GAO analysis.



[See PDF for image]



[End of figure]



Implementing Multiple Scenarios:



Two or more of these scenarios could be implemented in combination. 

Implementing scenarios in combination would not necessarily mean that 

costs would be additive. For example, the same biometric readers could 

be used to read biometrics from visa holders and passport holders at 

U.S. ports of entry. Similarly, the same watch list database could be 

used for checking before issuing travel documents and for checking 

before allowing entry into the United States.



It would also be possible to implement multiple biometric technologies. 

For example, it might be desirable for performance reasons to have both 

facial and fingerprint biometrics captured on visas so that either or 

both could be verified when people seek entry to the United States. It 

might be possible to integrate the match algorithms so that they take 

in results from both biometric readers and use them in combination to 

determine matches. The incremental costs associated with the additional 

biometric readers would have to be considered, as well as the costs of 

any additional labor and space required in order to capture the 

biometrics and any additional server capacity to store the additional 

biometrics. We discuss costs in the next chapter.



[End of section]



Chapter 5: Applying Biometrics to Border Control: Challenges and 

Implications:



While biometric technology is currently available and used in a variety 

of applications, questions remain regarding the technical and 

operational effectiveness of biometric technologies in applications as 

large as border control. In addition, before implementing any biometric 

border security system, a number of other issues would have to be 

considered, including:



* The systems effect on existing border control procedures and people. 

Technology is only part of an overall security solution and only as 

effective as the procedures within which it operates.



* The costs and benefits of the system, including secondary costs 

resulting from changes in processes or personnel to accommodate the 

biometrics.



* The systems effect on privacy, convenience, and the economy.



In this chapter, we present our analysis of the costs and benefits of 

the four scenarios as they could be applied to current border control 

procedures.



The Performance of Biometric Technologies:



Ideally, a biometric should be universally present, unique to the 

individual, and stable over time. The cost and ease of using a 

biometric technology also weigh into its selection. Of the four 

biometrics we examine in depth for border control, only a persons face 

is universally present, while other biometrics are not--people can lose 

or damage fingers, hands, and eyes. Estimates are that 1 to 3 percent 

of the population might be physically unable to use these biometrics.



Hand geometry and fingerprint, facial, and iris recognition have not 

been formally proven unique. Therefore, a biometrics uniqueness within 

a large population can be established only by its historical use. Table 

9 shows the sizes of some of the larger biometric systems.



Table 9: The Enrollment Size of Seven Operational Biometric Systems:



Biometric database: Mexican Federal Electoral Institute; Technology: 

Facial recognition; Enrollment: 60,000,000.



Biometric database: Integrated Automated Fingerprint Identification 

system; Technology: Fingerprint; Enrollment: 40,000,000.



Biometric database: INS Automated Biometric Fingerprint Identification 

System; Technology: Fingerprint; Enrollment: 4,500,000.



Biometric database: Ben Gurion International Airport; Technology: Hand 

geometry; Enrollment: 100,000.



Biometric database: INS Passenger Accelerated Service System; 

Technology: Hand geometry; Enrollment: 35,000.



Biometric database: King Abdul Aziz Airport, Saudi Arabia; Technology: 

Iris recognition; Enrollment: 30,000.



Biometric database: Schiphol Airport, Amsterdam; Technology: Iris 

recognition; Enrollment: 2,000.



Source: GAO analysis.

:



[End of table]



As table 10 shows, the sizes of the biometric systems specified in the 

four scenarios are large. The system required to issue visas with 

biometrics far exceeds in size the largest biometric database created 

so far. While fingerprint and facial recognition have been used in 

large systems, the size of each scenario far exceeds the largest iris 

recognition system of 30,000. As we have previously described, hand 

geometry is not highly distinctive and therefore cannot be used for 

border control where identification of one individual among many will 

be required. To be used for verification, hand geometry will need to be 

used in combination with one of the other technologies that can perform 

the initial identification match.



Table 10: Estimated Number of Biometric Matching Transactions in Four 

Border Control Scenarios:



Scenario: 1. Making a watch list check before issuing travel documents; 

System size: Depends on criteria used to develop the watch list: CLASS 

has about 10 million records for foreigners and U.S. citizens; Matching 

transactions per year: 17 to 31 million applications; * 10 million 
visas;

* 7 million passports; * possibly 14 million visas from visa waiver 

countries.



Scenario: 2. Making a watch list check before travelers enter the 

United States; System size: Depends on criteria used to develop the 

watch list: CLASS has about 10 million records; Matching transactions

per year: 500 million primary inspections.



Scenario: 3. Issuing U.S. visas with biometrics; System size: 100 

million to 240 million visa records over 10 years; Matching 

transactions per year: 48 to 63 million.



Scenario: 4. Issuing U.S. passports with biometrics; System size: 70 

million passport records over 10 years; Matching transactions

per year: Up to 175 million[A].



[A] About 175 million U.S. citizens were inspected at ports of entry in 

fiscal year 2001. Because a passport is not required in returning from 

countries such as Canada and Mexico, it is not clear how many of these 

citizens had passports.



Source: GAO analysis.

:



[End of table]



In testing and operation, some fingerprint and iris recognition 

technologies have proven fairly accurate. Fingerprint recognition has 

achieved a low FMR but a variable FNMR. According to the FBI, the FMR 

for IAFIS is about 1.5 x 10-12 with an FNMR of between 1.5 and 2.0 

percent. In testing NPL conducted, at an FMR of about 2 percent, the 

FNMR was about 4.3 percent. In pilots FAA sponsored, FNMR ranged from 6 

percent to 36 percent and the FMR was between 0 percent and 8 percent.



Iris recognition has also shown it can achieve a low FMR but with a 

variable FNMR. In NPLs testing, the FMR was 0 percent with an FNMR of 

1.9 percent. The U.S. Army Research Laboratory found an FMR below 1 

percent with an FNMR of 6 percent. Sandia National Laboratories test 

showed 0 percent FMR and 10.2 percent FNMR.



Facial recognition has had more mixed results. In verification testing 

NPL conducted, at an FMR of about 1 percent, the FNMR was about 3.3 

percent. In a pilot FAA sponsored, an FMR of 0.19 percent was achieved 

with an FNMR between 3 percent and 26 percent. In preliminary testing 

NIST conducted this year, facial recognition achieved an FMR of 1 

percent and an FNMR of 25 percent. For identification testing, facial 

recognition fared worse. A State Department pilot encountered an FMR of 

15 percent. Tests conducted at U.S. airports have found FMRs between 1 

and 5 percent and FNMRs between 5 and 15 percent. At one airport where 

the objective was to achieve an FMR as close to 0 as possible, an FMR 

of 0.3 percent was achieved but with an FNMR of 45 percent. The U.S. 

Army Research Laboratory found an FMR of 49 percent.



The final primary factor to consider when evaluating biometrics is 

stability over time, but little work has been done to establish this. 

Fingerprints are believed to be persistent from birth throughout life. 

It is believed that irises are stable from before birth until death. 

FRVT 2000 tested facial recognition with images collected a year before 

identification or verification. The FMR for verification was 44 to 59 

percent, while for identification it was 52 to 69 percent.



Fingerprint recognition appears to be the most mature of these 

biometric technologies. Fingerprint recognition has been used the 

longest and has been used with databases containing up to 40 million 

entries. Iris recognition is young and has not been used with 

populations approaching the size entailed in border control. While 

facial recognition has also been used with large databases, its 

accuracy results in testing have lagged behind those of iris and 

fingerprint recognition. IBG believes that further research, costing 

between $50 million and $100 million, would be required to determine 

whether iris or facial recognition could perform at the same level as 

fingerprint recognition.



How Introducing the Technology Affects People and Procedures:



The success of any border security technology depends on the border 

control procedures as well as the people engaged in those procedures. 

Technology is not a solution in isolation. Technology and people must 

work together to execute the border control process--from issuing 

travel documents to inspecting them at official ports of entry.



Introducing biometrics would affect people and processes differently, 

depending on the specific scenario. Further, the performance of the 

biometric technology can also affect the overall process. To check a 

watch list before issuing travel documents, the following would need to 

be considered:



* Installation of readers at consulates and embassies for visa 

operations and at passport acceptance offices for passport operations 

would require hiring additional staff and, in some cases, leasing 

additional space.



* While the watch list identification check is essentially just an 

additional computer check, high FMRs could increase the work of 

consular officers and passport examiners and could delay the 

disposition of applications if significant time were required to 

reconcile false hits.



* Consular staff, passport acceptance agents, and passport examiners 

would have to be trained.



* Mail-in and drop box applications could be expected to fall off 

considerably, if not completely.



Similar concerns would need to be addressed to check a biometric watch 

list before travelers enter the country.



* Installing readers at ports of entry would require hiring additional 

staff and, in some cases, leasing additional space.



* Because the watch list identification check is essentially just an 

additional computer check, similar to an IBIS check, hits would 

probably result in secondary inspection of the traveler. High FMRs 

could increase the work of inspectors and delay the passage of 

travelers if significant time were required to reconcile false hits.



* Inspectors would have to be trained to collect the biometric from 

travelers and to resolve watch list hits in secondary inspection. An 

outreach campaign would likely be necessary to educate travelers about 

the new biometric program.



One key impact, the increased time required to conduct an inspection 

with a biometric watch list, would result from three key factors. 

First, to check all identities through IBIS using a biometric watch 

list would be a more substantive security check that would lengthen 

primary inspection. As we have previously described, not all travelers 

are now subjected to an IBIS name check. Second, while some have 

suggested that biometrics could speed inspection, FAA tests suggest 

biometrics would slow it down. FAA tests with biometric technology in a 

physical access environment showed that transit time increased by 6 to 

9 seconds when biometrics were added to a magnetic card entry system. 

Third, an FMR that is too high could lead to excessive referrals of 

travelers to secondary inspection and could increase workload to 

resolve the false matches. For example, using facial recognition with a 

watch list of 10 million people and just a 1 percent FMR would result 

in an average of 100,000 false matches per traveler. Clearly, if the 

watch list will be large, the FMR will need to be extremely low to 

maintain workload at a manageable level.



For both watch list scenarios, policies and procedures would have to be 

developed for adding and maintaining records in the watch list 

database. Key questions that have to be answered for a watch list 

database include who is added to the watch list, how someone is removed 

from the watch list, and how errors can be corrected. One of the 

biggest issues would be the selection of a biometric to identify 

individuals on the watch list. Todays watch lists are primarily name-

based and frequently list only the individuals name, approximate age, 

suspected nationality, or other identifying data. The selection could 

be affected by who will be placed into the watch list because biometric 

information for some people is not available. Facial recognition could 

be the likely biometric technology for a watch list because often only 

photographs are available for certain people inadmissible to the United 

States. However, fingerprint recognition or iris recognition could also 

be used if the United States could collect records on those 

individuals.



To issue and verify visas with biometrics, changes would be required at 

embassies and consulates to issue the visas and at ports of entry to 

verify the identities of those traveling with visas. Specifically, the 

following would need to be considered:



* Installing readers at consulates and embassies for visa operations 

would require hiring additional staff and, in some cases, leasing 

additional space.



* While the biometric identification check for duplicate or rejected 

applications is essentially just an additional computer check, high 

FMRs could increase the work of consular officers and delay the 

disposition of visa applications if significant time were required to 

reconcile false hits.



* Consular staff would have to be trained.



* Mail-in and drop box applications could be expected to fall off 

considerably, if not completely.



Similarly, to issue and verify passports with biometrics, passport 

acceptance office operations could be dramatically modified. Because 

the vast majority of these offices are not State Department offices and 

do not have State Department personnel or equipment, policy decisions 

would have to be made regarding the installation of computers and 

biometric equipment at these offices. Specifically, the following would 

need to be considered:



* Installing readers at passport acceptance offices would require 

hiring additional staff and, in some cases, leasing additional space.



* Because there is not a State Department presence at passport 

acceptance offices, a mechanism would need to be developed to transmit 

the collected biometrics on removable media or through a network 

connection to the department.



* While the biometric identification check for duplicate or rejected 

applications is essentially just an additional computer check, high 

FMRs could increase the work of passport examiners and could delay the 

disposition of passport applications if significant time were required 

to reconcile false hits.



* Passport acceptance agents and passport examiners would have to be 

trained.



* Mail-in applications could be expected to fall off considerably, if 

not completely.



As we previously described for the use of a biometric watch list at the 

ports of entry, the use of biometrics with visas or passports would 

likely lengthen the inspection time. Although the matching operation 

conducted with visas or passports with biometrics would be a 

verification match instead of an identification match, the inspection 

time could still go up for the same reasons. Checking that the bearer 

of a travel document is the proper bearer of the document is a more 

stringent check than is conducted today. Further, the performance of 

the biometric technology affects the number of secondary inspections 

conducted if travelers are not properly matched to their biometric. 

Other issues that would need to be considered include:



* Installing readers at ports of entry would require hiring additional 

staff and, in some cases, leasing additional space.



* Because the biometric verification check is essentially just an 

additional computer check, similar to an IBIS check, hits would 

probably result in secondary inspection of the traveler. An FMR that is 

too high could lead to inadmissible people being allowed to enter the 

country. An FNMR that is too high could lead to an increase in the 

number of travelers referred to secondary inspection, adding to 

requirements for space and personnel.



* Inspectors would have to be trained to collect the biometric from 

travelers and to resolve watch list hits in secondary inspection. An 

outreach campaign would likely be necessary to educate travelers about 

the new biometric program.



The biometrics for visas and passports could be stored and verified 

with or without tokens. Biometric data could be stored on tokens 

travelers carried, to be compared with data from biometric readers at 

ports of entry. A token could be a travelers visa or passport with the 

biometric data stored on it as a bar code, or it could be a separate 

memory storage card, such as a smart card or laser card.



In an approach without tokens, a travelers biometric data would be 

stored in a central database to be queried during matching. The data in 

the central database could be indexed by the visa or passport number or 

simply by the travelers name combined with other identifying 

information such as date of birth, Social Security number, or drivers 

license number.



Regardless of the comparison method for verification, the enrollment 

process would be the same, whether at a consulate, embassy, or passport 

acceptance office. It is critical that the biometric, once collected, 

be securely linked to the visa or passport application and stored in a 

central database for comparison to other records, ensuring that 

duplicate identities are not being created. The operational concepts 

are:



* Check against token containing biometric data. The traveler enters a 

primary inspection area and presents to the inspector a token 

containing his or her biometric data. The token is read and the 

biometric data are decrypted and validated. The travelers stored 

biometric data and the biometric data obtained from the biometric 

reader are compared. If the data match, and if the inspector has no 

other reason to deny admission, then the traveler is admitted to the 

United States.



* Check against central database for biometric data. The traveler 

enters a primary inspection area and presents to the inspector a travel 

document or some other identifying information. Lacking a visa or 

passport, the traveler must provide information detailed such that a 

single record can be pulled from the central database. The remaining 

steps are the same as in checking a biometric token.



Process flow issues must be considered. A central database of 

biometrics would be required to prevent people from getting multiple 

passports or visas under different identities and for verifying the 

identity of a traveler whose token has been lost or stolen or becomes 

unusable. In this case, it is important that the traveler be able to 

provide enough information so that the inspector can check for and find 

the appropriate records. It is also possible that an identification 

match, instead of a verification match, could be run on an individual.



If a token is used, how it is produced must be considered. If it is to 

be a modification of the current passport or visa--for example, if the 

biometric is a two-dimensional bar code stored on the travel document-

-redesigning the passport or visa foil would be required. If the token 

is to be a separate card, such as a smart card or a laser card, the 

capital investment in a production facility would have to be 

considered.



Using tokens for the biometric storage could affect the inspection 

process. No studies have yet determined whether tokens expedite 

inspection. Studies should be conducted to determine what effect local 

data comparisons would have, compared with central database lookups.



For the three scenarios with biometric scanners at ports of entry, the 

physical configuration at the ports of entry could pose a challenge for 

collecting travelers biometrics and performing matches. Where there 

are terminals, such as at airports, some seaports, and pedestrian ports 

of entry, it would be relatively simple to install biometric readers 

and to read travelers biometrics. An inspector checks travelers 

identities and names at booths equipped with IBIS. At most sea ports of 

entry, where IBIS is not used, inspectors board the vessels to conduct 

inspections of aliens, while U.S. citizens are inspected as they 

disembark. Biometric readers could not be installed in such 

circumstances, making collecting biometrics from travelers 

challenging. Similarly, at land ports of entry, a way to collect 

biometrics expeditiously from all occupants of a vehicle would have to 

be developed.



For all four scenarios, exception processing would have to be carefully 

planned. When an applicant fails to enroll in a biometric system or 

when a system fails to correctly identify a person, that person must be 

treated as an exception. Exception processing that is not as good as 

biometric-based primary processing could be exploited as a security 

hole. Exceptions include passport and visa applicants whose biometrics 

cannot be properly enrolled in the system because they may not have the 

physiological characteristic that the system recognizes. One solution 

might be to use two or more biometric technologies in the same system, 

reducing the number of people who could fail to be enrolled.



The failure of biometric scanners, failure to access the central 

biometric database, failure to access the watch list, and 

communications failure are other exceptions. Because it is unlikely 

that inspections would cease, appropriate contingency plans would have 

to be developed to ensure continuity of operations without sacrificing 

security. Further, an appropriate transition strategy will be required 

to handle simultaneously biometric travel documents and the current 

travel documents that will remain valid without biometrics for the next 

10 years.



Biometrics and Information Security:



Just as operational processes must be considered, infrastructure 

processes must also be examined, particularly with respect to 

information security. Binding an identity to the biometric features of 

a person is only an entry in a database. Lax information security can 

weaken or break that bond. Laws enacted over the past 15 years require 

each federal agency to provide security protections for information 

collected and maintained by or for the agency commensurate with the 

risk and magnitude of harm that would result from unauthorized 

disclosure, disruption, modification, or destruction of the 

information.[Footnote 23]



Despite these statutory requirements, we have previously reported that 

poor information security is a widespread federal problem with 

potentially devastating consequences.[Footnote 24] Although agencies 

have taken steps to redesign and strengthen their information system 

security programs, our analyses of information security at major 

federal agencies have shown that federal systems were not being 

adequately protected from computer-based threats, even though these 

systems process, store, and transmit enormous amounts of sensitive data 

and are indispensable to many federal agency operations. These 

weaknesses continue, as indicated by our analyses of 24 large federal 

agencies that considered the results of inspector general reports and 

our reports published between July 2000 and September 2001.[Footnote 

25]



The security challenges directly affect the ability to implement 

existing laws and policies for protecting personal, proprietary, law 

enforcement, and national security information. Such safeguards require 

the appropriate tools to maintain confidentiality and ensure only 

authorized access, sharing, and use. Without appropriate security 

tools, the protection of this information will be at risk.



The information security challenges involved with a biometric system 

deal with the protection of biometric data--whether they are a 

biometric watch list or biometric reference templates stored in a 

central database or on a token--and the transmission of those data. 

Table 11 gives examples of operational issues, risks, and techniques 

related to binding individuals to their biometric information when 

issuing them visas or passports with biometrics. The binding process 

between a user and biometric information is critical to the success of 

a biometric-based user-authentication system. A process that does not 

have strong binding mechanisms will provide little improvement over 

existing processes.



Table 11: Security Risks and Mitigating Techniques:



Event: Unauthorized changes are made to data in the central database--

e.g., the biometric data associated with S. Smith is changed so she can 

claim an identity such as A. Smith; Adverse effect: The binding of a 

person to her biometric data is lost--in effect, she can assume 

multiple identities; Mitigating technique: Electronic signatures can 

ensure data integrity; system can periodically check to ensure that the 

data and associated signature still agree. Original data can be 

restored from a secure backup when a modification is detected.[A].



Event: Unauthorized changes are made to a tokens data--e.g., biometric 

data originally stored with a given identity are replaced with 

biometric data associated with an impostor; Adverse effect: The 

binding of a person to his or her biometric data is lost--in effect, he 

or she can claim the identity of another person or assume multiple 

identities, using the same token; Mitigating technique: Electronic 

signatures generated by a central database at enrollment can ensure a 

tokens data integrity. The data can be changed but changes would be 

detected, since the system would not validate the electronic signature 

generated during enrollment with the original data.



Event: A rogue government official generates a false identity for a 

person with the correct biometrics but altered name or birth date to 

bypass the systems checks for detecting suspicious individuals; 

Adverse effect: The binding of the person to her biometrics is not 

compromised, but the system cannot ensure that travel documents are 

issued only to an authorized person; Mitigating technique: Split 

knowledge and dual control techniques can ensure that at least two 

persons validate the identity data provided to the system. Also, once 

identified, the electronic signature of the official who authorized the 

token can easily be revoked.



Event: Biometric data on a token or in a database are compromised by 

unauthorized disclosure; Adverse effect: Since the public may believe 

that biometric data are as confidential as a Social Security number, 

their unauthorized disclosure may lead to identity theft and a public 

relations problem; Mitigating technique: A tokens biometric data can 

be encrypted to ensure that its loss or theft does not compromise the 

data. Although encrypting the database might make searching for 

duplicate values unrealistic, other controls can reasonably limit 

access to biometric images to authorized persons and processes.



[A] Electronic signatures are commonly used to provide assurances that 

unauthorized changes are not made to data. They may also represent an 

individual or an entity. A system-generated electronic signature should 

be (1) unique to the signer, (2) under the signers sole control, (3) 

verifiable, and (4) linked to the data in a way such that if the data 

are changed, the signature is invalidated on verification. See U.S. 

General Accounting Office, Information Security: Advances and Remaining 

Challenges to Adoption of Public Key Infrastructure Technology, GAO-01-

277 (Washington, D.C.: February 26, 2001).



Source: GAO analysis.



[End of table]



Weighing Costs and Benefits:



Before any significant project investment is made, the benefit and cost 

information should be analyzed and assessed in detail. A business case 

should be developed that identifies the organizational needs for the 

project. A clear statement of high-level system goals should drive the 

overall concept of a U.S. border control system. Every aspect of the 

overall system--from the selection of biometrics to the system 

architecture--depends on the overall system goals. The high-level goals 

should address the systems expected outcomes--for example,



* binding a biometric feature to an identity (information such as name, 

date of birth, place of birth) shown on a travel document,



* identifying undesirable individuals on a watch list,



* checking for duplicate enrollments,



* verifying identities at the borders,



* ensuring the adequacy of privacy protections, and:



* ensuring the security of the biometric information.



Certain performance parameters should also be carefully specified, 

including, among others, the:



* time required to enroll people,



* time required to verify each persons identity by comparing the 

biometrics against a stored template,;



* acceptable overall FMR and FNMR, and:



* maximum population the system must handle.



Similarly, not only must the costs of the technology be considered but 

also the costs of the effects on people and processes. A biometric-

based border control system is bound to require significant up-front 

investments and a certain level of recurring costs to keep it 

operating. Weighed against these costs are the security benefits that 

accrue from using the system. Analyzing this cost-benefit trade-off is 

crucial when choosing specific biometrics-based border control 

solutions. The consequences of performance issues--for example, 

accuracy problems, their effect on processes and people, and their 

costs--are also important in selecting a biometric technology.



The desired benefit of all the scenarios we describe is to prevent the 

entry of travelers who are inadmissible into the United States. More 

specifically, in both watch list scenarios, a biometric check could 

improve security by adding a watch list check to the name-based watch 

list checks already being performed. A biometric watch list could help 

detect travelers who are trying to evade detection and who have 

successfully established a separate name and identity. Biometrics that 

are unique to these individuals should identify them in biometric 

checks against the entries in the watch list. A biometric watch list 

could help detect certain travelers, even when a name or other 

biographical information about an individual on a watch list is 

unknown.



The quantitative benefit of the watch list scenarios (i.e., the number 

of travelers prevented from obtaining U.S. travel documents or denied 

access to the United States) would depend on the performance of the 

biometric technology, the quality of the biometrics in the watch list, 

and the data in the watch list. As we have described, the performance 

of the biometric technology will determine the additional number of 

people apprehended as well as the additional number of people 

identified incorrectly. The performance of the biometric technology is 

also dependent on the size of the biometric watch list. As more people 

are added to the watch list, the probability of a false match for any 

given traveler increases. While apprehending more people increases 

security, further questioning people identified incorrectly increases 

the operational costs of implementing the technology. The better the 

quality of the biometric in the watch list, the more likely it is that 

the technology will correctly match a traveler to it. Finally, if 

effective policies and procedures are not implemented to populate the 

watch list, the systems effectiveness will not be as great as it could 

be.



For issuing passports and visas with biometrics, the key benefit is to 

positively identify travelers as they enter the United States and to 

cut down on the use of fraudulent travel documents, including 

counterfeit and modified documents and impostors use of legitimate 

documents. Travel documents would continue to serve as evidence that 

the bearer has the right of entry. The addition of biometrics can link 

the individual to the travel document and serve as evidence that the 

present bearer of the document is indeed the proper bearer. At ports of 

entry, INS inspectors intercepted more than 114,000 fraudulent 

documents last year (see table 12). About one-third of the intercepted 

documents were U.S. passports or visas.



Table 12: The Number and Type of Fraudulent Documents INS Inspectors 

Intercepted, Fiscal Year 2001:



Document type: Border crossing cards; Number intercepted: 30,419.



Document type: Alien registration cards; Number intercepted: 26,259.



Document type: Nonimmigrant visas; Number intercepted: 21,127.



Document type: U.S. passport and citizenship documents; Number 

intercepted: 18,925.



Document type: Foreign passport and citizenship documents; Number 

intercepted: 15,994.



Document type: Reentry permit and refugee travel documents; Number 

intercepted: 702.



Document type: Immigrant visas; Number intercepted: 597.



Document type: Total; Number intercepted: 114,023.



Source: INS.



[End of table]



The Census Bureau has estimated that between 7.7 million and 8.8 

million unauthorized immigrants were in the United States in 

2000.[Footnote 26] INS has estimated that the annual increase in the 

number of unauthorized immigrants is about 275,000.[Footnote 27] Of 

this number, INS estimates that about 60 percent of illegal immigration 

occurred between the borders and not at a port of entry where people 

or documents could be inspected. INS estimates that the remaining 40 

percent of the undocumented population are nonimmigrant overstays, 

meaning they entered legally on a temporary basis but failed to depart. 

While it appears that current border control processes reduced the 

annual number of unauthorized entrants by about one-third, it is not 

known how many other travelers used fraudulent documents to enter the 

United States. Today, inspectors check identity manually, comparing 

photographs in a travel document with the face of the person carrying 

the document.



Linking biometrics to visas and passports would help ensure that 

travelers could not obtain travel documents under alternative 

identities once they had already applied for initial documents and 

established a biometric identity in the system. It would also help 

ensure that travelers who crossed the borders were the persons depicted 

on their travel documents. These two benefits could potentially 

decrease document fraud by making it harder to obtain a visa or 

passport under an assumed identity. The scenario could also reduce the 

use of counterfeit visas and passports and the use of legitimate 

documents by impostors.



Limitations to this approach are that a visa or passport biometric 

cannot necessarily link a person to his or her true identity, although 

it can bind him or her to a single identity within a system. A visa or 

passport biometric system would make it more difficult for people to 

establish multiple identities. Nevertheless, if the one identity a 

person claimed were not his or her true identity, then the person would 

be linked to the false identity in the biometric system.



Issuing visas with biometrics may have a limited effect because of the 

relatively few travelers who must carry a visa to enter the United 

States. While nonimmigrant aliens made 239 million border crossings 

last year, many were not required to present a visa at the port of 

entry, including Canadians, Mexicans who possessed a border crossing 

card, and aliens entering through the visa waiver program. It is 

estimated that in only about 22 million crossings were aliens required 

to have a visa to enter the United States last year--about 15 million 

entered as visitors or with task-specific visas (e.g., students), and 

another 7 million entered as crew on airplanes or ships. Even though 

the current Mexican border crossing cards are issued with two 

fingerprint templates on the card, it is unclear how Mexicans would be 

affected by a decision to issue visas with biometrics.



Issuing passports with biometrics might also have limited effect 

because passports are not required of U.S. citizens when they enter the 

United States from Canada or Mexico. While U.S. citizens made more than 

179 million border crossings last year, it is not clear how many of 

them needed or presented a passport to inspectors at the ports of 

entry.



While it is standard practice to quantify benefits in monetary terms, 

it is difficult to do so for security applications. The monetary 

benefits of keeping inadmissible people out of the country depend on 

the activities undertaken while these travelers are in the country. 

Some inadmissible people may simply affect the labor supply, while 

others may conduct criminal or terrorist activities. Further 

information, including behavioral assumptions, would be necessary in 

order to characterize the value of preventing the entry of inadmissible 

persons.



As we have already stated, biometric technology is not a panacea for 

all border security problems. For example, none of these scenarios 

addresses two other key problems with border security. Previous INS 

estimates of illegal immigration were that about 60 percent of all 

illegal immigrants entered between the borders, not at a port of 

entry where they could be inspected. The scenarios we describe also 

will not help address problems with aliens overstaying their visits; 

aliens who overstay have already presented themselves at a port of 

entry and were admitted by an inspector.



System Life-Cycle Costs:



For each of the four scenarios, we created cost models to estimate the 

cost of developing, implementing, and maintaining various biometric 

processes. Besides including in the models the cost of purchasing the 

biometric hardware, we estimated costs for additional hardware, 

software, maintenance, personnel, training, and effects on other 

procedures in order to derive life-cycle cost estimates. We used DODs 

definition of life-cycle cost, which includes all costs the government 

incurs in designing, developing, and operating a system through its 

life cycle, from its initiation through disposal of the system at the 

end of its useful life. We followed the cost element structure that DOD 

uses at acquisition program milestone and decision reviews to assess 

major automated information systems costs. Tailoring this structure to 

reflect our four scenarios, we used it to standardize costs so that 

they could be compared at a high level.



We present the costs in two parts. Initial costs represent the costs 

required to plan, design, develop, and field the system. Recurring 

costs represent the annual costs required to operate and continually 

maintain the system to keep it in operation.



We estimated seven sets of initial cost elements: costs for systems 

engineering and program management; development, installation, and 

training; biometric hardware; biometric software; network 

infrastructure; renovating consular facilities; and hardware 

infrastructure upgrades. We estimated ten sets of recurring cost 

elements: program management; biometric hardware maintenance; software 

and system maintenance; network infrastructure maintenance; consular 

operating personnel; port of entry operating personnel; communications; 

training; consular facility maintenance; and annual supplies. (More 

details on the cost elements can be found in appendix VI.):



Assumptions:



We prepared the life-cycle cost estimates using fiscal year 2002 

constant dollarsthat is, inflation was not considered for the multiple 

years over which funds would be required for acquisitionand they 

represent rough order of magnitude costs. In addition, the estimates in 

our technology assessment are best guesses and should not be considered 

budget quality. They attempt to provide a high-level view of what 

costs could potentially be, given the assumptions we describe here. In 

order to develop budget-quality estimates, more details about the 

system to be built are required, including an operational concept, 

detailed requirements, site surveys, and vendor proposal data. 

Following are the assumptions that frame the boundary of our cost 

estimates.



Scenario life-cycle cost estimates represent development and 

installation time plus 10 years operational life. Phasing of costs 

over time is simplified, and actual schedules to both develop and 

install equipment and infrastructure will most likely differ.



Biometric technologiesfingerprint, facial, and iris 

recognitionrepresent standardization to a single vendors protocols. 

Biometric technology costs represent the average costs of vendors 

products. Four flat fingerprints will be collected for fingerprint 

recognition.



There are 210 visa-issuing embassies and consulates worldwide. There 

are 4,500 passport acceptance offices. There are 3,950 primary and 

secondary inspection stations at 400 ports of entry.



Personnel costs reflect both direct costs and indirect costs. Three 

personnel will be needed to troubleshoot equipment at each port of 

entry, or 1,200 additional staff.



No costs were estimated for:



* additional inspectors at ports of entry,



* additional facility space for passport acceptance offices or at ports 

of entry for primary and secondary inspections,



* biometric equipment for exiting the United States,and:



* biometric security technology (e.g., encryption of biometric data).



Costs for Scenarios 1 and

2: Watch List Checks:



We used the following assumptions to create the cost estimates for the 

two biometric watch list scenarios:



* The watch list database will include 10 million records.



* Matches will be performed using facial recognition technology.



* To conduct watch list checks before issuing travel documents, facial 

images will be generated by capturing the physical photographs 

applicants present when they apply for a visa or passport.



* The images will be collected and scanned at consulates and embassies 

for visas and at passport acceptance offices and transmitted through 

telecommunications resources to a central facility in metropolitan 

Washington, D.C.



Estimates include costs for a primary central processing facility and a 

contingency processing site. Table 13 summarizes the costs for the two 

watch list scenarios.



Table 13: Estimated Costs for Watch List Checks:



Scenario: 1. Watch list check before issuing travel documents; Initial: 

$52.8; Recurring: $72.9.



Scenario: 2. Watch list check before entering the United States; 

Initial: $330.2; Recurring: $237.0.



Note: Dollars are in millions.



Source: GAO analysis.

:



[End of table]



In scenario 1, the major cost is additional consular staff to review 

biometric watch list hits. It is assumed that each embassy or consulate 

will require at least one additional foreign service officer to review 

biometric watch list hits before visas are issued. If the performance 

of the biometric technology requires more reviews and consequently more 

staff, the cost of the scenario will increase. Of the $52.8 million 

initial cost, $33.1 million is for the placement of 221 additional 

foreign service officers. Only $19.8 million is for the systems 

development, installation, and associated costs. Similarly, of the 

$72.9 million recurring costs per year, $50.7 million is for additional 

foreign service officers. Because it is unclear how many additional 

passport examiners would be required to review biometric watch list 

hits for passports, we have not included costs for additional passport 

examiners.



In scenario 2, adding facial recognition technology at the 400 ports of 

entry greatly increases costs over scenario 1. The additional costs 

related to developing and installing equipment at 3,950 primary and 

secondary inspection stations at the ports of entry adds another $200 

million to the systems initial cost. (More details on the estimated 

costs for conducting watch list checks with biometrics can be found in 

appendix VI.):



Costs for Scenarios 3 and 4: U.S. Visas and Passports with Biometrics:



We used the following assumptions to estimate the costs of adding 

biometrics to visas and to passports:



* The number of visa applicants will remain constant at 10.3 million 

annually. The number of travelers in the visa waiver program will 

remain constant at 14 million annually.



* The number of passport applicants will remain constant at 7 million 

annually.



* Enrolling travelers using a single biometric (whether for 

fingerprint, facial, or iris recognition) is estimated at 6 minutes (10 

applicants enrolled per hour).



* Enrolling travelers using multiple biometrics (e.g., fingerprint and 

facial combined, fingerprint and iris combined, or fingerprint, facial, 

and iris combined) is estimated at 10 minutes (6 applicants enrolled 

per hour).



* All current visa-issuing embassies and consulates and passport 

acceptance offices will be equipped to collect biometrics from visa and 

passport applicants, respectively.



* Biometric token cards will be used to verify identities.



We present cost estimates for six different combinations of biometric 

technologies under two different possibilities for issuing visas (see 

table 14). The State Department receives about 10.3 million visa 

applications each year. In fiscal year 2000, INS estimated that 

approximately 14 million individuals traveled under the visa waiver 

program. If these travelers must obtain a visa to travel to the United 

States, we assume that this same number would also be required to have 

their biometric sample collected. An additional 14 million applicants 

increases the initial costs of the biometric system by about 30 percent 

and annual recurring costs by about 50 percent. The costs differ 

between the different combinations of biometrics because of the 

different costs of the different types of equipment and the increased 

time required to enroll people if more than one biometric is used.



Table 14: Estimated Costs for Issuing Visas with Biometrics:



Annual visa applications: Annual visa applications : 10.3 million

with visa waiver program: [Empty].



Annual visa applications: 10.3 million

with visa waiver program: Scenario 3: Issuing visas with biometrics: 

Initial: Recurring: [Empty].



Scenario 3: Issuing visas with biometrics: Fingerprint recognition; 

Initial: $1,422; Annual visa applications: 10.3 million

with visa waiver program: Recurring: $708; Annual visa applications: 

10.3 million with visa waiver program: [Empty]; Annual visa 

applications: 24.3 million: Initial: $1,879; Annual visa applications: 

24.3 million: Recurring: $1,077.



Scenario 3: Issuing visas with biometrics: Iris recognition; Initial: 

1,419; Annual visa applications: 10.3 million with visa waiver program: 

Recurring: 707; Annual visa applications: 10.3 million with visa waiver 

program: [Empty]; Annual visa applications: 24.3 million: Initial: 

1,876; Annual visa applications: 24.3 million: Recurring: 1,075.



Scenario 3: Issuing visas with biometrics: Facial recognition; Initial: 

1,399; Annual visa applications: 10.3 million

with visa waiver program: Recurring: 698; Annual visa applications: 

10.3 million with visa waiver program: [Empty]; Annual visa 
applications: 

24.3 million: Initial: 1,851; Annual visa applications: 24.3 million: 

Recurring: 1,065.



Scenario 3: Issuing visas with biometrics: Fingerprint and iris 

recognition; Initial: 1,926; Annual visa applications: 10.3 million

with visa waiver program: Recurring: 863; Annual visa applications: 

10.3 million with visa waiver program: [Empty]; Annual visa 

applications: 24.3 million: Initial: 2,509; Annual visa applications: 

24.3 million: Recurring: 1,331.



Scenario 3: Issuing visas with biometrics: Fingerprint and facial 

recognition; Initial: 1,904; Annual visa applications: 10.3 million

with visa waiver program: Recurring: 854; Annual visa applications: 

10.3 million with visa waiver program: [Empty]; Annual visa 
applications: 

24.3 million: Initial: 2,479; Annual visa applications: 24.3 million: 

Recurring: 1,318.



Scenario 3: Issuing visas with biometrics: Fingerprint, iris, and 

facial recognition; Initial: 2,243; Annual visa applications: 10.3 

million with visa waiver program: Recurring: 970; Annual visa 

applications: 10.3 million with visa waiver program: [Empty]; Annual 
visa 

applications: 24.3 million: Initial: 2,845; Annual visa applications: 

24.3 million: Recurring: 1,482.



Note: Dollars are in millions.



Source: GAO analysis.



[End of table]



Operating personnel and space at the embassies and consulates are a 

major component of the cost estimates. Table 15 shows the initial and 

recurring costs for consular operating personnel and space when using 

single biometric and multiple biometrics. Depending on the combination 

of biometric technologies, we estimate the costs of consular operating 

personnel and space at 21 to 31 percent of initial costs and 23 to 29 

percent of recurring costs. We did not include costs at ports of entry 

for facility renovation or personnel to verify the biometrics of 

travelers with visas as they enter the country.



Table 15: Estimated Consular Costs for Issuing Visas with Biometrics:



Annual visa applicants: 



Scenario 3: Issuing visas with biometrics; [Empty]; Annual visa 

applicants: 10.3 million: [Empty].



Operating personnel; Single biometric; Annual visa applicants: 10.3 

million: Initial: $75.9; Annual visa applicants: 10.3 million: 

Recurring: $111.6; Annual visa applicants: 10.3 million: [Empty]; 

Annual visa applicants: Initial: $114.9; Annual visa applicants: 24.3 

million: Recurring: $150.6.



Multiple biometrics; Annual visa applicants: 10.3 million: Initial: 

95.0; Annual visa applicants: 10.3 million: Recurring: 130.7; Annual 

visa applicants: 10.3 million: [Empty]; Annual visa applicants: 

Initial: 160.0; Annual visa applicants: 24.3 million: Recurring: 

195.7.



Space; Single biometric; Annual visa applicants: 10.3 million: Initial: 

335.8; Annual visa applicants: 10.3 million: Recurring: 89.5; Annual 

visa applicants: 10.3 million: [Empty]; Annual visa applicants: 

Initial: 463.6; Annual visa applicants: 24.3 million: Recurring: 123.6.



Multiple biometrics; Annual visa applicants: 10.3 million: Initial: 

378.2; Annual visa applicants: 10.3 million: Recurring: 100.9; Annual 

visa applicants: 10.3 million: [Empty]; Annual visa applicants: 

Initial: 563.7; Annual visa applicants: 24.3 million: Recurring: 

150.5.



Note: Dollars are in millions.



Source: GAO analysis.



[End of table]



Another major recurring cost is storage media for the biometric. At a 

cost of about $15 per card for laser cards, this adds more than $150 

million to the recurring costs for 10.3 million visa applicants, or 

more than $360 million for 24.3 million applicants.



Table 16 summarizes the costs of issuing passports with biometrics with 

six different combinations of biometric technologies. Scenario 4 is by 

far the most expensive. The primary cost difference between issuing 

visas with biometrics and passports with biometrics is in the number of 

issuing locations. While only 210 embassies and consulates can receive 

visa applications, and even though fewer passport applications are 

received annually than visa applications, there are more than 20 times 

more passport acceptance offices (4,500) than embassies and consulates 

that issue visas. This greater number of locations has a direct effect 

on the estimates of initial and recurring costs for this scenario.



Table 16: Estimated Costs for Issuing Passports with Biometrics:



Scenario 4: Issuing passports with biometrics: Fingerprint recognition; 

Initial: $4,491; Recurring: $1,574.



Scenario 4: Issuing passports with biometrics: Iris recognition; 

Initial: 4,486; Recurring: 1,572.



Scenario 4: Issuing passports with biometrics: Facial recognition; 

Initial: 4,446; Recurring: 1,555.



Scenario 4: Issuing passports with biometrics: Fingerprint and iris 

recognition; Initial: 6,694; Recurring: 1,978.



Scenario 4: Issuing passports with biometrics: Fingerprint and facial 

recognition; Initial: 6,655; Recurring: 1,961.



Scenario 4: Issuing passports with biometrics: Fingerprint, iris, and 

facial recognition; Initial: 8,766; Recurring: 2,363.



Note: Dollars are in millions.



Source: GAO analysis.



[End of table]



As with the scenario in which visas are issued with biometrics, two 

major costs are the costs of cards to store the biometrics and the 

personnel required to collect the biometric sample from passport 

applicants. The cost of cards adds more than $100 million per year to 

the costs of the biometric system. While it is not clear how biometrics 

would be collected at passport acceptance offices, we assumed that 

collection at each office would require one additional staff person, 

for an additional annual cost of $443.8 million. We did not include 

costs for additional space at the offices.



In the scenarios where biometrics are added at the ports of entry 

(i.e., performing a watch list check before entering the United States 

and issuing visas and passports with biometrics), the cost of 

additional inspectors is not included. As we have previously described, 

the addition of biometrics at the ports of entry would likely increase 

the inspection time of each traveler. Without the addition of 

inspectors and the corresponding space (i.e., inspection stations or 

lanes), delays would go up at the ports of entry. We did not analyze 

how many additional inspectors would be required to maintain current 

service times. These costs will have to be collected and analyzed as 

part of the preparation for a budget-quality estimate should the 

government wish to pursue one of these options. (More details on the 

estimated costs of issuing visas and passports with biometrics can be 

found in appendix VI.):



Uncertainty Analysis:



Simulation is an analytical method meant to imitate a real-life system, 

especially when other analyses are too mathematically complex or too 

difficult to reproduce. Risk analysis uses both a spreadsheet model and 

simulation to analyze the effect of varying the inputs to a modeled 

system on the outputs. One type of spreadsheet simulation is Monte 

Carlo, which randomly generates values for uncertain variables over and 

over to simulate a model. The simulation results show not only the 

different result values but also the probability (or certainty) of 

values.



We used both the initial and recurring costs as the forecast values and 

ran the Monte Carlo simulation for each of the four scenarios. We 

applied a probability distribution to each parameter that we thought 

could vary, such as the costs for development and installation, annual 

operating personnel, and additional square feet in embassy or consular 

facilities. Table 17 shows our estimates, the level of certainty 

calculated by the simulation for our estimates, and the cost for each 

scenario at the 90 percent certainty level. For issuing visas and 

passports with biometrics, we simulated only two of the six possible 

combinationsone using a single biometric and one using multiple 

biometrics.



Table 17: Cost Estimate Uncertainty Analysis for Four Scenarios:



Scenario: 1. Watch list check before issuing document; Cost: $52.8; 

Initial: % certainty: 50%; Cost at 90% certainty: $53.3; [Empty]; 

Recurring: Cost: $72.9; Recurring: % certainty: 50%; Recurring: Cost at 

90% certainty: $74.2.



Scenario: 2. Watch list check and facial recognition; Cost: 330.2; 

Initial: % certainty: 50; Cost at 90% certainty: 347.9; [Empty]; 

Recurring: Cost: 237.0; Recurring: % certainty: 91; Recurring: Cost at 

90% certainty: 236.4.



Scenario: 3. Visa; Cost: [Empty]; Initial: % certainty: [Empty]; Cost 

at 90% certainty: [Empty]; [Empty]; Recurring: Cost: [Empty]; 

Recurring: % certainty: [Empty]; Recurring: Cost at 90% certainty: 

[Empty].



Scenario: Fingerprint recognition; Cost: 1,879; Initial: % certainty: 

70; Cost at 90% certainty: 1,923; [Empty]; Recurring: Cost: 1,077; 

Recurring: % certainty: 91; Recurring: Cost at 90% certainty: 1,059.



Scenario: Fingerprint and facial recognition; Cost: 2,479; Initial: % 

certainty: 60; Cost at 90% certainty: 2,529; [Empty]; Recurring: Cost: 

1,318; Recurring: % certainty: 89; Recurring: Cost at 90% certainty: 

1,324.



Scenario: 4. Passport; Cost: [Empty]; Initial: % certainty: [Empty]; 

Cost at 90% certainty: [Empty]; [Empty]; Recurring: Cost: [Empty]; 

Recurring: % certainty: [Empty]; Recurring: Cost at 90% certainty: 

[Empty].



Scenario: Facial recognition; Cost: 4,446; Initial: % certainty: 60; 

Cost at 90% certainty: 4,725; [Empty]; Recurring: Cost: 1,555; 

Recurring: % certainty: 92; Recurring: Cost at 90% certainty: 1,518.



Scenario: Fingerprint and iris recognition; Cost: 6,694; Initial: % 

certainty: 70; Cost at 90% certainty: 6,892; [Empty]; Recurring: Cost: 

1,978; Recurring: % certainty: 91; Recurring: Cost at 90% certainty: 

1,953.



Note: Dollars are in millions.



Source: GAO analysis.



[End of table]



Developing, integrating, deploying, and maintaining biometrics to help 

secure the nations borders will be costly. For example, the cost to 

implement visas with biometric technologies would be on a par with a 

major DOD weapons systems acquisition or FAAs Standard Terminal 

Automation Replacement System.



Effects on Privacy and the Economy:



Privacy and Civil Liberties:



Underlying much discussion about the deployment of biometric technology 

are questions about the sufficiency of information management laws, 

such as the Privacy Act of 1974 and the Computer Security Act of 1987, 

to protect civil liberties.[Footnote 28] Periodic public surveys have 

revealed a distinct unease with the potential ability of the federal 

government to monitor individuals movement and transactions.



The Privacy Act limits federal agencies collection, use, and 

disclosure of personal information. The acts protections are keyed to 

the retrieval of personal information by an identifying number, 

symbol, or other identifying particular assigned to the individual, 

such as a finger or voice print, or a photograph.[Footnote 29] 

Accordingly, the Privacy Act generally covers federal agency use of 

personal biometric information.



As a practical matter, however, the act is likely to have a more 

limited application to biometric information in the context of border 

control. First, it applies only to personal information regarding U.S. 

citizens and lawfully admitted permanent resident aliens.[Footnote 30] 

Second, the act includes a number of exemptions that permit the 

disclosure of otherwise covered information for internal agency use, 

for compatible routine uses, and for law enforcement and national 

security purposes.[Footnote 31]



Representatives of civil liberties groups and privacy experts are 

concerned about (1) the adequacy of protections for security, data 

sharing, identity theft, and other identified uses and (2) secondary 

uses and function creep. A significant number of concerns raised 

during our interviews and conferences relate to the adequacy of 

protections under current law for the large-scale data handling in a 

biometric system. Besides information security, concern was voiced 

about an absence of clear criteria for governing data sharing. The 

broad exemptions of the Privacy Act, for example, provide no guidance 

on the extent of the appropriate uses law enforcement may make of 

biometric information.



Of equal concern is a tendency for large organizations to develop 

secondary uses of information; information collected for one purpose 

tends over time to be used for other purposes as well. The history of 

the Social Security number, for example, gives ample evidence of how an 

identifier developed for one specific use has become a mainstay of 

identification for many other purposes, governmental and 

nongovernmental.[Footnote 32] Secondary uses of the Social Security 

number have been a matter not of technical controls but, rather, 

changing policy and administrative priorities. Further, some are 

concerned that biometric information can potentially be linked to 

multiple databases or to a vast national database. Questions being 

raised include what data would be included or linked to a biometric 

identification card; who would have access to such information, 

legitimately or otherwise; and how people who can access such data 

could use them.



Still others mention major concerns under the three headings of 

tracking, profiling, and loss of anonymity. Tracking is real-time, or 

near-real-time, surveillance in which a persons movements are followed 

through her biometrics-enabled transactions. While none of the 

scenarios we discuss use biometric technologies for surveillance, we 

have heard concerns raised about ways in which anonymity is likely to 

be undermined by surveillance. For example, many civil liberties groups 

are extremely concerned about the application of facial recognition 

technology for surveillance, which, like video surveillance, could 

result in the loss of anonymity in public places.



Profiling is the reconstruction of a persons movements or transactions 

over a specific period of time, usually to ascertain something about 

her habits, tastes, or predilections. Profiling for race, ethnicity, or 

national origin has caused much public debate in recent years. Tracking 

and profiling can destroy anonymity. The lack of clear policy goals and 

any flaws in the operation of biometric technology could compound all 

these concerns.



Concerns have also been raised about whether certain biometric data 

might reveal medical predispositions or personal health histories whose 

use could result in denial of insurance coverage or employment. For 

example, while not currently viable, the use of DNA matching as a 

biometric technology would be of concern because of the personal 

medical information that could be gleaned from it.



Not only are there concerns with secondary uses, but there are also 

concerns with unauthorized uses. Our recent studies on identity theft 

and studies by others reflect the difficulty of accurately measuring 

identification theft.[Footnote 33] Developing a large-scale 

interoperable system such as a watch list system or issuing visas or 

passports with biometrics could increase the risks of identity theft 

and other unauthorized uses of personal information if the biometric 

data are not properly protected.



Biometrics industry groups, while expressing their appreciation of 

privacy concerns, have responded by saying that biometric products are 

privacy neutral and that it is how they are used that reflects either 

privacy invasion or privacy protection. IBG has developed a framework 

for defining the potential privacy risks borne by specific biometric 

technologies and their deployment. IBIA is also advocating on behalf of 

the industry to create responsible use guidelines and public policy. 

Industry groups emphasize self-regulation, which some privacy groups 

assert is not enough because markets are erratic and because, they say, 

the high value placed on data means incentives for violation are too 

high. Nonindustry groups have also developed privacy frameworks. The 

Internal Revenue Service (IRS) published a guide to developing privacy 

impact assessments for information technology. Also, the RAND 

Corporation developed a four-step approach for responding to 

sociocultural concerns about biometrics. Table 18 combines some of the 

salient characteristics of the guidelines IBG, IRS, and RAND developed 

and outlines many of the questions to be answered in assessing the 

potential effect on privacy from any new biometric system.



Table 18: Summary of Biometric Systems Privacy Guidelines:



Issue: Scope and capabilities; Guideline: Does the system have a 

clearly and narrowly defined purpose?; Who will use the system?; Have 

potential system capabilities been evaluated?; Has there been an 

evaluation of a range of alternative choices, including biometrics?; 

What types of information will be available through the biometric?; 

Will the biometric information be used as a universal unique 

identifier?; Will the storage of biometric information include 

extraneous information?; Will the system store the original biometric 

data?.



Issue: Data protection; Guideline: Will the system separate biometric 

information from other types of personal information?; What procedures 

will limit access to the system? Who will have access? Do other systems 

or agencies share or have access to data in this system? If data are 

being consolidated, what controls protect them from unauthorized access 

or use?; How will the system ensure accuracy? What are the sources of 

information in the system? How will data collected from other sources 

be verified for accuracy?; Will the system derive new data or create 

previously unavailable data about an individual through aggregation 

from the information collected? Can the system make determinations 

about individuals that would not be possible without the new data?; How 

long will data be retained in the system? What are the procedures for 

eliminating the data at the end of the retention period? While the data 

are retained in the system, what are the requirements for determining 

whether the data are sufficiently accurate, timely, and complete to 

ensure fairness?.



Issue: User protection; Guideline: Will users have the ability to 

unenroll?; Will users be able to access and correct their biometric 

information?; Will there be procedures for anonymous enrollment?; Will 

the system and its use ensure individuals equitable treatment? If the 

system is operated in more than one site, how will consistent use of 

the system and data be maintained?; Will the system be able to 

identify, locate, and monitor individuals or groups of people? What 

controls will prevent unauthorized monitoring?.



Issue: Disclosure, auditing, accountability, and oversight; Guideline: 

Will there be full disclosure of audit data?; Will the systems purpose 

be disclosed? Will the enrollment, verification, and identification 

processes be disclosed? Will the names of the individuals and entities 

responsible for system operation and oversight be accessible?; Will 

users be informed about optional versus mandatory enrollment?; Will a 

board or committee assess biometric data policies?; Will third parties 

oversee the system? Who will review requests for access to biometric 

data? Who ensures that the biometric program is responsive to privacy 

concerns?.



Source: GAO summary of data from IBG, IRS, and RAND Corporation.



[End of table]



These guidelines can help decision makers and other stakeholders 

approach privacy issues and determine the appropriate balance of 

privacy and security to build into the system. However, because there 

is no general agreement on the answers to these guidance questions, 

further policy decisions are needed. The range of unresolved policy 

issues suggests that questions surrounding the use of biometric 

technology center as much on management policies as on technical 

issues.



Convenience for Travelers:



As previously described, implementing biometrics could lengthen the 

process of obtaining travel documents or entering the United States. At 

some posts, visas are issued the same day applications are received. If 

significant time is required to resolve biometric watch list or visa 

database hits, issuing visas could be delayed. At the ports of entry, 

in order to avoid long lines of pedestrians and vehicles, each 

inspection has to be fast--according to INS officials in El Paso, 

Texas, for example, any time longer than 15 seconds would cause 

staggering delays. Even so, the busiest ports of entry regularly have 

delays of 2 to 3 hours.



Checking the biometric identity of passengers in vehicles is especially 

challenging. In a biometric system, would passengers have to exit their 

vehicles in order to have their biometrics checked? Any increase in 

inspection times could compound delays. Delays inconvenience travelers 

and increase their costs. Studies have been conducted on the value of 

travel time, and further studies in this area could help determine 

whether the increased security could result in fewer visits to the 

United States or lost business to the nation.



Economic Impact:



While biometrics-based border control would affect regional economies 

and various economic sectors, it is difficult to quantify its effect. 

However, we can postulate that the travel and tourism industry might be 

adversely affected. Spending by international travelers in the United 

States totaled about $103 billion in 2000 and $90 billion in 2001. This 

spending is particularly important for California, Florida, and New 

York, which together account for more than half of all spending by 

international overseas visitors. If a biometric system made it more 

difficult to obtain visas for whatever reason, from higher visa fees to 

longer time between application and issuance, international travelers 

might choose to visit other countries instead. Further, there are 

concerns that if fingerprint recognition technology were used, the 

number of visitors from countries such as Japan would decrease 

dramatically because of those societies aversion to fingerprinting.



At the regional level, biometrics could significantly affect trade with 

Canada and Mexico, the nations largest trade partners, with total 

trade amounting to $653 billion in 2000. More than 80 percent of all 

Canadian exports are destined for the United States. If biometric 

identification checks increased waiting time at land crossings, local 

merchants on both sides of the border could lose sales. Biometric 

systems might also have a profound effect on Mexicos maquiladora 

industry--the most dynamic sector of the Mexican economy, adding 1,400 

new plants and 640,000 new jobs since the 1994 implementation of the 

North American Free Trade Agreement (NAFTA).[Footnote 34] U.S.-NAFTA 

partner trade is concentrated at a few ports. In 2000, 10 accounted for 

73 percent of all North American trade by land. Biometrics-based border 

control would have to be implemented carefully at these ports to 

preserve the flow of trade.



International Relations:



The use of biometrics in a border control system in the United States 

could affect the number of international visitors and how other 

countries treat visitors from the United States. Much visa issuance 

policy is based on reciprocity--that is, the process for allowing a 

countrys citizens to enter the United States would be similar to the 

process followed by that country when U.S. citizens travel there. If 

the United States requires biometric identifiers when aliens apply for 

a visa, other nations may require U.S. citizens to submit a biometric 

when applying for a visa to visit their countries. Similarly, if the 

United States requires other countries to collect biometrics from their 

citizens and store the data with their passport for verification when 

they travel here, they may require the United States to place a 

biometric in its passports as well.



As more countries require the use of biometrics to cross their borders, 

there is a potential for different biometrics to be required for 

entering different countries or for the growth of multiple databases of 

biometrics. Unless all countries agree on standard biometrics and 

standard document formats, a host of biometric scanners might be 

required at U.S. and other ports of entry. ICAO plans to standardize 

biometric technology for machine-readable travel documents, but 

biometric data-sharing arrangements between this country and others 

would also be required.



[End of section]



Chapter 6 Summary:



In this report, we have considered a number of leading and emerging 

biometric technologies that could potentially be used for securing the 

nations borders. The seven leading biometric technologies include 

facial recognition, fingerprint recognition, hand geometry, iris 

recognition, retina recognition, signature recognition, and speaker 

recognition. Among the emerging technologies, we considered vein scan, 

facial thermography, DNA matching, odor sensing, blood pulse 

measurement, skin pattern recognition, nailbed identification, gait 

recognition, and ear shape recognition. Our assessment is based on a 

snapshot of biometric technologies as they existed in early 2002.



Of the seven leading technologies, fingerprint recognition, facial 

recognition, iris recognition, and hand geometry appeared to be 

suitable for border security. These technologies could be used to 

associate a persons identity with travel documents and thus deter 

fraudulent use of travel documents. Some of these technologies could be 

used to check to see whether a person is on a watch list. Of the four 

technologies, hand geometry is not very good at identifying one person 

in millions and, therefore, is not suitable if we want to search the 

biometric database to determine whether a person has previously 

enrolled in the database. However, hand geometry can be used to verify 

identity in combination with another technology. We found that the 

emerging biometric technologies are in various stages of development 

and have not yet been used in border control applications.



When it comes to effectiveness, all biometric technologies share a 

common characteristic. Every time a biometric feature is captured, it 

is always slightly different from the feature that was originally 

captured and stored in the system. Also, sometimes the biometric device 

cannot capture the biometric feature at all. Thus, all biometric 

technologies suffer from three types of error--the failure to capture a 

biometric feature, falsely not matching a biometric even though the 

persons biometric is in the system, or falsely matching a biometric. 

Each biometric technology has different levels of these errors, and the 

errors depend on many different factors, including the operational 

environment and security-level setting. For example, it is possible to 

trade off the false match and false nonmatch errors against each other. 

Thus, the effectiveness of a biometric technology depends on how it is 

used in an overall system.



Key Considerations

in Using Biometrics for Border Control:



It is important to recognize that biometric technology would be but one 

component of the decision support systems that determine who is allowed 

to enter the United States and who is not. As we have described, these 

decisions are generally two-step processes. First, a decision must be 

made to determine whether or not to issue a traveler a U.S. travel 

document. Second, a decision is made at a port of entry on whether to 

admit the traveler into the country. While the first step is not always 

executed, depending on the nationality of the traveler, all legal 

entries into the United States must be through an official port of 

entry.



The task of sorting admissible travelers from inadmissible ones is now 

conducted by using information systems for checking names against watch 

lists and by using human, manual recognition capabilities to determine 

whether a photograph on a travel or identification document matches the 

person who seeks entry into the United States. The introduction of 

biometrics into this process could help automate the identification of 

travelers.



In this report, we explored the use of biometrics in two types of 

systems. In one system, biometrics can check a persons face against a 

watch list of facial images and provide alerts if there is a potential 

match. In another system, the identity of travelers can be verified by 

comparing their proffered biometrics (e.g., a fingerprint) against 

stored templates that are associated with their travel documents.



We have found that three key considerations must be addressed before a 

decision is made to design, develop, and implement biometric 

technologies in a border control system:



9.	 Decisions must be made on how the technology will be used.



10.	 A detailed cost-benefit analysis must be conducted to determine 

that the benefits gained from a system outweigh the costs.



11.	 A trade-off analysis must be conducted between the increased 

security, which the use of biometrics would provide, and the effect on 

areas such as privacy and the economy.



As we have described, technology and people each have a role in 

executing processes to achieve a goal. Before anything else can be 

defined, the high-level goals of a system with biometrics must be 

clearly articulated. System goals are based on business or public 

policy needs. For example, a goal could be to prevent known 

inadmissible people from entering the country. Based on the high-level 

goals, a concept of operations can be developed that embodies the 

people, process, and technologies to achieve the goals. To put together 

the concept of operations, a number of inputs have to be considered, 

including legal requirements, existing processes and infrastructure 

used, and known technology limitations. Performance requirements should 

also be included in the concept of operations. For example, an average 

inspection time or an average time to issue a visa could be included as 

performance requirements. Any process reengineering that would be 

required to accommodate the new technology should also be handled 

during this stage. For a biometric system, this could include new 

processes to conduct inspection of passengers in vehicles and to 

maintain the database of biometric reference templates.



Once the concept of operations is complete, the requirements of the 

biometric technology system can be developed and a particular 

technology solution can be selected. The system requirements are 

derived from the role of the technology system as defined in the 

concept of operations. Detailed system requirements should include 

functional and performance parameters, interface requirements, 

usability requirements, system quality requirements, and security and 

privacy issues.



The primary question to be asked when selecting the technology solution 

is whether it can support the requirements specified in the concept of 

operations and the system requirements. Particular attention must be 

paid to error measures, such as the false match rate, false nonmatch 

rate, and the failure to enroll rate. Concerns about the 

distinctiveness and stability of the technology, as well as its 

adherence to industry standards, should also be addressed. Because 

biometric technologies have not been used in applications as large as 

border control, further research may be required to establish the 

distinctiveness and stability of the biometric features. 

Distinctiveness has two aspects--how distinct a biometric feature is 

across a population and how many different biometric features are 

needed to uniquely identify an individual in a given population. 

Stability refers to how the biometric features change as a person ages. 

It is unclear whether a biometric captured during enrollment will still 

be properly matched with an acceptable level of accuracy after 5 years, 

for example.



A cost-benefit analysis must be conducted to justify the investment in 

a biometric border control system.[Footnote 35] The benefits gained 

from a biometric border control system should be based on how well the 

system achieves the high-level goals. For example, if a goal of the 

system is to prevent known inadmissible people from entering the 

country, one of the benefits should be based on an estimate of how many 

additional inadmissible people are intercepted or deterred from 

entering the country with this system. The performance of the biometric 

system measured by its error rates would directly affect the expected 

benefit.



All life-cycle costs for the biometric system should be included in the 

analysis. The costs of the system include design, development, 

implementation, operations, and maintenance costs. Costs associated 

with the new business processes needed to use the new system should 

also be included, such as the costs of personnel to enroll people in a 

biometric system and the office space required to conduct the 

operation. Additional people and processes, and their costs, required 

as a result of performance limitations of the technology also must be 

included.



Finally, a trade-off analysis must be conducted between increasing 

security and its effect on areas that are harder to quantify, such as 

privacy, convenience, and the economy. Even if the cost-benefit 

analysis shows that the benefits outweigh the costs, the effect of 

increasing security may affect these areas to such a degree that the 

biometric system should not be undertaken.



Complying with the legal requirements for privacy is necessary to 

implement the system. Further, a system that does not include adequate 

protections for privacy may encounter barriers from users, who may not 

accept it during operation. The historical trade-off in any security 

system is between security and convenience. If a security system is not 

easy to use, people will stop using it. Similarly, if the process to 

enter the United States becomes too hard or time-consuming, people may 

choose to stop coming. This effect may manifest itself as an economic 

impact on the country as retail and trade diminish. Finally, the effect 

on the nations dealings with other countries and their citizens must 

also be considered. International travel involves not only U.S. 

citizens but also citizens from other countries. Hardships imposed on 

those citizens may result in reciprocal procedures being imposed on our 

citizens.



High-Level Analysis of Four Scenarios Using Biometrics:



We defined four different scenarios in which biometrics could be used 

in a border control system. We considered each of the key 

considerations for the different scenarios. We did not answer the 

questions of whether each scenario is cost-beneficial or whether the 

gains in security outweigh the effects on privacy and the economy, but 

we did describe some of the effects that introducing biometrics into 

each scenario would have on people and processes, the benefits to be 

gained from the system, and the limitations of biometric technologies. 

We also described the effect of these limitations on the benefits and 

the effects on privacy, the economy, and international relations.



In addition, we prepared rough order of magnitude costs for each 

scenario. As summarized in table 19, initial investments could range 

anywhere from less than a hundred million dollars for a watch list 

application to billions for biometrics-enabled passports. Many of the 

recurring costs would be for the salaries of personnel required to 

enroll people and operate and maintain the system.



Table 19: Estimated Costs for Implementing Border Security Scenarios:



Scenario: 1, Watch list check before issuing travel documents; Initial 

cost: $53; Annual recurring cost: $73.



Scenario: 2. Watch list check before entering the United States; 

Initial cost: 330; Annual recurring cost: 237.



Scenario: 3. Issuing visas with biometrics; Initial cost: 1,399-2,845; 

Annual recurring cost: 698-1,482.



Scenario: 4. Issuing passports with biometrics; Initial cost: 4,446-

8,766; Annual recurring cost: 1,555-2,363.



Note: Dollars are in million.



Source: GAO analysis.



[End of table]



These costs have to be weighed against the benefits, which include 

reducing the fraudulent use of travel documents and automating the 

determination of whether travelers are on a watch list as they arrive 

at a port of entry. By binding an individuals biometric features to a 

travel document--either by storing the features on a token, such as a 

smart card the traveler carries, or by associating the identity with 

the biometric in a central database--the border inspection process 

would allow travelers to enter only if the stored biometric matches the 

biometric the individual presents at inspection. In a typical watch 

list, photographs of undesirable people would be in a watch list of 

facial images, and a facial recognition system would automatically 

compare the facial image of each traveler with that in the watch list 

and identify potential matches. Inspectors can then further investigate 

those matches.



While using biometric watch lists and incorporating biometrics into 

travel documents could improve border security, the use of biometrics 

alone will not prevent the illegal entry of foreign terrorists and 

others into the country. For example, biometrics cannot prevent the 

illegal entry of those who do not enter through official ports of 

entry. Further, even at the legal ports of entry, unless all travelers 

are required to have their biometrics checked, it is possible that a 

traveler could bypass the biometric check. For example, if U.S. 

citizens are not required to enroll their biometrics to travel 

internationally and an alien could convince the inspector that he or 

she is a U.S. citizen, he or she could avoid the biometric check.



The use of biometric technologies could also have a significant effect 

on many different areas, from privacy concerns to the economy. While it 

appears that the Privacy Act of 1974 generally covers federal agency 

use of personal biometric information, as a practical matter the act is 

likely to have a more limited application in the context of border 

control because the act includes exemptions for law enforcement and 

national security purposes and does not cover nonimmigrant aliens. 

Civil liberties groups and privacy experts expressed concern about the 

adequacy of protections under current law for the large-scale data 

handling in a biometric system. Besides information security, concern 

was voiced about an absence of clear criteria for governing data 

sharing. Another concern was raised about the potential for secondary 

uses of biometric data and what other data would be linked to a 

biometric identification; who would have access to such information, 

legitimately or otherwise; and how people who can access such data 

could use them.



When used in border control, biometric technologies also affect the 

economy and international relations. We can postulate that the travel 

and tourism industry might be adversely affected, because biometrics-

enabled visas may take longer to issue and may be considered more 

trouble than they are worth. Spending by international travelers in the 

United States totaled about $103 billion in 2000. At the regional 

level, biometrics could significantly affect trade with Canada and 

Mexico, the nations largest trade partners, with total trade amounting 

to $653 billion in 2000. If biometric identification checks result in 

increased waiting times at land crossings, local merchants on both 

sides of the border could lose sales.



Using biometrics in a border control system in the United States could 

affect how other countries treat visitors from the United States. The 

reciprocity of visa issuance policy implies that if the United States 

requires biometric identifiers when aliens apply for a visa, other 

nations may require U.S. citizens to submit a biometric when applying 

for a visa to visit their countries. Similarly, if the United States 

requires other countries to collect biometrics from their citizens and 

store the data with their passports for verification when they travel 

to the United States, these countries may require the United States to 

place a biometric in its passports as well. As more countries require 

the use of biometrics to cross their borders, there is a potential for 

different biometrics to be required for entering different countries or 

for the growth of multiple databases of biometrics. Unless all 

countries agree on standard biometrics and standard document formats, a 

host of biometric scanners might be required at U.S. and other ports of 

entry.



The Role of Biometrics in Border Security:



To address the role of biometrics in the overall border security 

problem and the high-level goals that can be achieved by using 

biometrics, a risk-based approach could be used. As we have previously 

reported, risk management is the foundation of effective 

security.[Footnote 36] The approach to good security is fundamentally 

similar, regardless of the assets being protected, whether information 

systems security, building security, or homeland security. The answers 

to five basic questions can help determine the role of biometrics in a 

border security solution:



* What am I protecting?



* Who are my adversaries?



* How am I vulnerable?



* What are my priorities?



* What can I do?



A decision to implement one or more of the scenarios we have defined 

and analyzed in this report should be founded on a risk-based approach 

that answers these questions. These scenarios are by no means the only 

ways to implement biometrics to assist in the border control mission. 

Some of these scenarios could be implemented partially, or brand new 

scenarios could be used. For example, it could be possible to check 

biometrically enabled travel documents only at air ports of entry 

instead of at all ports of entry. Some have suggested that only 

selected travelers biometrics be checked at the ports of entry instead 

of all travelers biometrics. The selection could be random or based on 

travelers who fit a particular description. Another possible 

implementation of biometrics is within optional programs similar to 

INSPASS, in which travelers voluntarily choose to have their biometrics 

enrolled. In such a system, travelers would enroll with the expected 

benefit that they would be able to cross into the United States more 

quickly. Regardless of the system concept, decisions must be made that 

determine how the biometric technology will be used, if the benefits 

outweigh the costs, and what the effects on areas such as privacy and 

the economy will be. While a partial implementation may be less costly, 

the security benefits gained from such a system may also be less.



We have also noted that biometric technologies are not a panacea for 

the border security problem. It is important to realize that even with 

biometrics, many system dependencies cannot be controlled wholly by a 

technological solution. For example, if biometrics are included with 

visas, the process will still require establishing a travelers initial 

identity in the biometric system. Once that identity is established, 

the benefits of strongly binding the individual to that identity can be 

gained. However, the system depends on the process used initially to 

establish that identity--that is, the applicants presentation of a 

passport from his or her country. If the foreign country does not have 

adequate controls over the way it issues passports or, worse, 

deliberately issues passports with false identities, an individual 

could obtain a U.S. visa with a biometric unless additional processes 

are in place to further verify the applicants identity. These 

processes are not a part of the biometric system but are still 

important for border security.



The population of a biometrics-based watch list is also dependent on 

nontechnological processes. As we have previously stated, the policies 

and procedures governing the population of a biometric watch list are 

critical to the success of the program. The success of the program 

depends on the effectiveness of the law enforcement and intelligence 

community to identify individuals to place on the watch list. People 

who are not on the watch list cannot be intercepted when trying to 

obtain a travel document or entering the country. Further, biometrics 

cannot help in detecting illegal entry into the United States through 

other than the official ports of entry. They also cannot help in 

detecting aliens who enter the country legally but then overstay the 

terms of their visit.



Using a risk-based approach should help in the development of a 

biometric systems high-level goals and its concept of operations. The 

answers will help point out the systems limitations and what it will 

not be able to provide. They could also play a role in determining the 

appropriate balance between increasing security and cost and 

operational considerations as well as the effect on issues such as 

privacy and the economy. With these answers, the proper role of 

biometric technologies in border security can be determined.



Agency Comments and Our Evaluation:



We provided a draft of this report to the Department of State and the 

Department of Justice for their review.



Department of State:



In written comments on a draft of this report, the Department of State 

stated that it appreciated the thorough and balanced approach we took 

in our assessment of the use of biometrics for border security. State 

found the overall thrust of the report to be in keeping with its own 

considerations of how biometrics could be used in admitting individuals 

to the United States and how it could be integrated into the existing 

process for visa and passport applications. State agreed with us on the 

need for high-level policy decisions such as defining the specific uses 

of biometric data and performing a cost-benefit analysis that weighs 

the effectiveness and security benefits of biometrics against costs and 

the probable implications or consequences of implementation, including 

economic, civil liberty, and foreign policy concerns. State believed 

that policy decisions must be made before the successful selection, 

execution, and implementation of a border security program involving 

biometrics.



State noted that it is developing additional options for the 

implementation of a biometric program whose final estimated costs may 

differ. State also provided written technical comments on the draft, 

which we incorporated as appropriate.



Department of Justice:



In written comments on a draft of this report, the Department of 

Justice expressed concern that the report did not (1) properly consider 

an overall border security strategy; (2) adequately recognize the draft 

NIST certified standards recommendations for biometrics, tamper-

resistant travel documents, or interoperability; or (3) fully explore 

the advantages of some biometrics over others. Justice also said that 

the draft contained analytical weaknesses related to a misunderstanding 

of the false match rate metric and to performance data and levels.



First, on the subject of an overall border security strategy, Justice 

explained that it has prepared such a strategy and that the U.S. 

government is continuing to consider this strategy. Justice believed 

that the implementation of this strategy would require major 

improvement in border systems. Further, according to Justice, if the 

use of biometrics were limited to visa applicants, who cover only about 

3 percent of visitors to the United States, the impact on preventing 

the entry of potential terrorists into the country would be 

marginalized.



We requested a copy of the strategy from Justice on October 11, 2002, 

but as of October 24, 2002, we had not received the strategy document 

from Justice. While we did not have the opportunity to review Justices 

strategy document, we do agree with Justices assertion that an overall 

border security strategy is needed. Concerning Justices point that 

visa applicants comprise only 3 percent of visitors to the United 

States, it is pertinent to note that limiting the use of biometrics to 

visa applicants would still target individuals living among the 

countries that are a higher risk of directing terrorism at the United 

States. Whether the use of biometrics should be limited to visa 

applicants should be based on Justices border security strategy. We 

have previously stressed the need to develop and implement a homeland 

security strategy in coordination with all relevant partners.[Footnote 

37] This strategy should be comprehensive and should encompass steps 

designed to reduce our vulnerabilities, deter attacks, manage the 

effects of any successful attacks, and provide for appropriate 

response. The strategy should involve all levels of government, the 

private sector, individual citizens both here and abroad, and other 

nations. The strategy should also use a risk management approach to 

focus finite national resources on areas of greatest need. In this 

report, we reiterate the need for a risk-based strategy for the use of 

biometric technology in border security.



Second, Justice was concerned that this report presents information 

about biometrics that is inconsistent with the results of a NIST study 

required by the USA PATRIOT Act. In particular, Justice stated that the 

intended application must (1) employ a biometric that is able to 

establish and verify a unique identity in a population of hundreds of 

millions, (2) be used to run a check against criminal records, and (3) 

operate with a very low risk of false positive reads and with a 

verification process that is not rendered ineffective in different 

border, lighting, and weather conditions.



The results of the NIST study were not available during our technology 

assessment. However, we provided a draft of this report to NIST and 

received comments from NIST reviewers, which we incorporated, where 

appropriate, into the report. Further, we do not see an inconsistency 

between our position and Justices description of the NIST study 

results. We consider these to be examples of items that would be 

defined in a concept of operations or a system requirements 

specification. The thrust of our report has been to point out the 

possibilities and not to select a specific biometric for border 

security--primarily because the selection comes after the concept of 

operations and requirements is developed. The requirements Justice 

described are what the department, with NISTs assistance, is defining 

as the requirements for a biometrics border control system.



Third, Justice stated that there are certain advantages to using 

fingerprints over other biometrics. For example, Justice cited the 

requirement in the Immigration and Nationality Act for aliens to be 

registered and fingerprinted, unless waived at the discretion of the 

Secretary of State. Justice further cited the law enforcement value of 

using fingerprint recognition for biometric identification on a large 

scale. Justice stated that unlike other biometric data, fingerprints 

are left at crime scenes. Further, Justice stated that we do not 

consider that the use of fingerprints would allow for a search against 

records stored in IAFIS to check for criminal history.



We acknowledge the qualities of fingerprint recognition raised by 

Justice. However, as we have described, the additional benefits Justice 

described should be included in the cost-benefit analysis that weighs 

the security benefits gained from a biometrics border control system 

against the costs of building the system. A benefits assessment should 

be based on how well the system achieves the high-level goals defined 

for the system. For example, if the ability to collect biometric 

samples at crime scenes is a requirement, it should be factored into 

the goals and requirements definition of the system. An evaluation of 

the technologies against the requirements would then show that 

fingerprint recognition is the only technology that can meet that 

particular requirement.



Justice also believed that the draft report contained analytical 

weaknesses related to an insufficient analysis of large systems, misuse 

of performance metrics, and the reporting of performance data. 

Specifically, Justice stated that the draft did not provide sufficient 

analysis of large systems and did not define and use the false match 

rate metric correctly. Justice pointed out that the report provided 

incorrect performance data on IAFIS. Justice further cited the problem 

of having to manually resolve false matches and how the size of the 

database affects the number of false matches.



In the report, we state that none of the biometric technologies have 

been used with databases containing hundreds of millions of 

individuals. We have clarified the definition of false match rate and, 

on the basis of written technical comments provided by Justice, we have 

incorporated the performance data on IAFIS. We acknowledge Justices 

point of having to manually resolve false matches. We state in the 

report that the performance of the biometric technology and its effect 

on people and processes are important in the selection of the 

technology. We also describe the potential effects of poorly performing 

biometrics on the border control process.



Finally, Justice stated that the draft report infers that any move 

toward biometrics be made slowly and cautiously. Justice agreed that 

it is important to proceed judiciously but pointed out the sense of 

congressional urgency raised in the USA PATRIOT Act and the Enhanced 

Border Security and Visa Entry Reform Act.



We appreciate the urgency in developing a biometric system for border 

security, but a timely decision to invest in such a system should still 

be made in accordance with applicable federal regulations and best 

practices for acquiring information technology systems.



Justice also provided written technical comments on the draft, which we 

incorporated as appropriate. We have included Justices comments in 

their entirety in appendix VIII.



External Reviewers Comments:



We provided a draft of this report to 16 different organizations for 

their review. Individuals from these organizations were selected 

because of their assistance during the data collection phase of our 

work. In addition, several of them attended one of our two biometric 

and border security meetings, convened for us by the National Academy 

of Sciences. The reviewers represented government, industry, and 

academia. We received comments and suggestions from 10 reviewers. The 

comments ranged from correcting technical inaccuracies to highlighting 

certain aspects of the assessment that the reviewers considered 

important.



Several reviewers commended us for putting together such a thorough 

report in a short time. One reviewer said that the report contains a 

wealth of information that will be useful to all biometric 

practitioners and researchers. Another reviewer noted that we have been 

able to successfully develop a cohesive report despite the difficulties 

associated with the wide variety of information available from vendors 

and studies. A reviewer felt that this will be a milestone report--head 

and shoulders above any other report on biometrics for border security.



In their comments, several reviewers cited their agreement with 

specific findings in our report, particularly with the limitations of 

biometric technologies in helping to secure the nations borders. 

Specifically, reviewers agreed that biometric technologies are not a 

panacea for the border security problem and that they are just one 

component of the decision support systems that determine who is allowed 

to enter the United States and who is not. Further, reviewers agreed 

that biometrics cannot necessarily link a person to his or her true 

identity, although it can bind an individual to a single identity 

within a system. One reviewer concurred with the reports point about 

the difficulty in quantifying the benefits of security improvements.



Regarding the accuracy of a biometrics system, reviewers were concerned 

that we had not clearly defined and used FMR and FNMR as performance 

metrics. Reviewers also mentioned that we should not always equate FMR 

to false accept rate and FNMR to false reject rate. Similarly, 

reviewers were concerned that we did not separate out the results of 

larger tests involving many enrolled individuals from smaller ones and 

that in tabulating the performance of the biometric technologies, we 

had mixed results from verification and identification into the same 

table.



On the basis of these comments, we clarified the definitions of FMR and 

FNMR, highlighted how the same technology can have different 

performance requirements for verification and identification, and 

selected comparable test results when summarizing the performances of 

the biometric technologies in a table.



Reviewers commented on our conclusion that hand geometry was not 

suitable for border control, pointing to its current use in border 

control applications. We had ruled out hand geometry for border control 

because it is not distinctive enough to perform identification matches 

when checking watch lists or for duplicate enrollment. Reviewers 

explained that for issuing visas or passports with biometrics, a more 

distinctive biometric such as fingerprint could be used for the 

duplicate enrollment check, whereas a simpler biometric such as hand 

geometry could be used when performing one-to-one verification at ports 

of entry. We incorporated this idea into our report so that hand 

geometry is now listed as a viable technology for border security.



On the subject of biometric standards, reviewers commented that in 

order to avoid being tied to a vendors proprietary biometric template 

format, any biometric system has to store the original biometric 

images. Reviewers also suggested that we include ANSIs Biometric 

Information Management and Security (ANSI X9.84-2001) standard in the 

report and the AAMVA drivers license standard that includes 

biometrics. Reviewers also stated we should mention the ongoing 

biometric standards activities of the InterNational Committee for 

Information Technology Standards Technical Committee M1, Biometrics, 

which was established in November 2001. We incorporated these 

suggestions into the report.



Some reviewers expressed concern that using biometrics as suggested in 

the scenarios would be ineffective in preventing terrorists from 

entering the United States. Some reviewers believed that the report 

needs to better emphasize the limitations and operational concerns 

associated with biometrics. For example, reviewers suggested that we 

highlight the fact that adding biometrics to a travel document can bind 

only a persons claimed identity to the document. Additionally, the 

claimed identity is only as good as the credentials that a person uses 

to claim that identity. They also mentioned that biometric systems are 

not perfect and that operational procedures must address weaknesses in 

any system implementation.



We state that the goals of any biometric system for border security 

need to be defined before any decision to design, develop, and 

implement it. We describe a risk-based approach to security that can 

help with the definition of goals. Part of this approach is an 

identification of adversaries and threats. Regardless of whether 

terrorists are considered the only adversary to border security, a 

vulnerability analysis is required to determine how an adversary can 

illegally enter the country. As we state in the report, biometric 

technologies are not a panacea for border security problems. Technology 

and people must work together to execute border security processes. As 

reviewers have pointed out, increasing security at the ports of entry 

does not address problems with people illegally crossing into the 

United States at points other than official ports of entry. Biometric 

technologies can only help support a single task, the binding of an 

identity to an individual. Numerous other technologies and people are 

needed to support other border security processes that, together as a 

whole, protect the border. We have further adjusted the report, where 

appropriate, to make this point clear.



Reviewers commented that our report does not take a sufficiently 

forward-looking approach to the civil liberties problems. The reviewers 

believed that the report appears to downplay significant issues with 

the effectiveness of biometric technologies and the significant civil 

liberties issues surrounding the use of biometrics for border control. 

These reviewers suggested that civil liberties issues need to be better 

addressed, including the potential for unauthorized access to data, 

abuse by those with authorized access, bad data in the system, the 

consequences of false matches on individuals, the need for greater 

transparency, and the dangers of racial or other profiling.



In the report, we summarize guidelines for addressing privacy in 

biometric systems. Similar to the need to define the security goals of 

a biometric border control system, there is a need to define the 

privacy requirements for the system. The guidelines can help decision 

makers develop a policy consensus on the amount of privacy to build 

into such a system. As we point out, many of the issues surrounding the 

implementation of privacy are not technical issues. Instead, they 

surround the management policies governing the use of the technology 

and the information generated by such a system.



We also received numerous technical comments on topics such as the 

specific enrollment numbers for biometric applications, template sizes, 

the maturity of new technologies, and equipment costs. We have 

incorporated these comments, where appropriate, in the report.



[End of section]



Appendix I Our Technology Assessment Methodology:



The objectives of this technology assessment were to:



12.	 Identify biometric technologies currently deployed, currently 

available but not yet deployed, or in development that could be 

deployed in the foreseeable future for use in securing the nations 

borders.



13.	 Determine how effective these technologies are for helping provide 

security to our borders currently or are likely to be in the future.



14.	 Determine the economic and effectiveness trade-offs of implementing 

these technologies.



15.	 Identify the implications of biometric technologies for personal 

security and the preservation of individual liberties.



To identify and describe biometric technologies, we convened, with the 

assistance of the National Academy of Sciences (NAS), two meetings on 

biometrics and border control issues, which included manufacturers of 

facial, fingerprint, and iris recognition and hand geometry 

technologies.[Footnote 38] The meetings also included informed 

representatives from academia, government, and industry groups; privacy 

and civil liberty advocates; and other stakeholders such as 

representatives of border communities and trade organizations. We 

interviewed manufacturers of biometric technologies and reviewed their 

publications to obtain descriptive information about their equipment. 

We interviewed officials from biometric industry organizations, 

including the Biometric Consortium and the Biometric Foundation. We 

also interviewed the consulting firm the International Biometric Group 

(IBG). We attended the biometrics session of the International 

Industrial Security Conference, where technologies were demonstrated, 

and we discussed various aspects of the technologies with industry 

representatives.



To identify the current deployment of biometric technologies, we 

conducted a literature search and reviewed reports of deployments, 

tests, and pilots of biometric technologies. We interviewed certain 

users of biometric technologies, including the Federal Bureau of 

Investigation (FBI), Immigration and Naturalization Service (INS), 

National Security Agency (NSA), National Institute of Standards and 

Technology (NIST), the Department of State, and the Canada Customs and 

Revenue Agency.



To determine how effective the technologies would be in helping to 

secure the nations borders, we needed to understand the current border 

security environment. We reviewed relevant statutes and regulations and 

interviewed State Department and INS officials at headquarters and INS 

officials at three ports of entry: El Paso, Texas (land); Miami, 

Florida (air and sea); and Niagara Falls, New York (land). We reviewed 

and analyzed statistics from INSs Performance Analysis System for 

fiscal year 2001.



To determine the effectiveness of biometric technologies, we reviewed 

test documentation from academic, government, and industry sources. In 

particular, we interviewed and reviewed documentation from the 

Department of Defense (DOD), Federal Aviation Administration (FAA), 

INS, NIST, Sandia National Laboratories, the State Department, the 

United Kingdoms National Physical Laboratory (NPL), and IBG.



To determine the economic and effectiveness trade-offs of implementing 

biometric technologies, we identified four different scenarios for 

implementing them and built cost models to obtain life-cycle costs for 

each scenario. The cost models represent rough order of magnitude costs 

and are based on DODs cost element structure for major automated 

information systems. To build the cost models, we used data provided by 

the FBI, IBG, Naval Center for Cost Analysis, State Department, and 

various vendors. We reviewed the cost model and assumptions associated 

with each model with IBG and the State Department and incorporated 

their feedback where appropriate.



In addition, we performed uncertainty analysis on the cost models, 

using a Monte Carlo simulation tool called Crystal Ball to analyze the 

effects of varying inputs and outputs of the modeled scenarios. This 

allowed us to try multiple what-if scenarios with our spreadsheet cost 

model values and cells. We used the results of this analysis to provide 

a probability value for our point estimates, as well as to provide a 

risk-adjusted life-cycle cost estimate for each scenario. Crystal Ball 

examines the degree of risk in forecasts by using Monte Carlo 

simulation techniques that forecast all statistically possible results 

for a given situation. We applied a probability distribution to each 

parameter that we thought could vary, such as the costs for development 

and installation, annual operating personnel, and additional square 

feet in embassy or consular facilities. Then, Crystal Ball generated 

random values for each cell, according to the parameters we chose to 

represent the risk. The software displayed the distribution of results, 

showing the highest, lowest, and most likely values.



We analyzed the benefits of each scenario and described the effects on 

people and processes, based on our understanding of the technology and 

current border control processes.



To determine the implications of biometric technologies, we reviewed 

relevant statutes and regulations and interviewed officials from 

privacy and civil liberty groups. We also heard from representatives of 

these groups at our meetings convened by NAS. We met with the Greater 

El Paso Chamber of Commerce to obtain its thoughts on the introduction 

of biometrics and the potential economic effect in the El Paso area. We 

reviewed data from the Department of Commerce and Department of 

Transportation to determine the potential economic effect of 

implementing biometrics.



We provided a draft of this report to the Department of State and 

Department of Justice for their review. We include their comments in 

appendixes VII and VIII, respectively. In addition, we provided a draft 

of this report to selected attendees of the two meetings NAS convened 

for this work and other interested organizations.



Three recognized independent external reviewers reviewed our process 

for conducting our work. In addition to providing a sound analysis of 

this assessment, the reviewers made recommendations for improving and 

enhancing future assessments should the Congress ask us to do more in 

the future.



We conducted our work from March to October 2002 in Washington, D.C.; 

Clarksburg, West Virginia; El Paso, Texas; New York, New York; Niagara 

Falls, New York; Miami, Florida; and Philadelphia, Pennsylvania. We 

performed our work in accordance with generally accepted government 

auditing standards.



[End of section]



Appendix II Fingerprint Recognition Technology:



Fingerprint identification has two basic premises. The basic 

characteristics of fingerprints do not change with time--persistence--

and each persons fingerprints are unique--individuality. Scientific 

studies in the mid-1800s established the persistence of friction ridge 

patterns on human fingers, beginning in the embryonic stage and 

extending throughout life, except for accidental damage.



Manual inspection of millions of fingerprints has led to the widely 

accepted notion of fingerprint individuality. However, it has not been 

formally established by scientific means that a persons fingerprints 

are unique. Because it is impossible to obtain the fingerprints of 

every person in the world, estimating fingerprint individuality 

requires statistical methods to project the probability that two people 

will have the same fingerprint. The FBIs Integrated Automated 

Fingerprint Identification System (IAFIS) is the largest biometric 

database in the world with its 400 million fingerprints. Although the 

FBI has never discovered matching fingerprints from two individuals, 

tests have not been performed to conclusively verify that the 

fingerprints in IAFIS are unique.



In response to the need for a study to rigorously test the scientific 

basis of fingerprint individuality, the National Institute of Justice 

issued a formal solicitation in March 2000 for Forensic Friction Ridge 

(Fingerprint) Examination Validation Studies. The objectives were 

basic research to measure the amount of detail in a single fingerprint 

that can be used for comparison and the amount of similar detail 

between two separate fingerprints.



Fingerprint identification has been used in law enforcement over the 

past hundred years and has become the de facto international standard 

for positively identifying individuals. The FBI has been using 

fingerprint identification since 1928. The first fingerprint 

recognition systems were used in law enforcement about four decades 

ago. Advances in optical scanning technology since the 1980s have made 

the technology practical for noncriminal applications. Figures 26 

through 28 illustrate some current applications of fingerprint 

recognition technology.



Figure 26: Using Fingerprint Biometrics for Physical Access:



Source: National Coordination Office for Information Technology 

Research and Development.



[See PDF for image]



[End of figure]



Figure 27: Using Fingerprint Biometrics for Logical Access:



Source: Identix Incorporated.



[See PDF for image]



[End of figure]



Figure 28: A Fingerprint Biometric Device for Personal Identification:



Source: Sagem Morpho Inc.



[See PDF for image]



[End of figure]



The use of fingerprints for forensic evidence was challenged recently. 

In 1999, the defense in U.S. v. Mitchell pointed to the Daubert 

Opinion, established in a 1993 U.S. Supreme Court case, that prompted 

the scientific community to address questions about the reliability and 

validity of certain types of evidence, such as whether the evidence has 

been adequately tested, what its error rate is, and whether there are 

standards for what constitutes a fingerprint match.[Footnote 39] The 

U.S. Court of Appeals in U.S. v. Mitchell held that fingerprinting 

meets the necessary criteria for admissibility as evidence. More 

recently, in January 2002, in U.S. v. Llera Plaza, the judge refused to 

allow fingerprint experts to express an opinion that a particular 

latent print matched or did not match the rolled print of a particular 

person.[Footnote 40] However, in March 2002, the judge reversed himself 

and concluded that the courts use of expert fingerprint identification 

testimony, subject to careful trial court oversight, could be allowed.



How the Technology Works:



Fingerprint recognition technology uses the impressions made by the 

unique ridge formations or patterns found on the fingertips.[Footnote 

41] The technology uses two main types of fingerprints: flat and 

rolled. A flat fingerprint is obtained by pressing the finger flat 

against the scanner, capturing an impression of the central area 

between the fingertip and the first knuckle. A rolled fingerprint is 

obtained by rolling the finger from one edge of the fingernail across 

to the other, capturing an impression of the side ridges as well. A 

flat fingerprint is quicker to capture, but a rolled fingerprint can 

provide up to 50 percent more surface area for future comparisons.



Whether flat or rolled, the image of the fingerprint is commonly 

captured by a scanner based on optical, silicon, or ultrasound 

technology. Optical technology is the oldest and most widely used; it 

requires that the finger be placed on a coated platen, typically made 

of hard plastic. In most devices, a charged coupled device converts the 

image, with dark ridges and light valleys, into a digital signal. The 

brightness is adjusted automatically or manually to produce a usable 

image. Although most companies use optical technology, the trend is 

toward silicon.



One type of silicon technology is based on capacitance, where the 

silicon sensor typically acts as one plate of a capacitor and the 

finger is the other. The capacitance between the platen and the finger 

is converted into an 8 bit gray-scale digital image. Although 

ultrasound technology is potentially more accurate than either optical 

or silicon, its performance has not been assessed in widespread use. It 

captures the fingerprint by transmitting acoustic waves and measuring 

the distance by the impedance of the finger, the platen, and air.



After a fingerprint image has been captured, it is enhanced to reduce 

image noise, formed when a fingerprint is converted into a digital 

image; the noise distorts the image, generally as repetitive patterns 

or random dots. A fingerprint image is one of the noisiest of image 

types, predominantly because fingertips become dirty, cut, scarred, 

creased, dry, wet, and worn. Image enhancement reduces this noise and 

enhances the definition of ridges and valleys. To allow for precise 

locations of ridge features, ridges are thinned from an original width 

of 5 to 8 pixels down to 1 pixel.



For a gray-scale image, areas lighter than a set threshold may be 

discarded, while darker areas may be made black. Image enhancement is 

relatively time consuming, since a 500 x 500 pixel fingerprint image 

has 250,000 pixels and each pixel is enhanced. Consequently, many 

fingerprint systems are designed to limit enhancement operations at 

this stage in order to reach a match result quickly, trading faster 

match time for poorer image quality.



Following image enhancement, several steps are required to convert a 

fingerprints unique features into a template. Known as feature 

extraction, this is the basis of fingerprint recognition technology and 

the various vendors proprietary algorithms. We discuss the different 

algorithms below. In none of these methods is the template a full 

fingerprint image, and a real fingerprint cannot be recovered from the 

digitized template. The generated template ranges from 250 bytes for 

minutiae-based templates to about 1,000 bytes for ridge-pattern-based 

templates.



Minutiae-Based Templates:



Approximately 80 percent of fingerprint recognition vendors base their 

algorithms on minutiae points, or the breaks in fingertip ridges. A 

typical fingerprint image may produce between 15 and 50 minutiae, 

depending on the portion of the image captured. As shown in figure 29, 

the most basic minutiae are ridge endings (where a ridge ends) and 

bifurcations (where a single ridge divides into two).



Figure 29: Common Fingerprint Features:



Source: GAO adaptation of FBI data.



[See PDF for image]



[End of figure]



Before minutiae can be identified, an algorithm must search the 

processed fingerprint image and filter out distortions and false 

minutiae. False minutiae can be caused by scars, sweat, or dirt and 

often create anomalies that can typically be detected. For instance, 

minutiae that seem out of place could include two ridge endings on a 

very short isolated line; the line would probably stem from image 

noise. Similarly, endings at the boundary of the fingerprint would be 

eliminated because they are not true endings but, rather, the edge of 

the image captured by the scanning device. A large percentage of 

minutiae candidates are discarded this way.



Once the minutiae are identified, their locations are usually set on an 

x,y axis and their angles are measured (typically by the direction of a 

ridge or valley ending). For each established minutiae point, 

neighboring minutiae and the number of ridges in between are recorded. 

The result of this stage is a minutiae template of the fingerprint. 

Because of differences in the determination, placement, and analysis of 

minutiae points, no two algorithms can be expected to yield the same 

template from a given fingerprint.



In a verification system, templates are usually matched by comparing 

the neighborhoods of nearby minutiae for similarity. If a comparison 

indicates only small differences, the neighborhoods are said to match. 

Comparisons are performed exhaustively for all combinations of 

neighborhoods, and if enough similarities are found, the fingerprints 

are said to match. One result of this verification stage is a match 

score, usually a number between 0 and 1 (or 10 or 100). Higher values 

in the range indicate higher confidence in a match, and the match score 

is measured against a predetermined threshold. If the score is greater 

than the threshold, the match result is said to be true. The threshold 

can be lowered to reduce the number of false nonmatches, but the trade-

off is a greater number of false matches. Some systems score the 

difference between two templates, in which case a lower score is 

considered a match.



In an identification system, which compares a trial fingerprint 

template to an entire database, the verification technique described 

above would be impractical. Making comparisons to every fingerprint in 

the database by neighborhoods would lengthen computation time 

extensively. Instead, a two-step process is typically used for 1:N 

matching. First, to provide an indexing method, the trial fingerprint 

and the reference template in the database are categorized according to 

an established fingerprint type (such as the plain arch, loop, or plain 

whorl shown in figure 30). This step is called binning, in which a 

pattern comparison quickly eliminates the bulk of the nonmatches. Care 

must be taken in binning. Errors in assigning images to bin categories 

increase the likelihood of a false nonmatch.



Figure 30: Established Fingerprint Types:



Source: FBI



[See PDF for image]



[End of figure]





In the second step for 1:N matching in identification, the trial 

template is compared by minutiae neighborhood to each reference 

template that closely matches the trial template pattern.



Ridge-Pattern-Based Templates:



In matching ridge patterns, data are extrapolated from a particular 

series of ridges, to be used in enrollment for the basis of future 

comparisons. The ridge series are chosen so as to maximize the amount 

of unique information that is recorded--for example, those with an 

unusual ridge combination. At verification, a segment of the same area 

must be found and compared. The match result contains information on 

how well the stored images fit the verification image. This information 

is measured against a threshold to determine whether the match result 

is true.



The Leading Vendors:



Fingerprint recognition technology companies number more than 75. There 

are more fingerprint recognition vendors than for all other biometrics 

combined. Some of the leading companies are listed in table 20.



Table 20: Leading Vendors of Fingerprint Recognition Biometrics:



Vendor: ActivCard; Scanner for image capture: Optical: X; Scanner for 

image capture: Silicon: [Empty]; Scanner for image capture: Ultrasound: 

[Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Astro Datensysteme AG; Scanner for image capture: Optical: 

[Empty]; Scanner for image capture: Silicon: X; Scanner for image 

capture: Ultrasound: [Empty]; Scanner for image capture: Other[A]: 

[Empty].



Vendor: AuthenTec Inc.; Scanner for image capture: Optical: [Empty]; 

Scanner for image capture: Silicon: X; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Bergdata Biometrics GmbH; Scanner for image capture: Optical: 

[Empty]; Scanner for image capture: Silicon: X; Scanner for image 

capture: Ultrasound: [Empty]; Scanner for image capture: Other[A]: 

[Empty].



Vendor: BIO-key International; Scanner for image capture: Optical: 

[Empty]; Scanner for image capture: Silicon: [Empty]; Scanner for image 

capture: Ultrasound: [Empty]; Scanner for image capture: Other[A]: X.



Vendor: BioLink Technologies International Inc.; Scanner for image 

capture: Optical: X; Scanner for image capture: Silicon: [Empty]; 

Scanner for image capture: Ultrasound: [Empty]; Scanner for image 

capture: Other[A]: [Empty].



Vendor: Biometric Access Corporation; Scanner for image capture: 

Optical: X; Scanner for image capture: Silicon: [Empty]; Scanner for 

image capture: Ultrasound: [Empty]; Scanner for image capture: 

Other[A]: [Empty].



Vendor: Bioscrypt Inc.; Scanner for image capture: Optical: X; Scanner 

for image capture: Silicon: X; Scanner for image capture: Ultrasound: 

[Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Cogent Systems Inc.; Scanner for image capture: Optical: X; 

Scanner for image capture: Silicon: [Empty]; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Cross Match Technologies Inc.; Scanner for image capture: 

Optical: X; Scanner for image capture: Silicon: [Empty]; Scanner for 

image capture: Ultrasound: [Empty]; Scanner for image capture: 

Other[A]: [Empty].



Vendor: Delsy; Scanner for image capture: Optical: [Empty]; Scanner for 

image capture: Silicon: X; Scanner for image capture: Ultrasound: 

[Empty]; Scanner for image capture: Other[A]: X.



Vendor: DigitalPersona Inc.; Scanner for image capture: Optical: X; 

Scanner for image capture: Silicon: [Empty]; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Ethentica; Scanner for image capture: Optical: [Empty]; Scanner 

for image capture: Silicon: [Empty]; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: X.



Vendor: Fingerprint Cards AB; Scanner for image capture: Optical: 

[Empty]; Scanner for image capture: Silicon: X; Scanner for image 

capture: Ultrasound: [Empty]; Scanner for image capture: Other[A]: 

[Empty].



Vendor: Identix Inc.; Scanner for image capture: Optical: X; Scanner 

for image capture: Silicon: [Empty]; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Infineon Technologies AG; Scanner for image capture: Optical: 

[Empty]; Scanner for image capture: Silicon: X; Scanner for image 

capture: Ultrasound: [Empty]; Scanner for image capture: Other[A]: 

[Empty].



Vendor: Polaroid Corp.; Scanner for image capture: Optical: X; Scanner 

for image capture: Silicon: [Empty]; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Precise Biometrics; Scanner for image capture: Optical: 

[Empty]; Scanner for image capture: Silicon: X; Scanner for image 

capture: Ultrasound: [Empty]; Scanner for image capture: Other[A]: 

[Empty].



Vendor: SAGEM Morpho Inc.; Scanner for image capture: Optical: X; 

Scanner for image capture: Silicon: [Empty]; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: SecuGen Corp.; Scanner for image capture: Optical: X; Scanner 

for image capture: Silicon: [Empty]; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Siemens AG; Scanner for image capture: Optical: [Empty]; 

Scanner for image capture: Silicon: X; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Sony Corp.; Scanner for image capture: Optical: X; Scanner for 

image capture: Silicon: X; Scanner for image capture: Ultrasound: 

[Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: STMicroelectronics; Scanner for image capture: Optical: 

[Empty]; Scanner for image capture: Silicon: X; Scanner for image 

capture: Ultrasound: [Empty]; Scanner for image capture: Other[A]: 

[Empty].



Vendor: Thales; Scanner for image capture: Optical: [Empty]; Scanner 

for image capture: Silicon: X; Scanner for image capture: Ultrasound: 

[Empty]; Scanner for image capture: Other[A]: [Empty].



Vendor: Ultra-Scan Corp.; Scanner for image capture: Optical: [Empty]; 

Scanner for image capture: Silicon: [Empty]; Scanner for image capture: 

Ultrasound: X; Scanner for image capture: Other[A]: [Empty].



Vendor: Veridicom Inc.; Scanner for image capture: Optical: [Empty]; 

Scanner for image capture: Silicon: X; Scanner for image capture: 

Ultrasound: [Empty]; Scanner for image capture: Other[A]: [Empty].



[A] Includes middleware and emerging scan technologies that use polymer 

or fiber optic readers.



Source: GAO analysis of vendor data.



[End of table]



The Cost of Devices:



Fingerprint readers designed for physical access control range from 

about $1,000 to $3,000 per unit. Software licenses for the fingerprint 

technology are about $4 per user enrolled. For smaller fingerprint 

scanners, maintenance costs are between 15 percent and 18 percent of 

cost. A live scan 10-print fingerprint reader costs about $25,000. The 

maintenance cost of the larger machines is approximately 14 percent of 

the cost of the reader.



Performance Issues:



Although fingerprints are stable physiological characteristics, daily 

wear can cause the performance of some fingerprint recognition 

technologies to drop drastically. Although high-quality enrollment 

improves long-term performance, the fingerprints of about 2 to 5 

percent of people cannot be captured because the fingerprints are dirty 

or have become dry or worn from age, extensive manual labor, or 

exposure to corrosive chemicals. Also, IBGs comparative biometric 

testing has shown that certain ethnic and demographic groups (elderly 

populations, manual laborers, and some Asian populations) have 

fingerprints that are more difficult to capture than others.



Optical and silicon scanning technologies have unique performance 

issues. Scanning fingerprints optically can be prone to error if the 

platen has a buildup of dirt, grime, or oil--producing leftover 

fingerprints from previous users, known as latent prints. Severe latent 

prints can cause the superimposition of two sets of prints and image 

degradation. Although silicon scanners generally produce a higher-

quality image, high-quality fingerprint capture is more difficult 

because the sensor size is smaller than that used in optical scanners. 

Ultrasound scanning technology is designed to penetrate the dirt and 

residue on platens.



Optical and silicon scanners using minutiae-based and pattern-matching 

technologies have been tricked into accepting reactivated latent prints 

or artificial fingers with forged fingerprints. Latent fingerprints 

were reactivated by simply breathing on the sensor or by placing a 

water-filled plastic bag on the sensors surface. Latent fingerprints 

could also be reconstructed and authenticated by dusting the sensors 

platen with commercially available graphite powder and lifting with 

adhesive tape. Artificial fingers made with candle wax or gelatin and 

the fingerprints of enrolled individuals have also successfully fooled 

the system.



User Acceptance:



Because law enforcement agencies identify criminals with fingerprints, 

the recognition technologys similarity to forensic fingerprinting 

causes some percentage of users discomfort. Privacy advocates fear that 

fingerprint recognition systems may collect data for one purpose but 

then use the data for other purposes, such as in forensic applications 

or for tracking peoples activities. Also, people may have hygiene 

issues with touching the plate of a scanner that many people have 

touched.



The Technologys Maturity:



Operational Uses:



The FBIs IAFIS is an automated 10-fingerprint matching system that 

relies on rolled fingerprints. The more than 40 million records in its 

criminal master file are connected electronically with all 50 states 

and some federal agencies. IAFIS was designed to handle a large volume 

of fingerprint checks against a large database of fingerprints. It 

processes, on average, approximately 48,000 fingerprints per day and 

has processed as many as 82,000 in a single day. IAFISs target 

response time for criminal fingerprints submitted electronically is 2 

hours; for civilian fingerprint background checks, 24 hours. According 

to FBI data from August 2002, the majority of criminal fingerprints 

were answered in less than 7 minutes and the majority of civilian 

fingerprints were answered in less than 4 minutes; 88.2 percent of 

criminal prints and 66.1 percent of civilian prints were completed in 

less than 2 hours. For fingerprint submissions in paper-card format, 

the response time is 3 days between receipt and mailed-back response. 

The FBI claims that IAFIS has a false match rate (FMR) of about 1.5 x 

10-12 with a false nonmatch rate (FNMR) of about 1.5 to 2.0 percent. 

The failure to enroll rate (FTER) is about 0.5 percent for criminal 

searches and about 2.5 percent for civilian background searches.



INS began developing the Automated Biometric Fingerprint Identification 

System (IDENT) around 1990 to identify illegal aliens who are 

repeatedly apprehended trying to enter the United States illegally. 

INSs goal was to enroll virtually all apprehended aliens. IDENT can 

also identify aliens who have outstanding warrants or who have been 

deported. When such aliens are apprehended, a photograph and two index 

fingerprints are captured electronically and queried against three 

databases (see figure 31). One database stores the fingerprints and 

photographs of approximately 300,000 aliens INS has previously 

apprehended; it tracks the number of apprehensions. The second database 

stores the fingerprints and photographs of approximately 240,000 

criminal aliens convicted of an aggravated felony, among other 

criteria. The third database stores the fingerprints and photographs of 

more than 4 million illegal aliens who were apprehended, enrolled in 

IDENT, and then permitted to voluntarily depart the United States or to 

withdraw their applications for admission at ports of entry. The 

fingerprint query of the three databases normally takes 2 minutes. In 

March 2002, the FMRs for the four fingerprint search types were 4.05 

percent for flat to flat, 2.60 percent for flat to roll, 0.70 percent 

for roll to roll, and 1.19 percent for roll to flat.



Figure 31: An IDENT Workstation:



Source: INS.



[See PDF for image]



[End of figure]



A number of states (including Arizona, California, Connecticut, 

Illinois, Massachusetts, New Jersey, New York, and Texas) require 

applicants for welfare benefits to submit their fingerprints in order 

to eliminate duplicate participation and to deter fraud. The first 

social service fingerprint recognition system in the nation was the Los 

Angeles County Automated Fingerprint Image Reporting and Match system, 

which enrolled 311,000 clients between 1992 and 1995. On the basis of a 

study group of 24,000 fingerprint recipients, it was determined that 

about 7 percent of the cases on the benefit rolls in Los Angeles were 

multiple identities. As of January 2001, this was the only substantial 

finding of multiple identity fraud in any of the various state welfare 

fingerprint programs.



Since January 31, 2002, immigrants seeking asylum in the United Kingdom 

are issued an application registration card to allow for quick and 

positive identification of all asylum applicants. The smart cards, 

manufactured by SAGEM Morpho Inc., store two fingerprint templates on a 

memory chip. An extension of the Immigration and Asylum Fingerprint 

System (IAFS), they are intended to prevent fraudulent behavior, such 

as impersonations to avoid removal or to make false benefits claims. 

The United Kingdom plans to adopt the Eurodac Convention and Protocol, 

which assists European Union members in applying the provisions of the 

Dublin Convention, a framework for ensuring that an asylum claim is 

heard within the European Union only once. Once the United Kingdom has 

adopted the Eurodac Convention and Protocol, British IAFS fingerprint 

data will be transmitted electronically to a central database, 

accessible to other members for fingerprint comparisons. The data will 

be retained for 10 years, with the exception of fingerprints from 

asylum seekers who are granted citizenship by European Union members. 

The prints of these new citizens will be immediately erased.



Pilots:



FAA Fingerprint Recognition Testing:



In 2001, FAA conducted operational testing of a fingerprint recognition 

reader for access control by different groups of people in various 

operating environments. Following the test, the biometric system was 

removed. Enrolling users in the fingerprint reader system took an 

average of 3 minutes and 30 seconds. Two of 38 users were unable to 

enroll because of the poor quality of their fingerprints. Passing 

through the door took about 10 seconds using fingerprint recognition, 

compared to an average of about 2 seconds before the device was 

installed. Performance rates varied at the three different security-

level thresholds tested. The FNMR ranged from about 6 percent to about 

17 percent for closely controlled test subjects. For actual airport 

employees using the door in a less controlled environment, the FNMR 

ranged from about 18 percent to about 36 percent. The FMR ranged from 0 

percent at the highest security level to approximately 8 percent at the 

lowest security level.



OHare International Airport, Chicago:



In 1998, FAA funded an operational test at Chicagos OHare 

International Airport involving smart cards and fingerprint recognition 

identification devices to screen employees of motor carrier and air 

cargo companies at access control points to cargo areas. Truck drivers 

were instructed to insert a smart card into the smart card reader and 

to confirm their identity by placing the enrolled fingers on the 

fingerprint reader.[Footnote 42] Fingerprints were chosen over other 

biometrics because of the users operational requirements, the 

perception that fingerprint recognition was one of the least intrusive 

technologies, and the results from a 1997 study that determined that 

fingerprints could be used in a variety of applications in the trucking 

industry.



Because all users verification attempts were voluntary, only some of 

the users who were initially rejected by the system chose to try again. 

For 65 users, the first-try FNMR was 28 percent. Of the seven users who 

chose to try again, 71 percent successfully accessed the system. If all 

rejected users had retried with this rate of success, only 8 percent of 

the users would have been rejected after two tries.[Footnote 43] 

Testing of impostors fingerprints against the operational database was 

not performed, so an FMR could not be obtained for the OHare 

databases.



Tests:



Fingerprint Verification Competition 2000:



The Fingerprint Verification Competition 2000 (FVC 2000) tested 

relative technology performance in a one-to-many application and was 

not intended to predict performance of fingerprint recognition 

technology in a real environment. Eleven algorithms were submitted, 

seven from academic groups and four from companies (one from Ditto 

Information & Technology Inc., one from FingerPin AG, and two from 

SAGEM Morpho Inc.). Three databases in this competition were acquired 

in a laboratory environment, using a variety of sensors (both optical 

and silicon), while the fourth contained synthetically generated 

images. Enrollment time averaged 0.20 to 10.42 seconds, 10 of the 

algorithms requiring no more than 3.18 seconds. Matching time averaged 

0.20 to 2.67 seconds, 9 of the algorithms requiring no more than 1.58 

seconds. The most accurate algorithm had an average equal error rate 

(EER) of 1.73 percent, while the least accurate algorithm had an 

average EER of 47.84 percent. These data are depicted in table 21.



Table 21: Summary of Results from the Fingerprint Verification 

Competition 2000:



Participant: SAGEM SA, France (Algorithm 1); Type: Company; Average 

EER: 1.73%; Average enroll time: 3.18; Average match time: 1.22.



Participant: SAGEM SA, France (Algorithm 2); Type: Company; Average 

EER: 2.28; Average enroll time: 1.11; Average match time: 1.11.



Participant: Centre for Signal Processing, Nanyang Technological 

University, Singapore; Type: Academic; Average EER: 5.19; Average

enroll time: 0.20; Average match time: 0.20.



Participant: CEFET-PR/Antheus Technologia LTDA., Brazil; Type: 

Academic; Average EER: 6.32; Average enroll time: 0.95; Average

match time: 1.06.



Participant: Centre for Wavelets, Approximation, and Information 

Processing, Department of Mathematics, National University of 

Singapore, Singapore; Type: Academic; Average EER: 7.08; Average

enroll time: 0.27; Average match time: 0.35.



Participant: Kent Ridge Digital Labs, Singapore; Type: Academic; 

Average EER: 10.94; Average enroll time: 1.08; Average match time: 

1.58.



Participant: University of Twente, Electrical Engineering, 

Netherlands; Type: Academic; Average EER: 15.24; Average

enroll time: 10.42; Average match time: 2.67.



Participant: FingerPin AG, Switzerland; Type: Company; Average EER: 

15.94; Average enroll time: 1.22; Average match time: 1.27.



Participant: Inha University, Korea; Type: Academic; Average EER: 

19.33; Average enroll time: 0.71; Average match time: 0.76.



Participant: Ditto Information & Technology Inc., Korea; Type: Company; 

Average EER: 20.97; Average enroll time: 1.24; Average match time: 
1.32.



Participant: Natural Sciences and Mathematics, Institute of 

Informatics, Macedonia; Type: Academic; Average EER: 47.84; Average

enroll time: 1.44; Average match time: 1.71.



Note: Time is in seconds.



Source: FVC 2000.



[End of table]



Fingerprint Verification Competition 2002:



Researchers from University of Bologna, Italy; San Jose State 

University, California; and Michigan State University, East Lansing; 

jointly conducted Fingerprint Verification Competition 2002 (FVC 2002), 

a large-scale evaluation of fingerprint recognition technology that was 

a follow-up to FVC 2000. There were 31 participants--21 from companies, 

6 from academic institutions, and 4 others.  As in FVC 2000, three 

databases were acquired in a laboratory environment, using both optical 

and silicon sensors, and a fourth contained synthetically generated 

images. 



Enrollment time averages ranged from 0.11 to 7.05 seconds, 24 of the 

participants requiring no more than 1 second. Matching time averages 

ranged from 0.18 to 5.01 seconds, 21 of the participants requiring no 

more than 1 second. The most accurate algorithm had an average EER of 

0.19 percent, while the least accurate algorithm had an average EER of 

50 percent. Table 22 depicts these data.



Table 22: Summary of Results from the Fingerprint Verification 

Competition 2002:



Participant: Bioscrypt Inc., United States (Algorithm 1); Type: 

Industrial; Average EER: 0.19%; Average enroll time: 0.11; Average

match time: 1.97.



Participant: Anonymous; Type: Industrial; Average EER: 0.33; Average

enroll time: 2.12; Average match time: 1.98.



Participant: Anonymous; Type: Industrial; Average EER: 0.41; Average

enroll time: 1.23; Average match time: 1.13.



Participant: Bioscrypt Inc., United States (Algorithm 2); Type: 

Industrial; Average EER: 0.77; Average enroll time: 0.07; Average 

match time: 0.22.



Participant: Siemens AG, Germany; Type: Industrial; Average EER: 0.92; 

Average enroll time: 0.48; Average match time: 0.52.



Participant: Neurotechnologija Ltd., Lithuania; Type: Industrial; 

Average EER: 0.99; Average enroll time: 0.56; Average match time: 

0.56.



Participant: SAGEM, France (Algorithm 1); Type: Industrial; Average 

EER: 1.18; Average enroll time: 4.05; Average match time: 1.65.



Participant: Andrey Nikiforov (Independent Developer), United States; 

Type: Other; Average EER: 1.31; Average enroll time: 0.81; Average

match time: 1.23.



Participant: SAGEM, France (Algorithm 2); Type: Industrial; Average 

EER: 1.42; Average enroll time: 0.77; Average match time: 0.66.



Participant: Deng Guoqiang (Independent Developer), China; Type: Other; 

Average EER: 2.18; Average enroll time: 0.17; Average match time: 0.48.



Participant: IDENCOM AG, Switzerland; Type: Industrial; Average EER: 

2.22; Average enroll time: 0.52; Average match time: 0.62.



Participant: Suprema Inc., Korea; Type: Industrial; Average EER: 2.50; 

Average enroll time: 0.54; Average match time: 0.63.



Participant: Anonymous; Type: Industrial; Average EER: 3.31; Average

enroll time: 0.53; Average match time: 0.65.



Participant: Biometrics System Lab, Beijing University of Posts and 

Telecommunications, China; Type: Academic; Average EER: 3.76; Average

enroll time: 0.57; Average match time: 0.59.



Participant: Anonymous; Type: Industrial; Average EER: 4.19; Average

enroll time: 0.18; Average match time: 0.18.



Participant: HZMS Biometrics Co. Ltd., China; Type: Other; Average EER: 

4.24; Average enroll time: 0.65; Average match time: 0.66.



Participant: ActivCard Canada, Canada; Type: Industrial; Average EER: 

5.21; Average enroll time: 0.68; Average match time: 1.76.



Participant: Antheus Tecnologia Ltda, Brazil; Type: Industrial; Average 

EER: 5.46; Average enroll time: 0.20; Average match time: 0.54.



Participant: TeKey Research Group, Israel; Type: Industrial; Average 

EER: 5.72; Average enroll time: 0.01; Average match time: 3.15.



Participant: FINGERPIN AG, Switzerland; Type: Industrial; Average EER: 

6.05; Average enroll time: 0.48; Average match time: 0.77.



Participant: Inha University, Korea; Type: Academic; Average EER: 6.07; 

Average enroll time: 0.80; Average match time: 0.84.



Participant: Aldebaran Systems, United States; Type: Industrial; 

Average EER: 6.16; Average enroll time: 1.81; Average match time: 1.81.



Participant: Digital Fingerpass Corporation, China; Type: Industrial; 

Average EER: 6.40; Average enroll time: 0.49; Average match time: 0.50.



Participant: DATAMICRO Co. Ltd., Russia; Type: Industrial; Average EER: 

6.72; Average enroll time: 0.33; Average match time: 0.56.



Participant: Anonymous; Type: Industrial; Average EER: 7.12; Average

enroll time: 0.24; Average match time: 0.28.



Participant: Department of Computer Science and Information 

Engineering, Da-Yeh University, Taiwan; Type: Academic; Average EER: 

9.04; Average enroll time: 0.13; Average match time: 0.15.



Participant: Anonymous; Type: Industrial; Average EER: 12.09; Average

enroll time: 0.68; Average match time: 0.70.



Participant: AILab, Institute of Automation, The Chinese Academy of 

Sciences, China; Type: Academic; Average EER: 14.66; Average

enroll time: 0.57; Average match time: 0.65.



Participant: University of Tehran, Electrical and Computer Department, 

Iran; Type: Academic; Average EER: 16.79; Average

enroll time: 1.16; Average match time: 1.19.



Participant: Anonymous; Type: Other; Average EER: 39.10; Average

enroll time: 0.52; Average match time: 0.63. 



Participant: Anonymous; Type: Academic; Average EER: 50.00; Average

enroll time: 7.05; Average match time: 5.01.



Note: Time is in seconds.



Source: FVC 2002.



[End of table]



Biometric Product Testing:



NPL conducted a performance evaluation of seven different biometric 

systems from May to December 2000. The fingerprint part of the test 

included two types of systems, one based on optical technology and the 

other on silicon technology. The vendor of the silicon sensor was 

VeriTouch Ltd., with alternative enrollment and matching algorithms 

provided by Infineon Technologies AG. The vendor of the optical sensor 

was not identified.



The silicon systems FTER was 1.0 percent, the optical systems 2.0 

percent. FMR and FNMR measure the accuracy of the matching process. 

Adjusting the decision criteria can make for a trade-off between false 

match and false nonmatch errors. At an FMR of about 2 percent, the FNMR 

was about 4.3 percent for the silicon sensor with the alternative 

algorithm. Additional experimental results are summarized below:



* The silicon sensor system had a mean transaction time of 19 seconds, 

a median of 15 seconds, and a minimum of 9 seconds. The optical 

fingerprint system had a mean transaction time of 9 seconds, a median 

of 8 seconds, and a minimum of 2 seconds.



* The silicon sensor system could make 60 matches per minute, the 

alternative algorithm 2,500 matches per minute. The optical system 

could make only 50 matches per minute. These diagnostic programs had 

significant overheads, so the matching algorithm may be significantly 

faster than the results showed, perhaps by a factor exceeding 100.



* For both the silicon sensor and the optical system, younger people 

generally had a lower FNMR than older people, and the FNMR for attempts 

made immediately following enrollment were lower than those made on 

second or third visits.



Republic of the Philippines Social Security System:



Identification Card Benchmark Test:



In May 1997, the U.S. National Biometric Test Center at San Jose State 

University conducted an automated fingerprint identification system 

(AFIS) benchmark test for the Republic of the Philippines Social 

Security System Identification Card Project. The test measured single 

comparison FMRs and FNMRs (among other metrics) for each of the four 

participating international AFIS vendors. The FMR and FNMR for each 

vendor were determined by matching 4,128 test images against a database 

of 4,080 fingerprints. Flat prints of the thumb through the ring finger 

of each hand were collected from adult employee volunteers in the 

Social Security System. One vendor returned 16 million cross 

comparisons with only one false match, indicating a 95 percent 

statistical confidence in an FMR of fewer than 3 in 10 million prints 

but with an FNMR approaching 20 percent. Statistical analysis of the 

test results supports the feasibility of an AFIS system that could 

support 16 million flat fingerprint comparisons without a false match.



INSs IDENT Benchmark Test:



In June 1998, an independent verification and validation test was 

conducted on the Cogent PMA3 Matcher configuration that was later 

installed for IDENT. This benchmark test used a fingerprint test 

database created by INS and provided to Cogent Systems Inc. that 

consisted of 129,712 rolled fingerprints, 951,956 flat fingerprints, 

and six different search fingerprint image input files. The data were 

highly representative of IDENT criminal alien records at the time. As 

about half of the search subjects in the input files had mates in the 

rolled or flat fingerprint database, the benchmark data were designed 

to obtain results with a high confidence level.



All four types of searches (flat-flat, roll-flat, flat-roll, and roll-

roll) in operation in IDENT were tested during the benchmark. Two 

verification match tests (flat-flat and flat-roll) were also conducted. 

The results are displayed in table 23.



Table 23: INSs IDENT Fingerprint Benchmark Test Results, 1998:



Match test: Flat to flat; Match test: 0.4%; Identification: FMR: 5.4%; 

[Empty]; Verification: FNMR: 0; Verification: FMR: 0.1%.



Match test: Roll to flat; Match test: 8.4; Identification: FMR: 1.5; 

[Empty]; Verification: FNMR: a; Verification: FMR: a.



Match test: Flat to roll; Match test: 7.3; Identification: FMR: 0.2; 

[Empty]; Verification: FNMR: 1.6%; Verification: FMR: 0.1.



Match test: Roll to roll; Match test: 0.2; Identification: FMR: 0.1; 

[Empty]; Verification: FNMR: a; Verification: FMR: a.



[A] Data not available.



Source: GAO analysis of INS data.



[End of table]



The results of the flat-to-flat and roll-to-roll search test were more 

accurate than those of the mixed media tests--roll-to-flat and flat-to-

roll--because the mixed media searches resulted in a higher FNMR. The 

results of the verification match test supported the use of the 

algorithm for future INS verification applications.



Border Control Applications Piloted and Deployed:



CANPASS-Airport:



The CANPASS-Airport pilot at Vancouver International Airport was 

initiated in October 1995 using both fingerprint recognition and hand 

geometry technologies. The pilot used identity cards and biometric 

identification devices to allow previously screened travelers to bypass 

customs and immigration lines. Qualified Canadian and U.S. residents 

entered Canada through a special line by opening an automated gate with 

an encoded identification card and providing a fingerprint or hand 

geometry biometric for one-to-one authentication. Roughly a thousand 

travelers registered with CANPASS in the pilots first 7 months, with 

an average enrollment time of 15 minutes. Of 1,385 authentication 

attempts, 87 percent were successful and 13 percent were falsely 

rejected by the technology and had to be processed manually. On the 

basis of these results, authorities decided to use solely hand geometry 

for CANPASS-Airport.



Border Biometric Program and Border Crossing Card:



The biometric border crossing card project is a joint effort of the 

Department of State and INS to replace the paper-based card previously 

issued to Mexican citizens. The new card is a laser visa, a credit-

card-like document, that permits the holder to enter the United States 

without being issued further documentation for business or pleasure and 

to stay for 72 hours or less, going no farther than 25 miles from the 

border. With additional documentation, the laser visa can permit longer 

stays and travel farther than 25 miles from the border. The laser visas 

are manufactured by LaserCard Systems Corp. and are made of 

polycarbonate material with a rectangular strip of optical memory for 

data storage. The cards store a frontal facial image and the templates 

of two index flat fingerprints. More than 5 million cards have been 

issued, but the performance of the fingerprint recognition technology 

has not been measured, because ports of entry have no scanners for 

reading travelers fingerprints and matching them with the information 

on the laser visa. INS is buying and installing 30 readers at six ports 

of entry for a pilot test.



Hong Kong Resident Smart Cards:



About 250,000 people cross the Hong Kong-Shenzhen border daily, causing 

long lines at the immigration checkpoint. The Hong Kong government 

plans to issue new identity smart cards to residents in 2003. The smart 

cards will hold a template of a rolled fingerprint to be matched 

against the bearer at a self-service kiosk. The $21 million smart card 

contract was awarded in March 2002, and distribution to the 6.8 million 

Hong Kong residents will be phased in over 4 years.



Processing Issues:



The size of an identification systems projected database has a 

significant effect on the systems configuration and cost. The larger 

the database, the more storage devices are required. In addition, it 

takes longer to search a larger database unless matching processor 

power is also increased. Database size can also affect a systems 

accuracy. Some matching algorithms are effective only with relatively 

small databases and are simply not capable of accurate matching against 

the larger numbers of records found in forensic automated fingerprint 

identification systems.



Device Durability and Environmental Constraints:



Capturing fingerprints has been a significant issue in border control 

pilots. In an unattended environment, trained users have generally 

skewed fingers or have not pressed hard enough on the platen. The 

difficulty of acquiring a usable fingerprint after three attempts has 

resulted in approximately a 50 percent rejection rate. In addition, 

fingerprint readers do not operate below freezing temperature, so the 

issues of freezing and condensation are significant in selecting 

biometric systems.



[End of section]



Appendix III Hand Geometry Technology:



Patents for hand geometry technology were first issued in the late 

1960s and early 1970s. The technology is based on the premise that the 

hands bone structure, while changing over time, remains 

characteristically the same. The hands shape usually stabilizes at age 

13 or 14.



How the Technology Works:



Hand geometry technology uses the hands distinctive features, 

particularly the height and width of its back and fingers, to verify a 

persons identity. In measuring size and shape, a hand geometry system 

collects more than 90 dimensional measurements, including finger width, 

height, and length; distances between joints; and knuckle shapes. 

Although the shape and size of our hands are reasonably diverse, they 

are not necessarily unique. In larger populations, for example, it is 

almost certain that various people have very similar hand dimensions. 

Consequently, the technology cannot be used for 1:N identification.



In the measurement of the different features, a person places his or 

her hand flat on the devices metal surface, where pegs guide the 

fingers into position. Hand geometry systems require a person to 

squeeze his or her fingers against the pegs to prevent spoofing. 

Cameras capture two orthogonal two-dimensional images of the back and 

sides of the hand (see figure 32).



Figure 32: Fingers Guided by Pegs in a Biometric Hand Geometry 

Measurement:



Source: Michigan State University, Biometrics Research Group.



[See PDF for image]



[End of figure]



Only the spatial geometry is acquired; prints of the palm and fingers 

are not taken. The derived template is 9 bytes in size, the smallest in 

the biometric industry. In a process known as template averaging, the 

template is automatically updated whenever the difference between the 

individuals hand and his or her reference template exceeds a 

designated threshold.



The Leading Vendors:



Recognition Systems Inc. (RSI) dominates the market in hand geometry 

technology. Its systems are used in nearly every current 

implementation. Companies that integrate hand geometry technologies 

include Electronic Data Systems Corp. and ADT. However, Dermalog, a 

German company, is developing an alternative technology that uses a 

pegless device. Biomet Partners, a Swiss company, sells a finger 

geometry device that operates on the same basic principles as the RSI 

hand geometry devices.



The Cost of Devices:



Hand geometry devices generally cost between $2,000 and $4,000. 

Training is minimal, and no personnel costs are incurred because most 

hand geometry devices are typically unattended.



Performance Issues:



Hand geometry disregards fingernails and surface details such as 

fingerprints, lines, scars, and dirt. Except for jewelry, arthritis, 

water retention, and swelling from pregnancy or hand injury, the hand 

is not susceptible to major changes that would affect the technologys 

accuracy. However, because measurements of the hand are not distinct 

over a large population, false matches can occur. Therefore, hand 

geometry is not effective in large-scale1:N applications or in 

applications where resistance to impostors is essential.



User Acceptance:



Hand geometry is generally perceived as nonintrusive, nonthreatening, 

and noninvasive, and it bears very little of the stigma of other 

biometric technologies. It lacks the forensic association that may 

affect users perceptions of fingerprint recognition systems. It is 

considered easy to use, although a minimal amount of training may be 

required to learn how to align the hands in the device. However, some 

people are uncomfortable touching a device that many people have 

previously touched.



The Technologys Maturity:



Operational Uses:



Hand geometry is an established, mature, and reliable technology that 

has remained unchanged for several years. Hand geometry systems have 

been deployed since the 1980s in tens of thousands of locations for 

access and entry control, personal identification, and time and 

attendance applications. For example, hand geometry is the most 

commonly deployed biometric technology for controlling physical access 

and for processing time and attendance records. Devices used for time 

and attendance applications are often tied into physical access control 

systems. Hand geometry devices have been installed at the entrances to 

more than half the nuclear power plants in the United States. In 1991, 

San Francisco International Airport installed hand geometry devices to 

protect secure areas such as the tarmac and loading gates. At the 1996 

Olympic Games in Atlanta, Georgia, athletes used a hand geometry system 

to gain access to Olympic Village.



Tests:



FAA Hand Geometry Testing:



In 2001, FAA and the National Safe Skies Alliance evaluated the 

effectiveness of hand geometry technology for the use of access control 

of airport employees. Following the test, the biometric system was 

removed. Of the 39 people who successfully enrolled, 27 enrolled in an 

average of 57 seconds. The hand geometry system had varying security-

level settings, resulting in differing performance rates at 

verification. The FNMR ranged from approximately 5 percent at a high 

security-level setting to less than 1 percent at a low security-level 

setting. The FMR ranged from approximately 0 percent at the high 

security-level setting to about 2 percent at the low security-level 

setting. Before the biometric technology was installed, passing through 

the door was estimated at 2 seconds; after installation, the time 

increased to 8 seconds.



The results of testing under abnormal conditions are summarized below:



* At the default security level setting, adding or removing rings 

similar to the wide-band ring used in this test would very likely cause 

users to be rejected at a high rate. Smaller rings do not appear to 

cause a higher FNMR.



* Wearing gauze pads or splints to cover injuries would also probably 

cause a higher rejection rate. Standard adhesive bandages three-quarter 

inches wide do not appear to cause higher FNMRs.



* High backlight conditions did not noticeably affect FNMR.



Biometric Product Testing:



From May to December 2000, NPL evaluated seven different biometric 

technologies in a real-world environment for positive verification 

comparative testing. The hand geometry portion of the test used RSIs 

Hand Key II, which had the fastest transaction time of the biometric 

technologies compared. With 200 people enrolled, the FTER was 0 

percent. At an FMR of about 1 percent, the hand geometry system had an 

FNMR of approximately 1.4 percent. Additional experimental results 

were:



* The Hand Key II had a mean transaction time of 10 seconds, a median 

of 8 seconds, and a minimum of 4 seconds.



* The matching algorithm could make 80,000 matches per minute when 

using a SunUltra5 with a SunOS5.8 operating system, 270 MHz processor, 

and 128 MB of memory.



* Males had a somewhat lower FNMR than females.



Sandia National Laboratories:



In 1991, Sandia National Laboratories evaluated five biometric 

technologies, with nearly a hundred volunteers testing each technology. 

Nearly 20,000 transactions were recorded for RSIs ID-3D hand geometry 

devices. Overall, the hand geometry technology was the fastest, most 

accurate, and most user-friendly device. Average verification time was 

5 seconds, and the EER was about 0.2 percent. At the test threshold 

value, the three-try FNMR was less than 0.1 percent, and the one-try 

FMR was 0.1 percent.



Sandia National Laboratories performed a field analysis with hand 

geometry for physical building access control from 1993 to 1995. RSIs 

model ID-3D HandKey biometric verifier was tested. Overall, 316 people 

used the device in more than 100,000 instances. Sandia concluded that 

the device operated differently in an exterior, unattended field 

installation than in previous laboratory experiments: 7.20 percent of 

the individuals failed in the first verification attempt, 53.48 percent 

in the second, and 66.49 percent in the third. These percentages are 

not equivalent to FNMRs, since not enough information was available to 

determine whether users who were rejected should have been accepted. 

They may have been correctly rejected because they may not have been 

who they claimed to be. Researchers also found that maintenance and 

cleaning were paramount; when the readers were not cleaned properly, 

performance was severely degraded.



Border Control Applications Piloted and Deployed:



INSPASS:



Hand geometry is being used in many border control environments. The 

INS Passenger Accelerated Service System (INSPASS), installed at seven 

U.S. and two Canadian airports, uses 29 hand geometry kiosks to reduce 

inspection time to less than 15 seconds for trusted travelers (see 

figure 33). INSPASS enrollment is open to all citizens of the United 

States, Canada, Bermuda, and visa waiver countries. To enroll, 

travelers must provide a passport or travel document and two 

fingerprints and present their hand geometry biometric.



Figure 33: A Traveler Using an INSPASS Hand Geometry Device:



Source: IR--Recognition Systems.



[See PDF for image]



[End of figure]



Ben Gurion Airport:



Since 1998, Ben Gurion Airport in Tel Aviv, Israel, has installed 21 

hand geometry kiosks and enrolled more than 100,000 passengers. The 

implementation was initially offered only to frequent international 

travelers, but passenger demand led to its expansion to all Israeli 

citizens. Each month, more than 50,000 travelers use the automated 

passenger screening system to reduce the immigration process to about 

15 seconds. When using the system, a traveler swipes a magnetic stripe 

card over a biometric reader (see figure 34). More than 2 million 

inspections have been performed, and they are growing at 2 percent a 

month. In addition to biometric authentication, the system checks the 

biometric against Israeli law enforcement and immigration databases.



Figure 34: A Traveler Using Ben Gurion Airports Biometric Hand 

Geometry System:



Source: IR--Recognition Systems.



[See PDF for image]



[End of figure]



Basel Project:



Headed by Electronic Data Systems, the Basel Project will implement a 

system using facial recognition and hand geometry for day workers 

crossing into and out of Israel from the Gaza Strip. Fingerprint 

technology was rejected because the primary users are laborers whose 

fingerprints are not reliable for biometric matching. People will 

enroll at the Israel-Palestine land border, receiving a contactless 

smart card with a high-resolution picture and a hand geometry 

biometric. When entering or leaving Israel, they will be processed 

through 42 routing passages to unattended checkpoints at verification 

terminals inside a building. It is estimated that 60,000 verifications 

will be processed daily in one-to-one matches against stored templates 

in a central server, with a backup stored on the smart card.



Port of Rotterdam:



In June 1999, the Port of Rotterdam, Europes busiest container port, 

implemented a hand geometry system designed to speed cargo movement. 

Each truck drivers identity is verified with the biometric template 

stored on a radio frequency smart card, accessed through the trucks 

window. It has more than 6,000 users and has logged more than 3 million 

transactions.



CANPASS-Airport:



The CANPASS-Airport pilot at Vancouver International Airport was 

initiated in October 1995 using both fingerprint recognition and hand 

geometry technologies. The pilot used identity cards and biometric 

identification devices to allow previously screened travelers to bypass 

customs and immigration lines. Qualified Canadian and U.S. residents 

entered Canada through a special line by opening an automated gate with 

an encoded identification card and providing a fingerprint or hand 

geometry biometric for one-to-one authentication. The systems use was 

discontinued on September 11, 2001.



Device Durability and Environmental Constraints:



Hand geometry is well suited for most environments. The equipment is 

durable and can withstand most workload demands. Various types of hand 

geometry devices on the market are suitable for all types of climates 

(see figures 35 and 36). Most can withstand temperatures ranging from -

45 degrees to 120 degrees Fahrenheit and can provide protection against 

snow, sleet, rain, splashing water, hose-directed water, falling dirt, 

and wind-blown dust.



Figure 35: A Typical Hand Geometry Recognition Device:



Source: IR--Recognition Systems.



[See PDF for image]



[End of figure]



Figure 36: A Hand Geometry Recognition Device That Is Enclosed:



Source: IR--Recognition Systems.



[See PDF for image]



[End of figure]



[End of section]



Appendix IV: Facial Recognition Technology:



Every day, people identify other people by their faces. Much research 

has yielded evidence that people may recognize others faces through a 

unique process that highlights the importance of the location and shape 

of eyes, nose, and eyebrows and face shape, chin, lips, and mouth, in 

decreasing order. Because this process differs from how we recognize 

other objects, the idea that machine recognition systems should also be 

face-specific may have been encouraged. However, just as some people 

may have difficulty differentiating between identical twins and other 

people with similar features, facial recognition technology also cannot 

effectively distinguish between people who resemble one another, and it 

still requires development to full maturity. Nevertheless, active 

research over the past 10 years has made the technology commercially 

available.



How the Technology Works:



Facial recognition identifies people by the sections of the face that 

are less susceptible to alteration--the upper outlines of the eye 

sockets, the areas around the cheekbones, the sides of the mouth. 

Systems using this technology capture facial images from video cameras 

and generate templates for comparing a live facial scan to a stored 

template. Facial recognition technology can also be used to compare 

static images, such as digitized passport photographs.



The comparisons are used in verifying and identifying individuals. 

Verification systems compare a persons facial scan to a stored 

template for that person and can be used for access control. In an 

identification system, a persons facial scan is compared to a database 

of multiple stored templates. This makes an identification system more 

suitable for surveillance in conjunction with closed-circuit television 

(CCTV) to spot suspected criminals whose facial characteristics have 

been captured and stored in a database on a template. The face is the 

only biometric used in a viable recognition technology that is able to 

operate without a users cooperation, since a CCTV camera need only 

capture a picture for the technology to generate a template. However, 

the technology is much more able to identify people who are motivated 

to use the system correctly than those who are uncooperative and can 

avoid recognition by, for example, using disguises or taking other 

evasive measures.



The primary facial recognition technologies are used for one-to-one as 

well as one-to-many matching. Whether used for verification or 

identification, the stored image templates must be kept up to date, 

since appearances naturally alter with age.



However, IBGs testing has found that the core technology is highly 

susceptible to falsely nonmatching users in one-to-one verifications 

and to failing to identify enrolled users in one-to-many 

identifications.



Two primary types of facial recognition technology are used to create 

templates.[Footnote 44] Requiring as many as 1,300 bytes, or as few as 

84 bytes, they are local feature analysis (LFA) and the eigenface 

method.



Local Feature Analysis:



Patented by Visionics Corp.--now Identix Incorporated--LFA uses dozens 

of images from regions of the face, resulting in feature-specific 

fields--eyes, nose, mouth, cheeks. The fields relative locations are 

incorporated so that the face can be represented as a topographical 

grid made up of blocks of features. The features represented by these 

blocks and their positions are used to identify or verify the face (see 

figure 37).



Figure 37: Local Feature Analysis: A Topographical Grid of Facial 

Regions:



Source: Identix Incorporated.



[See PDF for image]



[End of figure]



Just as Washington, D.C., can be identified by describing its 

landmarks locations and their relative positions (e.g., the National 

Mall has the U.S. Capitol building to its east, the Lincoln Memorial to 

its west, and the Washington Monument and Smithsonian museums at its 

center), a persons face can be identified by the features defined by 

LFA. Small shifts in a feature may cause a related shift in an adjacent 

feature and the technology can accommodate these changes in appearance 

or expression (such as smiling or frowning). Since LFA does not provide 

a global representation of the face, it is rendered ineffective when a 

person tilts his or her head from a direct frontal pose to more than 

about 25 degrees horizontally or more than about 15 degrees vertically.



The Eigenface Method:



Eigenface, meaning roughly ones own face, is a technology patented 

at the Massachusetts Institute of Technology. Unlike LFA, the eigenface 

method always looks at the face as a whole. A collection of facial 

images is used to generate a set of two-dimensional, gray-scale images 

(eigenfaces) to produce the biometric template (see figure 38). The 

vast majority of faces can be represented by locating distinctive 

features from approximately 100 to 125 eigenfaces. When a live image of 

a persons face is introduced, the system represents the image as a 

combination of templates. This combination is compared with a set of 

stored templates in the systems database, and the degree of variance 

determines whether or not a face is recognized.



Figure 38: Two-Dimensional, Gray-Scale Images of an Eigenface Template:



Source: Baback Moghaddam, MIT Media Laboratory.



[See PDF for image]



[End of figure]



Modifications of the algorithms used in LFA and the eigenface method 

can lead to variances that incorporate:



* Neural network mapping: Comparisons of a live facial image with a 

stored template are based on unique global features rather than 

individual features. When a false match is made, the comparison 

algorithm modifies the weight given to certain features, such as 

shadows.



* Automatic face processing: Facial images are captured and analyzed 

from the distances and distance ratios between features, such as 

between the eyes.



The Leading Vendors:



The leading algorithms are licensed by Identix Inc. (which merged with 

Visionics in June 2002) and Viisage Technology. Identix uses local 

feature analysis; Viisages algorithm is based on the eigenface method.



The Cost of Devices:



A facial recognition server controlling access at a facility with up to 

30,000 persons would cost about $15,000. Depending on the number of 

entrances with installed facial recognition technology, the cost of the 

software licenses would range from about $650 to $4,500. As the size of 

the database and the number of attempted matches increased, so would a 

systems cost.



In addition to the server and software licenses, a live-scan facial 

recognition surveillance system includes CCTV surveillance (see figure 

39). A fully integrated CCTV system for physical access surveillance 

can cost from $10,000 to $200,000, depending on the size of the 

entrance and the degree of monitoring required. For additional CCTV 

equipment, cameras can cost between $125 and $500. Cameras with 

advanced features can cost up to $2,300.



Figure 39: CCTV Surveillance Equipment:



Source: Pelco.



[See PDF for image]



[End of figure]



Performance Issues:



The effectiveness of facial recognition technology is influenced 

heavily by environmental factors, especially lighting conditions. 

Variations in camera performance and facial position, expression, and 

features (hairstyle, eyeglasses, beards) further affect performance. 

Accurate image alignment is necessary for the leading facial 

recognition algorithms, which rely on identifying eye positions. As a 

result, current facial recognition technology is most effective when 

used in consistent lighting with cooperative subjects in a mug-shot-

like position--where hats and sunglasses are removed and everyone looks 

directly at the camera one at a time.



Attempts to spoof live-scan facial recognition systems have been 

successful. In one test, trial images were obtained by downloading 

unprotected reference facial images to a computer and by taking digital 

pictures of an enrolled person. These images were displayed on a 

notebook computer monitor and were successfully matched, granting 

testers access to the system. A video of an enrolled person moving his 

head slightly left and right also fooled the system.



User Acceptance:



When used in a verification system for access control, facial 

recognition technology is typically considered by users to be less 

intrusive than fingerprint readers, iris scanners, and other biometric 

technologies. It can recognize people at a distance and does not 

require users to pause and interact with the equipment. However, some 

users are concerned that when used as a surveillance tool, facial 

recognition technology can facilitate tracking them without their 

consent. To address such concerns, specific policies for using facial 

recognition in a surveillance application have been suggested, 

including the following.



Transparency:



As with any technology, public understanding of the operation and uses 

of electronic surveillance might mitigate fears that the government may 

be tracking peoples whereabouts. Signs indicating the use of facial 

recognition in surveillance systems should be prominently displayed, 

and the government entity using facial recognition for surveillance 

should provide as much information as possible to the public about the 

technologys purposes and capabilities.



No Match, No Memory:



Concerns have been raised about the possibility that facial recognition 

surveillance systems can identify law-abiding citizens, not only 

terrorists or violent criminals. A no match, no memory policy 

dictates that a persons image is saved only if a match is made to a 

record in a watch list database.



Data Retention:



One issue that could arise is the governments handling of the data it 

collects. Even if a no match, no memory policy has been implemented, a 

retention policy should be followed that indicates the time period 

after which the data will be erased. Similarly, the data should be 

securely stored and maintained.



Oversight:



Concern about how facial technology surveillance will be used is often 

related to fear that the technologys capabilities will be abused. 

Facial recognition systems must be used only for the purpose they were 

designed for, and some form of active oversight should be implemented. 

A cooperative effort between government officials and citizen oversight 

committees would provide accountability.



The Technologys Maturity:



Operational Uses:



The largest implementation of Identixs facial recognition technology 

is the Mexican Federal Electoral Institutes program to eliminate 

duplicate voter registrations. This system helps the Institute prevent 

citizens from voting more than once under different aliases. Facial 

recognition is used to compare people with matching names to determine 

whether the faces also match. The systems database, first used in 

Mexicos July 2000 presidential elections, contains about 60 million 

images.



The largest deployment of facial recognition for surveillance began in 

1998 in Newham Borough, London, England, when Identixs facial 

recognition technology was introduced to 12 town center cameras to 

record activity and decrease street robbery in an unsafe neighborhood. 

With three hundred CCTV cameras, this system captures faces and 

compares them against a police database of about a hundred convicted 

street robbers known to have been active in the previous 12 weeks. When 

a face does not match, the image is deleted; when a match is found, an 

operator checks the result. In August 2001, 527,000 separate faces were 

detected and operators confirmed 90 matches against the database.



Public approval of Newhams system was judged by comparing the results 

of opinion polls over the course of the implementation. When Identixs 

facial recognition technology was first introduced, 50 percent of local 

citizens approved of the system. After about 2 years of operation, the 

technology was credited with a 34 percent reduction in street robbery, 

and the user approval rating rose to 90 percent. As the system has not 

led directly to any arrests, the effect of facial recognition 

technology appears to function largely as a deterrent to street crime 

in the monitored area.



In the United States, Viisages facial recognition technology is 

deployed in 17 states to identify people with credentials or 

identification documents under more than one name. The majority of the 

states databases consist of image templates from drivers license 

photographs. Illinoiss drivers license database consists of about 10 

million images and has the capacity for another 15 million images. The 

technology can perform a one-to-many match against this database in 

less than 15 seconds, and about 15,000 images are captured daily.



Facial recognition surveillance systems have been deployed in casinos 

worldwide, performing one-to-many matching against a database of casino 

offenders. Although the notable facial recognition implementations are 

in surveillance applications, facial recognition systems have been 

deployed in selected environments as a one-to-one verification solution 

for physical and logical access. Some casinos use facial recognition 

for employee time and attendance processing, while applications for 

automated teller machine fraud prevention and security have been 

implemented in grocery stores and gas stations.



Pilots: U.S. Airport

Surveillance:



Identix has been involved in four pilots that use facial recognition 

for surveillance at U.S. airports. The pilots had different operating 

scenarios to determine the relationship between the correct match rate-

-that is, the rate of actual matches--and the FNMR. Video cameras that 

were not hidden from travelers were set up near the airport metal 

detectors. The pilots were designed at some airports so that travelers 

were specifically instructed to stop and look at the cameras; travelers 

at other airports were not given such instructions.



From the four pilots, Identix concluded that lighting was the primary 

performance factor. It learned also that the correct match rate, and 

therefore the FMR, is quickly compromised as the threshold is adjusted 

to minimize the FNMR. The data are shown in table 24.



Table 24: Identix Airport Facial Biometric Pilot Results:



Airport: Boston Logan International, Mass.; Status: Completed; False

 match rate: Not reported; False

nonmatch rate: ~10%; Notes: Viisage technology was also piloted.



Airport: Dallas/Fort Worth International, Texas; Status: Completed; 

False match rate: 1.2%; False nonmatch rate: 6-15; Notes: Two cameras 

were used; when a match was made, the persons image was dispatched to 
a 

central control room for further investigation.



Airport: Fresno Yosemite International, Calif.; Status: Ongoing; False

match rate: 1-5; False nonmatch rate: 5-15; Notes: A liquid crystal 

display instructed each traveler when to pause in front of a fixed 
camera 

and when to resume walking.



Airport: Palm Beach International, Fla.; Status: Completed; False

match rate: 0.3; False nonmatch rate: 45; Notes: The objective was to 

obtain an FMR as close to 0 as possible.



Source: GAO analysis of Identix data.



[End of table]



Facial Recognition Vendor Test 2000:



From May to June 2000, Naval Surface Warfare Center, Crane Division, 

evaluated an identification system in the Facial Recognition Vendor 

Test 2000 (FRVT 2000). The two test categories conducted during the 

evaluation used the Face Recognition Technology (FERET) Database, which 

DODs Counterdrug Technology Development Program Office sponsors. The 

evaluation report was issued on February 16, 2001.



The first category, the recognition performance test, evaluated all 

algorithms on a standardized database collected by a universal sensor. 

Participating vendors were given 72 continuous hours in which to 

compare 13,872 images to one another, amounting to more than 192 

million comparisons. Three vendors completed this portion: Identix, 

Viisage, and C-VIS Computer Vision and Automation GmbH. Banque 

Technology Systems International Ltd. (Banque-Tec) and Miros Inc.

(E-True Technology), two other vendors, were able to compare only 

approximately 4,000 of the 13,872 images in the allotted time, and 

their results were not included.



Following this test, different environmental studies were conducted to 

show how the system responded to numerous variables such as distance, 

lighting, and facial expressions. We describe a sample of the results 

from a number of environmental studies, noting the overall lack of 

appreciable difference between the match accuracy of the Viisage and 

Identix algorithms. For the identification experiments, the charts we 

present show the probability that a vendors top match correctly 

identified individuals. For the verification experiments, the results 

show the probability of correct verification while holding the FMR 

constant at 0.01. Each probe image was taken with a camera and matched 

by the vendors system to the gallery images, which were drawn from 

FERET and other large databases.



Distance Experiments:



The distance experiments were designed to evaluate the performance of 

face-matching algorithms on images of subjects at different distances 

from the fixed camera. For the distance experiments, the probe images 

were taken at varying distances and compared, using the vendors 

system, to gallery images that were taken at a distance of between 1.5 

and 2 meters (see figures 40 and 41).



Figure 40: Facial Recognition Distance Identification:



Source: GAO analysis of FRVT 2000 data.



[See PDF for image]



[End of figure]



Figure 41: Facial Recognition Distance Verification:



Source: GAO analysis of FRVT 2000 data.



[See PDF for image]



[End of figure]



Across all algorithms, the three sets of distance experiments indicated 

that performance decreases as the distance between the person and 

camera increases. At a distance of 5 meters, Viisage, the best vendor 

in this category, could correctly identify the image only about 13.7 

percent of the time.



Expression Experiments:



The expression tests evaluated how well identification and verification 

work when comparing images of the same person with different facial 

expressions. In this test, the gallery image was a face with a specific 

expression, and the probe image was the same face with an alternative 

expression. Identification proved more sensitive to change in 

expression than verification. Viisage and Identix correctly identified 

and verified more than 80 percent of the images (see figures 42 and 

43).



Figure 42: Facial Recognition Expression Identification:



Source: GAO analysis of FRVT 2000 data.



[See PDF for image]



[End of figure]



Figure 43: Facial Recognition Expression Verification:



Source: GAO analysis of FRVT 2000 data.



[See PDF for image]



[End of figure]



Media Experiments:



The media experiments were designed to evaluate the performance of 

face-matching algorithms when comparing images stored on different 

media. This application may be useful in comparing older mug shots to 

newer pictures taken with digital cameras. For Viisage and Identix, 

switching between 35 mm gallery images and digital probe images, and 

vice versa, did not significantly affect performance (see figures 44 

and 45).



Figure 44: Facial Recognition Media Identification: Digital to 35 mm:



Source: GAO analysis of FRVT 2000 data.



[See PDF for image]



[End of figure]



Figure 45: Facial Recognition Media Verification: Digital to 35 mm:



Source: GAO analysis of FRVT 2000 data.



[See PDF for image]



[End of figure]



Pose Experiments:



The pose experiments measured the effect of different viewpoints on 

identification. They attempted to match a frontal gallery image with 

probe images that were rotated various degrees away from the front. The 

results reflected the best score of all vendors at each degree. As the 

degrees from the frontal image increased, the probability of 

identification fell rapidly. At 60 degrees away from the frontal image, 

identification was correct only 30 percent of the time (see figure 46).



Figure 46: Facial Recognition Pose Identification:



Note: These results reflect the best scores of all vendors at each 

degree.



Source: FRVT 2000.



[See PDF for image]



[End of figure]



Temporal Experiments:



Temporal experiments addressed the effect of time delay between a first 

and subsequent capture of facial images. The test attempted to match 

each probe image with a gallery image of the same person taken 

approximately 1 year earlier. These experiments showed that a vendors 

ability to correctly identify and verify images decreases significantly 

with time. After 1 year, Viisage and Identix identified 31 percent and 

48 percent of faces, respectively. Viisage correctly verified 41 

percent of images, Identix 56 percent (see figures 47 and 48).



Figure 47: Facial Recognition Temporal Identification:



Note: The time period measured was 1 year.



Source: GAO analysis of FRVT 2000 data.



[See PDF for image]



[End of figure]



Figure 48: Facial Recognition Temporal Verification:



Note: The time period measured was 1 year.



Source: GAO analysis of FRVT 2000 data.



[See PDF for image]



[End of figure]





The second test category, product usability, evaluated the complete 

facial recognition system rather than just the facial recognition 

algorithm. An access control scenario with live subjects was chosen. 

Five vendors reported results, including the three vendors that 

completed the recognition performance test and the two that did not. 

When discussing the results, however, it is important to note that some 

systems tested were not intended for access control applications.



The two product usability tests were the enrollment timed test (ETT) 

and the old image database timed test (OIDTT). The tests had two main 

differences: (1) the subjects were stationary for the ETT and walked 

toward the camera in the OIDTT and (2) all vendors performed 

substantially better on the ETT, in which they enrolled the images 

under their own systems, than on the OIDTT, in which the images were 

provided to them before the test. Also, the facial recognition systems 

were quicker and more accurate in the verification experiments than in 

the identification experiments. See table 25 for the results.



Table 25: Facial Recognition Product Usability Test:



Vender: [Empty]; Vender: [Empty].



Vender: Banque-Tec; [Empty]; Old image database timed: Percent

verified: 7%; Old image database timed: Percent

identified: 0; [Empty]; Enrollment timed: Percent

verified: 22%; Enrollment timed: Percent identified: 22%.



Vender: C-VIS; [Empty]; Old image database timed: Percent

verified: 0; Old image database timed: Percent

identified: 0; [Empty]; Enrollment timed: Percent

verified: 69; Enrollment timed: Percent identified: 83.



Vender: Identix; [Empty]; Old image database timed: Percent

verified: 64; Old image database timed: Percent

identified: 31%; [Empty]; Enrollment timed: Percent

verified: 78; Enrollment timed: Percent identified: 52.



Vender: Miros (E-True); [Empty]; Old image database timed: Percent

verified: 36; Old image database timed: Percent

identified: 0; [Empty]; Enrollment timed: Percent

verified: 78; Enrollment timed: Percent identified: 71.



Vender: Viisage; [Empty]; Old image database timed: Percent

verified: 0; Old image database timed: Percent

identified: 0; [Empty]; Enrollment timed: Percent

verified: 84; Enrollment timed: Percent identified: 84.



Note: Percentages are correct matches. Matching that took longer than 

10 seconds counted as failure.



Source: GAO analysis of FRVT 2000 data.



[End of table]



Facial Recognition Vendor Test 2002:



Facial Recognition Vendor Test 2002 (FRVT 2002), a follow-up to FRVT 

2000, does not use the FERET database and is not a live facial 

recognition test.[Footnote 45] Since the variables involved with a live 

capture do not allow for an equal test bed among all the participants, 

databases of photograph images will be used. Also, this test will 

include video data to determine whether multiple images of a person 

increase matching accuracy.



Twenty-seven organizations are participating in FRVT 2002, each testing 

for a minimum of 4 days and a maximum of 11 days with its own hardware 

and software (i.e., its own algorithms). The tests will perform a 100 

kilobyte by 100 kilobyte comparison (comparing each face to every other 

face in the database) and return the results in the form of similarity 

matrixes. In preliminary tests of facial recognition, NIST has seen a 

75 percent probability of verification with a 1 percent probability of 

false acceptance, compared with fingerprint recognitions 95 percent 

probability of verification and 1 percent probability of false 

acceptance.



Biometric Product Testing:



NPL conducted a performance evaluation of seven biometric systems from 

May through December 2000, producing a final biometric product testing 

report on March 19, 2001. The facial recognition portion of the test 

used an Identix FaceIt Verification demonstration as well as 

alternative enrollment and matching algorithms.



The 0 percent FTER included persons unable to present the required 

biometric feature, those unable to produce an image of sufficient 

quality at enrollment, and those unable to reproduce their biometric 

feature consistently. At an FMR of about 1 percent, the facial 

recognition system with the alternate matching algorithm had an FNMR of 

approximately 3.3 percent. Additional experimental results were:



* The facial recognition system collected a sequence of images over a 

10-second period, saving the best match. This resulted in a mean 

transaction time of 15 seconds, a median of 14 seconds, and a minimum 

of 10 seconds.



* The matching algorithm could make 800 matches per minute with a 

Pentium processor, a Windows interface, and a Windows 2000 operating 

system. These diagnostic programs had significant overhead, so the 

matching algorithm may be significantly faster than the results showed, 

perhaps by a factor exceeding 100.



* Tests also found that males had a lower FNMR than females, and the 

FNMR for attempts made immediately following enrollment were 

significantly lower than those made at a volunteers second or third 

visit.



U.S. Army Research Laboratory Test:



For a personnel identification application, the Army Research 

Laboratory tested an identification system from July through October 

2001, using Identix facial recognition technology. With 270 

participants, approximately 42,000 face identification attempts were 

made. Despite the vendors claims of a 75 percent rate of correct 

identification, the testing showed that only 51 percent were correctly 

identified. Further, the correct identification was in the systems top 

10 possible matches only 81 percent of the time, instead of the 99.3 

percent that the vendor claimed. Inadequate lighting was a primary 

performance issue.



FAA Facial Recognition Test:



In 2001, FAA and Safe Skies tested a facial recognition technology 

system for access control of airport employees. Following the test, the 

biometric system was removed. Twenty-eight people successfully enrolled 

in an average of 3 minutes and 2 seconds. The test included operational 

testing in a normal environment as well as testing under a controlled 

environment. The FNMR for the operational test was approximately 26 

percent. Before device installation, the time required to pass through 

the door was approximately 2 seconds; after installation, 11.5 seconds. 

The FNMR for the controlled test was approximately 3 percent. Under 

normal test conditions, the rate of passage through the door was about 

six people per minute.



Test results for abnormal conditions were as follows:



* FNMR was nearly 100 percent when test subjects enrolled without 

sunglasses but passed through the device with sunglasses. The opposite-

-enrolling with sunglasses and presenting with sunglasses--also yielded 

an FNMR of nearly 100 percent.



* When test subjects enrolled without reading glasses but passed 

through the device with reading glasses, FNMR was nearly 60 percent; 

when they enrolled with and presented without reading glasses, FNMR was 

nearly 20 percent.



* FNMR increased notably for one test subject of three with a 5-day 

beard growth. No effect was noted for the two other subjects. The 

effect was little or none for enrolling with 5-day beard growth and 

then attempting access while clean-shaven.



* A horizontal -inch adhesive bandage on the chin produced an overall 

FNMR of 40 percent, but the results were highly dependent on the test 

subject--three had a rate of 0 percent and two had a rate of 100 

percent. A round bandage on the cheek produced an overall FNMR of 6 

percent.



* No effect was noted from high backlighting directly; however, one 

test subject with glasses was falsely rejected 10 of 10 times. Further 

investigation showed a reflection on the glasses from the backlighting 

from the door window.



State Department Consular Affairs Tests:



The Department of State Bureau of Consular Affairs evaluated facial 

recognition technology for identifying ineligible visa applicants. 

Viisage and Identix provided facial recognition software. The final 

evaluation report was issued on January 30, 2001.



Laboratory testing involving data sets of 10,000 to 100,000 images 

revealed that less than 30 percent of intentionally seeded duplicate 

images were correctly matched. This translates into an FNMR of around 

70 percent. The processing speed for facial recognition enrollment was 

more than adequate. Images were aligned and enrolled at a rate of 

approximately two per second for both tested products. Processing speed 

for search ranged from excellent for one vendors product to marginal 

for the other vendors product. In the latter case, an improved version 

of the software, submitted after formal testing was completed, was 

faster by a factor of two in performing searches of large data sets. 

The search speed might limit its usefulness in processing a large data 

set but is acceptable for daily operations.



The National Visa Center tested the technology with the diversity visa 

program in the field. This trial showed that a facial recognition 

system can be successful in identifying matches involving duplicate 

applications. More than 500 matches were found while examining more 

than 5,000 of 35,000 possible duplicate images. Of these 500 and more, 

146 represented cases that had not been discovered by other means. This 

success was obtained despite the obviously poor quality of the pictures 

submitted, the poor capture characteristics of the Quickcam cameras 

used, and the less than optimal scanning technique the data entry 

personnel used. It was observed that Identixs product was more 

forgiving of the image quality problems and generally reported more 

matches.



Despite the vendors cooperative, responsive, and interactive approach 

in supplying testable products and engineering support, the facial 

recognition software packages, even in their final versions, 

following numerous developmental versions, exhibited significantly 

troublesome behavior--such as corrupt databases, poorly implemented 

capabilities, and the need for workaround solutions--that impeded 

testing.



Border Control Applications Piloted and Deployed:



INS SENTRI:



INS conducted a facial verification test for the Secure Electronic 

Network for Travelers Rapid Inspection (SENTRI) from November 1997 

through July 1998 at Californias Otay Mesa port of entry. The facial 

verification test involved taking video images of drivers at an 

inspection booth. The video clips were compared to the SENTRI 

enrollment database of photographs for all drivers in the SENTRI lane. 

An Identix system was used for the tests.



The experiment found that pictures taken in a full frontal enrollment 

pose showed a significantly higher recognition rate than pictures taken 

when the head was rotated slightly. It also found a principal 

identification problem when the image was obtained during validation. 

Obscured faces that were hidden by part of the vehicle and those with 

excessive glare or extreme shadows were essentially unusable. In 

testing, the proportion of video clips exhibiting these properties was 

initially very high. Adding cameras increased the chance of getting an 

unobstructed video clip. A new camera system using fuzzy logic helped 

reduce glare and shadows.



With these changes, the system was able to get usable images for 

approximately 90 percent of the vehicles in a lane. With such images, 

the system had an FNMR of 1.6 percent and a low EER of 2.1 percent. The 

report concluded that the facial verification system performed 

admirably in a challenging environment.



State Department Posts:



The State Department is conducting pilots using facial recognition 

technology from Identix and Viisage to compare images from 23 of its 

posts. The facial recognition software is used primarily to compare 

digital pictures in one-to-many matching to identify people who apply 

more than once for nonimmigrant visas or diversity visas.[Footnote 46] 

A secondary one-to-many matching of photographs from both previously 

issued visas and new visa applications is performed against a watch 

list database. The photographs from all visa applications are scanned 

into the system, regardless of whether visas are issued or applications 

are rejected. All scanned images (not just the templates) are retained 

in case future versions of the facial recognition software use a 

different template format.



The primary performance factor for the State Department pilots has been 

the quality of the photographs submitted with applications. The better 

the quality of the photographs is, the more likely it is that match 

results will be good. It was found that many of the images in the 

databases are poor in quality--either too dark or too light for facial 

recognition, poorly focused, or distorted in some other way. 

Consequently, the State Department is working to develop standards for 

photograph quality. Age was found to be a performance factor. For 

example, both Identix and Viisage have found it difficult to match 

children because their faces change rapidly. However, State Department 

officials have not noticed any appreciable differentiation in the 

quality between the Identix and Viisage match algorithms.



Of approximately 197,000 images (applicants photographs) for diversity 

visas processed in the 2002 program year, 75 percent were successfully 

enrolled in the diversity visa facial recognition database. The images 

from the 74,348 successful applications were matched against the 

enrollment database. About 6,000 candidate matches were made; 85 

percent were determined to be actual matches. The facial recognition 

technology identified 60 individuals who submitted multiple 

applications that were not detected by the manual process.



In October 2001, 23 posts processed approximately 26,000 nonimmigrant 

visa images, of which 78 percent were successfully enrolled in the 

nonimmigrant visa facial recognition database. For all 23 posts, around 

4,000 candidate matches were made. The percentage of actual matches 

varied by post, as one posts matching had an FMR of 1 percent, and 

another posts matching resulted in an FMR of 65 percent.



Iceland:



One of the first major installations of facial recognition technology 

at an airport was at Icelands Keflavik International Airport in June 

2001. As a result of Icelands participation in Europes Schengen 

agreement, border controls between that country and others 

participating in the agreement have been eliminated.[Footnote 47] The 

facial recognition system was implemented to identify known criminals 

and false asylum seekers while maintaining a level of convenience for 

citizen travelers.



Israel:



The Basel Project is a pending implementation of facial recognition and 

hand geometry for day workers entering and exiting Israel by way of the 

Gaza Strip. Fingerprint technology was rejected, since the primary 

users are laborers whose fingerprints are unreliable as a biometric for 

matching.



Individuals enrolling at the Israeli-Palestinian land border will 

receive a contactless smart card with a high-resolution picture and a 

hand geometry biometric. As they enter and leave Israel, they will be 

processed through 42 routing passages to unattended checkpoints at 

verification terminals inside a building. An estimated 60,000 

verifications will be processed daily, performing a one-to-one match 

against a stored template in a central server, with a backup stored on 

the smart card.



Australia:



Australias Sydney Airport is conducting a facial recognition pilot to 

determine cost effectiveness and efficiency in an operational 

environment. The technology is being used for both verification and 

identification. One-to-one verification is performed to identify false 

passports as travelers present their passports, and one-to-many 

identification is used to identify terrorists among the crowds.



Dominican Republic:



The Dominican Republic is implementing Identixs facial recognition 

technology for scanning passports at 120 entry points. The system will 

capture a face biometric, which will be used in a search against a 

central criminal watch list database. If another biometric is needed in 

the future, the passport reader will also be capable of reading a 

fingerprint.



Processing Issues:



Processing speed for facial recognition enrollment is approximately two 

images per second. The raw search speed is one million searches per 

second on a single computer, but other factors are involved, such as 

the size of the database. For an identification application, search 

speed can be dramatically improved by storing some templates on a disc 

during alignment for use during later searches. A facial recognition 

system can be designed to achieve a desired response time by increasing 

the number of processors, but the trade-off to increased speed is 

greater cost.



Because facial recognition biometrics can be used in various 

applications, different requirements affect performance time 

differently. The requirements for performing a background check and a 

duplicate face check at enrollment would differ from those for 

performing verifications at borders. Verifications at a border would be 

practically instantaneous if performing a one-to-one match against a 

template stored on a travel document or a smart card, but an additional 

one-to-many watch list search would add time, depending on the size of 

the database. Facial recognition results in a faster response time than 

fingerprint recognition in a one-to-many search. The implication of a 

heavily queried database is that a priority level must be assigned to 

determine when the various transactions are to be handled.



Device Durability and Environmental Constraints:



In surveillance applications, travelers would not interact physically 

with the cameras and computers that run the facial recognition 

technology. The durability of this equipment would depend on the 

manufacturers specifications.



Because lighting is such a major performance factor, the use of awnings 

or shades with outdoor installations of facial recognition technology 

could be required to block direct light. Without awnings or shade, 

glare or shadows might present a problem that could be compounded by 

reflections from nearby buildings or vehicles.



[End of section]



Appendix V: Iris Recognition Technology:



Iris recognition technology was developed in 1992 and is therefore one 

of the newest of the commercially available biometric technologies. It 

is based on the distinct, visible characteristics of the eyes iris, 

the colored ring that surrounds the pupil (see figure 49). Built from 

elastic connective tissue, the iris is a very rich source of biometric 

data. The characteristics of the iris are formed during the eighth 

month of gestation and do not change except through actions such as 

refractive surgery, cataract surgery, and cornea transplants. Iris 

recognition can even be used to verify the identity of blind people as 

long as one of their sightless eyes has an iris.



Figure 49: The Iris and Other Parts of the Eye:



Source: Copyright, the American Academy of Ophthalmology.



[See PDF for image]



[End of figure]



The iris has more numerous and dense forms of variability than other 

biometrics. Whereas traditional biometrics have only 13 to 60 distinct 

characteristics, the iris can be said to have 266 unique spots, and 

iris recognition technology uses some 173 of these features. The 

primary visible characteristic of the iris is the trabecular meshwork, 

tissue that gives the appearance of dividing the iris radially. Other 

features include striations, rings, furrows, a corona, and freckles.



Besides the iris many distinctive characteristics, its patterns also 

differ substantially from person to person. A persons left and right 

eyes have different iris patterns, and the irises of identical twins 

have almost no statistical similarity. It has been postulated that the 

probability of two persons having the same iris pattern is 1 in 7 

billion.



How the Technology Works:



An iris recognition system uses a small high-quality camera to capture 

a black-and-white, high-resolution picture of the iris. The technology 

relies on infrared imaging, using wavelengths from 700 to 900 

nanometers, a range the American Academy of Ophthalmology has stated is 

safe.



How close the person should be to the camera and her level of 

participation depend on the type of system. Physical access control 

applications require a person to stand within 3 to 10 inches of the 

camera and center the iris in a mirror within an area 1 inch square 

directly in front of the camera (see figure 50). The system may prompt 

the person to move slightly forward or backward to allow a proper image 

capture. Systems using desktop cameras to control logical access to 

computers and networks require a distance of about 18 inches to capture 

the iris image (see figure 51). Users must center their eyes on the 

camera with a guidance light or hologram. Personal identification 

systems, such as those at airport kiosks in trusted traveler 

applications, allow users to stand as far away as 3 feet. However, 

users must remain still as the camera locates the eye and captures the 

image.



Figure 50: Iris Recognition Physical Access Control System:



Source: Panasonic Digital Communications & Security Co.



[See PDF for image]



[End of figure]



Figure 51: Iris Recognition System with Desktop Camera:



Source: Panasonic Digital Communications & Security Co.



[See PDF for image]



[End of figure]



An iris recognition system first defines the boundaries of the iris, 

establishes a coordinate system over the iris, and defines the zones 

for analysis within the coordinate system. Feature extraction 

algorithms map the segments of the iris into hundreds of independent 

vectors that define the orientation and spatial frequency of the 

distinctive features, along with the position of the features. However, 

the entire iris is not used: A portion of the top as well as 45 degrees 

of the bottom remain unused, to account for pupil dilation, occlusion 

from eyelids, and reflection from the camera (see figure 52).



Figure 52: Mapping the Eye for Iris Recognition Systems:



Source: Dr. John Daugman, Cambridge University, Cambridge, U.K.



[See PDF for image]



[End of figure]



Algorithms also check for the presence of a pattern on the sphere of 

the eye instead of on an internal plane and use measurements at 

different wavelengths to determine that the eye is living. The visible 

characteristics within the zones are then converted into a 512 byte 

template that is used to identify or verify the identity of an 

individual; 256 of these bytes contain control information.



The Leading Vendors:



Iridian Technologies Inc. is the sole owner and developer of iris 

recognition technology. Iridian markets applications through hardware 

manufacturers and systems integrators, including Argus Solutions, 

EyeTicket Corp., IBM, Joh. Enschede Security Solutions, LG Electronics, 

NEC Singapore, Oki Electric Industry Co., Panasonic, SAFLINK Corp., 

Siemens AG, Titan Corp., and Unisys.



The Cost of Devices:



Iris recognition systems cost approximately $2,000 for physical access 

units. The camera itself costs $200.



Performance Issues:



Some users are unable to provide adequate enrollment images because 

they find the iris image capture process too difficult. Poor eyesight 

may also hinder the ability of some people to line up their eyes with 

the camera. Colored and bifocal contact lenses can affect system 

performance, and so can exceptionally strong glasses. People with 

glaucoma may not be reliably identified. Also, glare and reflections, 

along with user settling and distraction, can cause interference.



User Acceptance:



Some people resist technologies that scan the eye, but unlike biometric 

identification and verification technologies such as fingerprint 

recognition or hand geometry, iris recognition technology requires no 

body contact. Iris recognition technology is more user friendly than 

retina recognition systems in that no light source is shone into the 

eye and close proximity to the scanner is not required. However, iris 

recognition does use active infrared illumination in the 700 to 900 

nanometer wavelength range. It has none of the inherent risks 

associated with lasers. Some people assume that the imaging of their 

irises will reveal their medical data, such as heart disease, diabetes, 

and high blood pressure, but images of the iris acquired for iris 

recognition reveal no information about a persons health.



The Technologys Maturity:



Operational Uses:



Iris recognition is being used operationally for physical access 

control, logical access control, and personal identification 

applications. An EyeTicket access control system was installed at 

Douglas International Airport in Charlotte, North Carolina, in July 

2000 to control airline and airport employee access to restricted 

areas. The company has also installed the access control system at 

Germanys Frankfurt Airport. Iridian has installed IrisAccess at 

Baltimore Technologies data hosting center in Sydney, Australia. 

Access to the highly secure facility requires that anyone requesting 

entry verify her identity with both a proximity card and the iris 

recognition technology.



The Office of Legislative Counsel for the U.S. House of Representatives 

has recently installed an iris recognition system to protect 

confidential computer files and working documents. Iris recognition 

systems have been deployed in several prison systems in the United 

States to prevent inmates from swapping identities with visitors as 

well as to verify the identity of prisoners before they are released.



Tests:



Biometric Product Testing:



NPL conducted a performance evaluation of seven biometric systems from 

May through December 2000. The iris portion used Iridians IriScan 

System 2200. The FTER was 0.5 percent. The FMR was 0 percent and the 

iris recognition system had an FNMR of 1.9 percent. Additional 

experimental results were that:



* the iris system had a mean transaction time of 12 seconds, a median 

of 10 seconds, and a minimum of 4 seconds;



* the matching algorithm could make 1.5 million matches per minute when 

using a SunUltra5 with a SunOS 5.8 operating system, 270 MHz processor, 

and 128 Mb of memory; and:



* people without glasses had a lower FNMR than those with glasses.



U.S. Army Research Laboratory:



The U.S. Army Research Laboratory recently tested an Iridian 

verification system. There were 186,918 eye identification attempts on 

93,459 registrations. The FMR was well below 1 percent. Despite the 

vendors claims of greater than 99.5 percent correct identification, 

the testing showed a 6 percent FNMR; glare and reflections appeared to 

be primary culprits in this discrepancy. User settling and distraction 

also contributed to the problem.



Sandia National Laboratories:



In April 1996, Sandia National Laboratories evaluated a prototype 

biometric recognition system provided by IriScan. Average enrollment 

time was 2 minutes and 15 seconds. During the first phase of the test, 

there was a raw FNMR of 11.8 percent. After removing the errors that 

could be attributed to extreme environmental conditions or deliberate 

misuse, the FNMR became 10.2 percent. The average transaction time of a 

sampling of transactions was 14 seconds. The minimum transaction time 

recorded was 6 seconds, the maximum 23 seconds. Users attempted 96 

false match transactions with no actual false matches. Overall, the 

researchers concluded that the system performed extremely well in 

difficult conditions.



ct Magazine:



Researchers at ct Magazine in Germany set out to see whether they 

could fool Panasonics Authenticam BM-ET100, a desktop iris recognition 

system. The investigators first attempts to spoof the system by using 

iris images projected on monitors failed because of the too intense 

reflection of light. However, they succeeded in beating the system by 

holding up to the camera a high-resolution picture of an iris with a 

tiny hole cut out to allow the pupil of a live eye shine through. They 

also found it possible to enroll with the aid of this artificial eye. 

From that point on, anyone in possession of the eye pattern was able to 

log on to the system. Moreover, the system also matched the iris of the 

person whose picture had been used to create the artificial eye with 

the enrolled reference template.



Border Control Applications Piloted and Deployed:



United Kingdom:



Iris recognition has been used in some border control environments. For 

example, beginning in July 2001, frequent travelers on transatlantic 

Virgin Atlantic Airways and British Airways flights have been able to 

bypass passport control at Londons Heathrow Airport, without waiting 

in line for an immigration agent. In trial runs, 2,000 American and 

Canadian passengers have undergone identity checks by British 

immigration officers before being enrolled. Once registered and 

enrolled, they can proceed, as arriving passengers, directly to 

specific lanes to verify their identity against a biometric template 

stored in a central database (see figures 53 and 54). If the 

verification is successful, they are issued a ticket admitting them 

directly to the United Kingdom. The trial is being operated by the 

airlines and involves no changes to passports.



Figure 53: Iris Recognition Device for Border Control at Londons 

Heathrow Airport:



Source: EyeTicket Corporation.



[See PDF for image]



[End of figure]





Figure 54: Border Control Lane with Iris Recognition Device at Londons 

Heathrow Airport:



Source: EyeTicket Corporation.



[See PDF for image]



[End of figure]



Canada:



The Canada Customs and Revenue Agency has initiated the Expedited 

Passenger Processing System, which will include iris recognition 

technology. The system will allow frequent travelers to expedite 

inspection. It is planned to be operational at Lester B. Pearson 

International Airport in Toronto and Vancouver International Airport at 

the beginning of 2003. An enrollment of about 200,000 spread out over 5 

years is expected. The plan is to use a central database for storing 

the iris templates. Initially, it was not clear whether computer 

performance would allow for a central database, so provision was made 

for a token to store the biometric. However, testing has shown that 

doing the checks centrally does not significantly affect performance 

time. Either one-to-one matches (with an identifying token) will be 

made or one-to-many, with the system identifying applicants by the iris 

match.



Netherlands:



In October 2001, an iris recognition system was installed at 

Amsterdams Schiphol Airport. The system expedites the way for 

travelers from 18 European countries into the Netherlands and includes 

about 2,000 frequent travelers. Users must go through a two-phase 

process. First, passengers undergo a background check, a passport 

review, and an iris scan. The template is encrypted and embedded on a 

smart card. This phase takes about 15 minutes. The second phase 

identifies and verifies each registered traveler at the immigration 

checkpoint. The travelers reference template is compared with a real-

time scan of the iris. This process typically takes about 10 to 15 

seconds and allows the passenger to bypass long immigration lines. The 

Schiphol program charges each enrolled traveler a yearly fee of $89 to 

use the system. The FNMR is less than 1 percent; the FMR is less than 

0.001 percent.



Singapore:



Iris recognition is used to admit workers who travel into Singapore 

from Malaysia each day by motorcycle. The workers irises are scanned 

by a camera installed in kiosks in designated lanes, instead of their 

having to present their paperwork to an official. About 50,000 workers 

cross the border each day.



Saudi Arabia:



In February 2002, at the King Abdul Aziz Airport in Jeddah, Saudi 

Arabia, iris recognition tracked and identified visitors who were on 

pilgrimage for the Hajj season of worship. The process included a 

random check at passport control, enrollment into a database, and 

subsequent identification on departure. The systems were in place to 

ensure that visitors did not overstay their visas and also to identify 

potential security threats. It is estimated that images of 20,000 to 

30,000 irises were collected.



Processing Issues:



Although iris recognition systems can perform both one-to-many 

identification and one-to-one verification, they are deployed primarily 

for identification. In some processors, iris recognition technology can 

search hundreds of thousands of records per second. Very few biometrics 

have the capability of iris recognition for a high-speed exhaustive 

search of a database.



Device Durability and Environmental Constraints:



Because iris recognition systems use infrared illumination, they can be 

used in the dark. Their durability depends greatly on the 

specifications of the systems individual components.



[End of section]



Appendix VI Cost Estimates for Using Biometrics for Border Security:



For each of the four scenarios, we created cost models to estimate the 

cost of developing, implementing, and maintaining various biometric 

systems. Besides including the cost of purchasing the biometric 

hardware, we estimated costs for additional hardware, software, 

maintenance, personnel, training, and effects on other procedures in 

order to derive life-cycle cost estimates. We followed the cost element 

structure that DOD uses at acquisition program milestone and decision 

reviews to assess major automated information systems costs. Tailoring 

this structure to reflect our four scenarios, we used it to standardize 

costs so that they could be compared at a high level. We present the 

costs in two parts. Initial costs represent the costs required to plan, 

design, develop, and field the system. Recurring costs represent the 

annual costs required to operate and continually maintain the system to 

keep it in operation.



Initial Cost Elements:



We estimated seven sets of initial cost elements: costs for systems 

engineering and program management; development, installation, and 

training; biometric hardware; biometric software; network 

infrastructure; renovating consular facilities; and hardware 

infrastructure upgrades.



Systems engineering and program management costs included both program 

management activities and government in-house engineering efforts to 

design, develop, and test the biometric system. For the watch list 

scenarios, we used an engineering build-up of personnel and their 

respective costs. For issuing visas and passports with biometrics, we 

used an overall factor of the total initial cost to estimate this 

effort.



Development, installation, and training costs included all resources 

required to design, develop, test, and implement a biometric system. 

For the watch list scenarios, we used an analogy to the Consular 

Lookout and Support System (CLASS) to estimate the cost of developing 

and implementing a watch list database. For issuing visas and passports 

with biometrics, we used an analogy to IAFIS and applied an engineering 

scaling factor to account for additional biometric storage space.



Biometric hardware costs included costs for biometric scanners, token 

card readers, and token cards for storing biometric data as well as 

costs for the personal computers to make these devices function 

properly. To estimate costs, we used average vendor costs where 

available and, in other cases, we relied on expert opinion.



Biometric software costs included the licensing cost for biometric 

scanners, card readers, and database software. For the watch list 

scenarios, we used cost estimates provided by the State Department, 

based on analogy to CLASS. For issuing visas and passports with 

biometrics, we assumed this cost was already included in the 

development cost for IAFIS.



Network infrastructure included costs associated with purchasing and 

installing the local area networks needed to establish the connectivity 

required by the biometric systems. For the watch list scenarios, we 

used cost estimates provided by the State Department, based on an 

analogy to CLASS. For issuing visas and passports with biometrics, we 

used an analogy to a trusted traveler cost estimate developed by IBG.



To issue visas with biometrics, additional space at the consulates and 

embassies will be required to accommodate the new process of capturing 

applicants biometrics. For the watch list scenarios, the consular 

facility cost is for the renovation of primary and contingency space 

for the new computer systems. We used square foot data provided by the 

State Department to estimate this cost. We did not include costs for 

the collection of biometrics at passport acceptance offices because 

most of these are not State Department facilities, and we had no basis 

on which to estimate the appropriate amount of space for these offices.



Hardware infrastructure upgrades included the cost to refresh hardware 

every 3 years. To estimate this element, we calculated the cost to 

replace one-third of the hardware annually, an accepted industry 

standard and the practice for the State Departments visa and passport 

sites.



Recurring Cost Elements:



We estimated 10 sets of recurring cost elements: program management, 

biometric hardware maintenance, software and system maintenance, 

network infrastructure maintenance, consular operating personnel, port 

of entry operating personnel, communications, training, consular 

facility maintenance, and annual supplies.



Program management included the cost of providing continuing program 

management over the systems useful life. To estimate this cost for the 

watch list scenarios, we used an engineering build-up of personnel and 

their respective costs. For issuing visas and passports with 

biometrics, we estimated this cost to be 20 percent of the initial 

systems engineering and program management cost.



Biometric hardware maintenance included the cost of providing 

maintenance and repair for the biometric and system hardware. We used 

an average factor of 12.5 percent, based on a 10 percent to 15 percent 

range IBG provided in its trusted traveler cost estimate.



Software and system maintenance costs included annual software 

licensing for databases plus costs for personnel to upgrade and 

maintain them. For the watch list scenarios, we used an engineering 

build-up of personnel and their respective costs. For issuing visas and 

passports with biometrics, we used an analogy to IAFIS annual system 

costs, applying the engineering scaling factor to account for 

additional database storage of the various biometrics.



Network infrastructure maintenance included the cost of providing 

hardware and software maintenance for the network. For the watch list 

scenarios, we used data from the State Department, based on its 

experience from CLASS. For issuing visas and passports with biometrics, 

we used the same factor of 12.5 percent that was used for estimating 

hardware maintenance.



The costs for consular operating personnel are for visa operating 

personnel at embassies and consulates around the world or for passport 

operating personnel at passport acceptance offices. For the checking of 

a biometric watch list before issuing visa, we estimated that one 

additional staff member per embassy or consulate would be required to 

resolve watch list hits. We did not include additional staff for 

checking a biometric watch list before issuing a passport. For the 

issuance of visas with biometrics, we first estimated the number of 

personnel needed at the consulates, using time to capture the 

biometrics as a variable. We then estimated the cost for the foreign 

service nationals who would perform the capturing, the foreign service 

officers who would oversee them, and auxiliary consulate staff to 

assist during peak load times. The annual costs for all visa operating 

personnel and the one-time moving costs for new foreign service 

nationals and officers were provided to us by the State Department. For 

the issuance of passports with biometrics, we assumed one staff member 

per passport acceptance office to troubleshoot problems with the 

biometric equipment.



Port of entry operating personnel include staff to troubleshoot 

biometrics at ports of entry. To estimate costs for these personnel, we 

made the assumption that there would be three staff per port of entry 

who would be trained and able to troubleshoot problems arising from 

biometric capturing or the inability to match biometric data.



The costs of communications included the cost of maintaining a wide 

area network able to provide secure electronic connectivity from the 

consular and port of entry sites to a headquarters location for 

comparing biometrics. To estimate this element, we used an analogy to 

IAFIS communication costs with a cost-per-location methodology.



Training included the costs to train personnel in using biometrics, 

including the cost of travel. We used an average of $5,000 per staff 

annually to estimate this cost.



The cost of maintaining consular facilities included maintaining newly 

acquired space. We used data on cost per square foot provided by the 

State Department.



In estimating the cost of annual supplies, we included the cost to 

purchase biometric token cards for the storage of biometrics collected 

for issuing passports and visas. This cost also includes the amortized 

cost of the infrastructure required to produce the cards, including 

elements such as centralized certificate issuance servers, key 

management components, and the card management infrastructure. We used 

data provided by the State Department for the Mexican border crossing 

card.



Assumptions:



We prepared the life-cycle cost estimates using fiscal year 2002 

constant dollarsthat is, inflation was not considered for the multiple 

years over which funds would be required for acquisitionand they 

represent rough order of magnitude costs. Following are the assumptions 

that frame the boundary of our cost estimates.



* Scenario life-cycle cost estimates represent development and 

installation time plus 10 years operational life.



* Phasing of costs over time is simplified, and actual schedules to 

both develop and install equipment and infrastructure will most likely 

differ.



* Biometric technologiesfingerprint, facial, and iris 

recognitionrepresent standardization to a single vendors protocols. 

Biometric technology costs represent the average costs of vendors 

products. Four flat fingerprints will be collected for fingerprint 

recognition.



* There are 210 visa-issuing embassies and consulates worldwide. There 

are 4,500 passport acceptance offices. There are 3,950 primary and 

secondary inspection stations at 400 ports of entry.



* Personnel costs reflect both direct costs and indirect costs. Three 

personnel will be needed to troubleshoot equipment at ports of entry, 

or 1,200 additional staff.



No costs were estimated for:



* additional inspectors at ports of entry,



* additional facility space for passport acceptance offices or at ports 

of entry for primary and secondary inspections,



* biometric equipment for exiting the United States, and:



* biometric security technology (e.g., encryption of biometric data).



Estimated Costs for Conducting Watch List Checks with Biometrics:



We used the following assumptions to create the cost estimates for the 

two biometric watch list scenarios:



* The watch list database will include 10 million records.



* Matches will be performed using facial recognition technology.



* To conduct watch list checks before issuing travel documents, facial 

images will be generated by capturing the physical photographs 

applicants present when they apply for a visa or passport.



* The images will be collected and scanned at consulates and embassies 

for visas and at passport acceptance offices and transmitted through 

telecommunications resources to a central facility in metropolitan 

Washington, D.C.



The estimated costs for conducting biometric watch list checks before 

travelers are issued travel documents and before they enter the country 

are shown in table 26.



Table 26: Estimated Costs for Watch List Checks before Issuing Travel 

Documents and before Entering the United States:



[See PDF for image]



Note: In thousands of fiscal year 2002 constant dollars.



[A] Numbers do not sum because of rounding.



Source: GAO analysis.



[End of table]



Estimated Costs for Issuing Visas with Biometrics:



We developed cost estimates for six different combinations of biometric 

technologies under two different possibilities for issuing visas. The 

State Department receives about 10.3 million visa applications each 

year. In fiscal year 2000, INS estimated that approximately 14 million 

individuals traveled under the visa waiver program. If these travelers 

must obtain a visa to travel to the United States, we assume that this 

same number would also be required to have their biometric sample 

collected. We used the following assumptions to estimate the costs of 

adding biometrics to visas:



* The number of visa applicants will remain constant at 10.3 million 

annually. The number of travelers in the visa waiver program will 

remain constant at 14 million annually.



* Enrolling travelers using a single biometric (whether for 

fingerprint, facial, or iris recognition) is estimated at 6 minutes (10 

applicants enrolled per hour).



* Enrolling travelers using multiple biometrics (for example, 

fingerprint and facial combined, fingerprint and iris combined, or 

fingerprint, facial, and iris combined) is estimated at 10 minutes (6 

applicants enrolled per hour).



* All current visa-issuing embassies and consulates will be equipped to 

collect biometrics from visa applicants.



Costs were not included for additional inspectors or facility space at 

ports of entry. Tables 27-32 show the cost of issuing visas with 

biometrics using fingerprint recognition, iris recognition, facial 

recognition, fingerprint and iris recognition, fingerprint and facial 

recognition, and fingerprint, iris, and facial recognition.



Table 27: Estimated Costs for Issuing Visas with Biometrics Using 

Fingerprint Recognition:



Cost element: Investment costs; Cost element: [Empty]; Annual visa 

applicants: 10.3 million with visa waiver program: Annual recurring 
cost: 

[Empty]; Annual visa applicants: [Empty]; Annual visa applicants: 24.3 

million without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Cost element: 

$111,147; Annual visa applicants: 10.3 million

with visa waiver program: Annual 

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: $145,645; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Development; installation; training; Cost element: 

527,655; Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 558,936; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric hardware; Cost element: 219,033; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 443,241; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Cost element: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Cost element: 152,500; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 152,500; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Cost element: 335,781; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 463,606; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: $79,114; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $93,986.



Cost element: Operations and support; Cost element: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Program management; Cost element: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 22,229; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 29,129.



Cost element: Biometric hardware maintenance; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 10,905; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 16,538.



Cost element: Software and system maintenance; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 73,123; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 125,292.



Cost element: Network infrastructure maintenance; Cost element: 

[Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 19,063; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 19,063.



Cost element: Visa operating personnel; Cost element: 75,926; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 111,626; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 114,903; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 150,603.



Cost element: Port of entry operating personnel; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 94,679; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 94,679.



Cost element: Communications; Cost element: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 20,577; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 20,577.



Cost element: Recurring training; Cost element: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 32,472; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 38,040.



Cost element: Consular facility maintenance; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 89,541; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 123,628.



Cost element: Annual supplies (cards); Cost element: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 154,809; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 365,229.



Cost element: Total; Cost element: $1,422,042; Annual visa applicants: 

10.3 million

with visa waiver program: Annual

recurring cost: $708,138; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: $1,878,832; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $1,076,765.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Table 28: Estimated Costs for Issuing Visas with Biometrics Using Iris 

Recognition:



Annual visa applicants: Annual visa applicants : 10.3 million

with visa waiver program: [Empty].



Cost element: Investment; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Annual visa 

applicants: 10.3 million

with visa waiver program: Initial cost: $110,925; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: $145,375; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Development; installation; training; Annual visa 

applicants: 10.3 million

with visa waiver program: Initial cost: 527,655; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 558,936; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric hardware; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: 216,563; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 440,240; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: 152,500; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 152,500; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: 335,781; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 463,606; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: $78,298; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $92,996.



Cost element: Operations and support; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Program management; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 22,185; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 29,075.



Cost element: Biometric hardware maintenance; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 10,596; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 16,163.



Cost element: Software and system maintenance; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 73,123; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 125,292.



Cost element: Network infrastructure maintenance; Annual visa 

applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 19,063; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 19,063.



Cost element: Visa operating personnel; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: 75,926; Annual visa applicants: 

10.3 million

with visa waiver program: Annual recurring cost: 111,626; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 114,903; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 150,603.



Cost element: Port of entry operating personnel; Annual visa 

applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 94,679; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 94,679.



Cost element: Communications; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 20,577; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 20,577.



Cost element: Recurring training; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 32,472; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 38,040.



Cost element: Consular facility maintenance; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 89,541; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 123,628.



Cost element: Annual supplies (cards); Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 154,809; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 365,229.



Cost element: Total; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: $1,419,349; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: $706,970; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: $1,875,562; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $1,075,346.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Table 29: Estimated Costs for Issuing Visas with Biometrics Using 

Facial Recognition:



Annual visa applicants: Annual visa applicants : 10.3 million

with visa waiver program: [Empty].



Cost element: Investment: [Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: Investment: [Empty]; 

Annual visa applicants: Investment: [Empty]; Annual visa applicants: 

24.3 million

without visa waiver program: Initial cost: Investment: [Empty]; Annual 

visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: Investment: [Empty].



Cost element: Investment: Systems engineering and program management; 

Cost element: $109,258; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: $143,350; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Investment: Development; installation; training; Cost 

element: 527,655; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 558,936; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Investment: Initial biometric hardware; Cost element: 

198,037; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 417,737; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Investment: Initial biometric software; Cost element: 

[Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Investment: Network infrastructure; Cost element: 

152,500; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 152,500; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Investment: Consular facility renovation; Cost element: 

335,781; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 463,606; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Investment: Hardware infrastructure upgrade; Cost 

element: [Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: $72,185; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $85,570.



Cost element: Investment: Operations and support; Cost element: 

[Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Investment: Program management; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 21,852; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 28,670.



Cost element: Investment: Biometric hardware maintenance; Cost element: 

[Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 8,280; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 13,350.



Cost element: Investment: Software and system maintenance; Cost 

element: [Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 73,123; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 125,292.



Cost element: Investment: Network infrastructure maintenance; Cost 

element: [Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 19,063; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 19,063.



Cost element: Investment: Visa operating personnel; Cost element: 

75,926; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 111,626; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: 114,903; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 150,603.



Cost element: Investment: Port of entry operating personnel; Cost 

element: [Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 94,679; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 94,679.



Cost element: Investment: Communications; Cost element: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 20,577; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 20,577.



Cost element: Investment: Recurring training; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 32,472; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 38,040.



Cost element: Investment: Consular facility maintenance; Cost element: 

[Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 89,541; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 123,628.



Cost element: Investment: Annual supplies (cards); Cost element: 

[Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual recurring cost: 154,809; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 365,229.



Cost element: Investment: Total; Cost element: $1,399,156; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: $698,207; Annual visa 

applicants: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Initial cost: $1,851,033; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $1,064,702.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Table 30: Estimated Costs for Issuing Visas with Biometrics Using 

Fingerprint and Iris Recognition:



Cost element: Investment; Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Initial cost: 

$151,218; Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: $193,935; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Development; installation; training; Initial cost: 

820,165; Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 867,087; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric hardware; Initial cost: 253,098; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 495,336; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Initial cost: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Initial cost: 228,750; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 228,750; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Initial cost: 378,188; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 563,655; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Initial cost: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: $119,315; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $145,299.



Cost element: Operations and support; Initial cost: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Program management; Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 30,244; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 38,787.



Cost element: Biometric hardware maintenance; Initial cost: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 16,601; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 26,444.



Cost element: Software and system maintenance; Initial cost: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 96,591; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 176,331.



Cost element: Network infrastructure maintenance; Initial cost: 

[Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 28,594; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 28,594.



Cost element: Visa operating personnel; Initial cost: 95,044; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 130,744; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: 160,006; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 195,706.



Cost element: Port of entry operating personnel; Initial cost: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 94,679; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 94,679.



Cost element: Communications; Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 20,577; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 20,577.



Cost element: Recurring training; Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 70,405; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 88,966.



Cost element: Consular facility maintenance; Initial cost: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 100,850; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 150,308.



Cost element: Annual supplies (cards); Initial cost: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 154,809; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 365,229.



Cost element: Total; Initial cost: $1,926,463; Annual visa applicants: 

10.3 million

with visa waiver program: Annual

recurring cost: $863,409; Annual visa applicants: [Empty]; Annual visa 

applicants: 24.3 million

without visa waiver program: Initial cost: $2,508,769; Annual visa 

applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $1,330,920.



Note: In thousands of fiscal year 2002 constant dollars.



Source: GAO analysis.



[End of table]



Table 31: Estimated Costs for Issuing Visas with Biometrics Using 

Fingerprint and Facial Recognition:



Annual visa applicants: Annual visa applicants : 10.3 million

with visa waiver program: [Empty].



Cost element: Investment; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Annual visa 

applicants: 10.3 million

with visa waiver program: Initial cost: $149,375; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: $191,495; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Development; installation; training; Annual visa 

applicants: 10.3 million

with visa waiver program: Initial cost: 820,165; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: 867,087; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric hardware; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: 232,621; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: 468,231; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: 228,750; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: 228,750; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: 378,188; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: 563,655; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: $112,557; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $136,354.



Cost element: Operations and support; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: [Empty]; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Program management; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 29,875; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 38,299.



Cost element: Biometric hardware maintenance; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 14,042; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 23,056.



Cost element: Software and system maintenance; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 96,591; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 176,331.



Cost element: Network infrastructure maintenance; Annual visa 

applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 28,594; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 28,594.



Cost element: Visa operating personnel; Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: 95,044; Annual visa applicants: 

10.3 million

with visa waiver program: Annual recurring cost: 130,744; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: 160,006; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 195,706.



Cost element: Port of entry operating personnel; Annual visa 

applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 94,679; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 94,679.



Cost element: Communications; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 20,577; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 20,577.



Cost element: Recurring training; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 70,405; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 88,966.



Cost element: Consular facility maintenance; Annual visa applicants: 

10.3 million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 100,850; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 150,308.



Cost element: Annual supplies (cards); Annual visa applicants: 10.3 

million

with visa waiver program: Initial cost: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: 154,809; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: [Empty]; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 365,229.



Cost element: Total; Annual visa applicants: 10.3 million

with visa waiver program: Initial cost: $1,904,143; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual recurring cost: $853,723; Annual visa 

applicants: [Empty]; Annual visa applicants: Initial cost: $2,479,223; 

Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $1,318,099.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Table 32: Estimated Costs for Issuing Visas with Biometrics Using 

Fingerprint, Iris, and Facial Recognition:



Annual visa applicants: Annual visa applicants : 10.3 million

with visa waiver program: [Empty].



Cost element: Investment; Cost element: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Systems engineering and program mnagement; Cost element: 

$177,371; Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: $221,694; Annual visa applicants: 24.3 

million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Development; installation; training; Cost element: 

1,027,676; Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: 1,090,238; Annual visa applicants: 24.3 

million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric hardware; Cost element: 259,924; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: 504,372; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Cost element: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Cost element: 305,000; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: 305,000; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Cost element: 378,188; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: 563,655; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: $150,527; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: $182,402.



Cost element: Operations and support; Cost element: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: [Empty]; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: [Empty].



Cost element: Program management; Cost element: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 35,474; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 44,339.



Cost element: Biometric hardware maintenance; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 18,893; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 30,967.



Cost element: Software and system maintenance; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 119,661; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 226,432.



Cost element: Network infrastructure maintenance; Cost element: 

[Empty]; Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 38,125; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 38,125.



Cost element: Visa operating personnel; Cost element: 95,044; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 130,744; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: 160,006; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 195,706.



Cost element: Port of entry operating personnel; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 94,679; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 94,679.



Cost element: Communications; Cost element: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 20,577; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 20,577.



Cost element: Recurring training; Cost element: [Empty]; Annual visa 

applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 105,608; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 133,449.



Cost element: Consular facility maintenance; Cost element: [Empty]; 

Annual visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 100,850; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 150,308.



Cost element: Annual supplies (cards); Cost element: [Empty]; Annual 

visa applicants: 10.3 million

with visa waiver program: Annual

recurring cost: 154,809; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: [Empty]; Annual visa applicants: 24.3 million

without visa waiver program: Annual

recurring cost: 365,229.



Cost element: Total; Cost element: $2,243,202; Annual visa applicants: 

10.3 million

with visa waiver program: Annual

recurring cost: $969,947; Annual visa applicants: [Empty]; Annual visa 

applicants: Initial cost: $2,844,964; Annual visa applicants: 24.3 

million

without visa waiver program: Annual

recurring cost: $1,482,212.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Estimated Costs for Issuing Passports with Biometrics:



We used the following assumptions to estimate the costs of adding 

biometrics to passports:



* The number of passport applicants will remain constant at 7 million 

annually.



* Enrolling travelers using a single biometric (whether for 

fingerprint, facial, or iris recognition) is estimated at 6 minutes (10 

applicants enrolled per hour).



* Enrolling travelers using multiple biometrics (for example, 

fingerprint and facial combined, fingerprint and iris combined, or 

fingerprint, facial, and iris combined) is estimated at 10 minutes (6 

applicants enrolled per hour).



* All current passport acceptance offices will be equipped to collect 

biometrics passport applicants.



Costs were not included for additional inspectors or facility space at 

ports of entry. Costs are also not included for additional facility 

space at passport acceptance offices.



Tables 33-38 show the cost of issuing passports with biometrics using 

fingerprint recognition, iris recognition, facial recognition, 

fingerprint and iris recognition, fingerprint and facial recognition, 

and fingerprint, iris, and facial recognition.



Table 33: Estimated Costs for Issuing Passports with Biometrics Using 

Fingerprint Recognition:



Cost element: Investment; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Initial cost: 

$370,797; Annual recurring cost: [Empty].



Cost element: Development; installation; training; Initial cost: 

2,665,282; Annual recurring cost: [Empty].



Cost element: Initial biometric hardware; Initial cost: 229,685; Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Initial cost: 1,225,000; Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Initial cost: [Empty]; 

Annual recurring cost: $450,488.



Cost element: Operations and support; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Program management; Initial cost: [Empty]; Annual

recurring cost: 74,159.



Cost element: Biometric hardware maintenance; Initial cost: [Empty]; 

Annual recurring cost: 17,514.



Cost element: Software and system maintenance; Initial cost: [Empty]; 

Annual recurring cost: 58,146.



Cost element: Network infrastructure maintenance; Initial cost: 

[Empty]; Annual recurring cost: 153,125.



Cost element: Passport operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 443,805.



Cost element: Port of entry operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 94,679.



Cost element: Communications; Initial cost: [Empty]; Annual

recurring cost: 122,962.



Cost element: Recurring training; Initial cost: [Empty]; Annual

recurring cost: 53,875.



Cost element: Consular facility maintenance; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Annual supplies (cards); Initial cost: [Empty]; Annual

recurring cost: 105,210.



Cost element: Total; Initial cost: $4,490,764; Annual

recurring cost: $1,573,965.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Table 34: Estimated Costs for Issuing Passports with Biometrics Using 

Iris Recognition:



Cost element: Investment; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Initial cost: 

$370,366; Annual recurring cost: [Empty].



Cost element: Development; installation training; Initial cost: 

2,665,282; Annual recurring cost: [Empty].



Cost element: Initial biometric hardware; Initial cost: 224,898; Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Initial cost: 1,225,000; Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Initial cost: [Empty]; 

Annual recurring cost: $448,908.



Cost element: Operations and support; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Program management; Initial cost: [Empty]; Annual

recurring cost: 74,073.



Cost element: Biometric hardware maintenance; Initial cost: [Empty]; 

Annual recurring cost: 16,916.



Cost element: Software and system maintenance; Initial cost: [Empty]; 

Annual recurring cost: 58,146.



Cost element: Network infrastructure maintenance; Initial cost: 

[Empty]; Annual recurring cost: 153,125.



Cost element: Passport operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 443,805.



Cost element: Port of entry operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 94,679.



Cost element: Communications; Initial cost: [Empty]; Annual

recurring cost: 122,962.



Cost element: Recurring training; Initial cost: [Empty]; Annual

recurring cost: 53,875.



Cost element: Consular facility maintenance; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Annual supplies (cards); Initial cost: [Empty]; Annual

recurring cost: 105,210.



Cost element: Total; Initial cost: $4,485,545; Annual

recurring cost: $1,571,700.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Table 35: Estimated Costs for Issuing Passports with Biometrics Using 

Facial Recognition:



Cost element: Investment; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Initial cost: 

$367,135; Annual recurring cost: [Empty].



Cost element: Development; installation; training; Initial cost: 

2,665,282; Annual recurring cost: [Empty].



Cost element: Initial biometric hardware; Initial cost: 188,991; Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Initial cost: 1,225,000; Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Initial cost: [Empty]; 

Annual recurring cost: $437,059.



Cost element: Operations and support; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Program management; Initial cost: [Empty]; Annual

recurring cost: 73,427.



Cost element: Biometric hardware maintenance; Initial cost: [Empty]; 

Annual recurring cost: 12,428.



Cost element: Software and system maintenance; Initial cost: [Empty]; 

Annual recurring cost: 58,146.



Cost element: Network infrastructure maintenance; Initial cost: 

[Empty]; Annual recurring cost: 153,125.



Cost element: Passport operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 443,805.



Cost element: Port of entry operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 94,679.



Cost element: Communications; Initial cost: [Empty]; Annual

recurring cost: 122,962.



Cost element: Recurring training; Initial cost: [Empty]; Annual

recurring cost: 53,875.



Cost element: Consular facility maintenance; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Annual supplies (cards); Initial cost: [Empty]; Annual

recurring cost: 105,210.



Cost element: Total; Initial cost: $4,446,407; Annual

recurring cost: $1,554,716.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Table 36: Estimated Costs for Issuing Passports with Biometrics Using 

Fingerprint and Iris Recognition:



Initial cost: Cost element Investment: [Empty]; Annual

recurring cost: Cost element Investment: [Empty].



Cost element: Investment: Systems engineering and program management; 

Initial cost: $552,750; Annual recurring cost: [Empty].



Cost element: Investment: Development; installation; training; Initial 

cost: 4,026,605; Annual recurring cost: [Empty].



Cost element: Investment: Initial biometric hardware; Initial cost: 

277,560; Annual recurring cost: [Empty].



Cost element: Investment: Initial biometric software; Initial cost: 

[Empty]; Annual recurring cost: [Empty].



Cost element: Investment: Network infrastructure; Initial cost: 

1,837,500; Annual recurring cost: [Empty].



Cost element: Investment: Consular facility renovation; Initial cost: 

[Empty]; Annual recurring cost: [Empty].



Cost element: Investment: Hardware infrastructure upgrade; Initial 

cost: [Empty]; Annual recurring cost: $670,993.



Cost element: Investment: Operations and support; Initial cost: 

[Empty]; Annual recurring cost: [Empty].



Cost element: Investment: Program management; Initial cost: [Empty]; 

Annual recurring cost: 110,550.



Cost element: Investment: Biometric hardware maintenance; Initial cost: 

[Empty]; Annual recurring cost: 24,476.



Cost element: Investment: Software and system maintenance; Initial 

cost: [Empty]; Annual recurring cost: 67,777.



Cost element: Investment: Network infrastructure maintenance; Initial 

cost: [Empty]; Annual recurring cost: 229,688.



Cost element: Investment: Passport operating personnel; Initial cost: 

[Empty]; Annual recurring cost: 443,805.



Cost element: Investment: Port of entry operating personnel; Initial 

cost: [Empty]; Annual recurring cost: 94,679.



Cost element: Investment: Communications; Initial cost: [Empty]; Annual

recurring cost: 122,962.



Cost element: Investment: Recurring training; Initial cost: [Empty]; 

Annual recurring cost: 107,750.



Cost element: Investment: Consular facility maintenance; Initial cost: 

[Empty]; Annual recurring cost: [Empty].



Cost element: Investment: Annual supplies (cards); Initial cost: 

[Empty]; Annual recurring cost: 105,210.



Cost element: Investment: Total; Initial cost: $6,694,415; Annual

recurring cost: $1,977,890.



Note: In thousands of fiscal year 2002 constant dollars.



Source: GAO analysis.



[End of table]



Table 37: Estimated Costs for Issuing Passports with Biometrics Using 

Fingerprint and Facial Recognition:



Cost element: Investment; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Initial cost: 

$549,518; Annual recurring cost: [Empty].



Cost element: Development; installation; training; Initial cost: 

4,026,605; Annual recurring cost: [Empty].



Cost element: Initial biometric hardware; Initial cost: 241,654; Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Initial cost: 1,837,500; Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Initial cost: [Empty]; 

Annual recurring cost: $659,144.



Cost element: Operations and support; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Program management; Initial cost: [Empty]; Annual

recurring cost: 109,904.



Cost element: Biometric hardware maintenance; Initial cost: [Empty]; 

Annual recurring cost: 19,988.



Cost element: Software and system maintenance; Initial cost: [Empty]; 

Annual recurring cost: 67,777.



Cost element: Network infrastructure maintenance; Initial cost: 

[Empty]; Annual recurring cost: 229,688.



Cost element: Passport operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 443,805.



Cost element: Port of entry operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 94,679.



Cost element: Communications; Initial cost: [Empty]; Annual

recurring cost: 122,962.



Cost element: Recurring training; Initial cost: [Empty]; Annual

recurring cost: 107,750.



Cost element: Consular facility maintenance; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Annual supplies (cards); Initial cost: [Empty]; Annual

recurring cost: 105,210.



Cost element: Total; Initial cost: $6,655,277; Annual

recurring cost: $1,960,906.



Note: In thousands of fiscal year 2002 constant dollars. Numbers do not 

sum because of rounding.



Source: GAO analysis.



[End of table]



Table 38: Estimated Costs for Issuing Passports with Biometrics Using 

Fingerprint, Iris, and Facial Recognition:



Cost element: Investment; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Systems engineering and program management; Initial cost: 

$723,821; Annual recurring cost: [Empty].



Cost element: Development; installation; training; Initial cost: 

5,302,929; Annual recurring cost: [Empty].



Cost element: Initial biometric hardware; Initial cost: 289,529; Annual

recurring cost: [Empty].



Cost element: Initial biometric software; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Network infrastructure; Initial cost: 2,450,000; Annual

recurring cost: [Empty].



Cost element: Consular facility renovation; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Hardware infrastructure upgrade; Initial cost: [Empty]; 

Annual recurring cost: $879,648.



Cost element: Operations and support; Initial cost: [Empty]; Annual

recurring cost: [Empty].



Cost element: Program management; Initial cost: [Empty]; Annual

recurring cost: 144,764.



Cost element: Biometric hardware maintenance; Initial cost: [Empty]; 

Annual recurring cost: 26,950.



Cost element: Software and system maintenance; Initial cost: [Empty]; 

Annual recurring cost: 77,407.



Cost element: Network infrastructure maintenance; Initial cost: 

[Empty]; Annual recurring cost: 306,250.



Cost element: Passport operating personnel; Initial cost: [Empty]; 

Annual recurring cost: 443,807.



Cost element: Port of entry operating personnel; Initial cost: [Empty]; 

Annual  recurring cost: 94,679.



Cost element: Communications; Initial cost: [Empty]; Annual

recurring cost: 122,962.



Cost element: Recurring training; Initial cost: [Empty]; Annual

recurring cost: 161,625.



Cost element: Consular facility maintenance; Initial cost: [Empty]; 

Annual recurring cost: [Empty].



Cost element: Annual supplies (cards); Initial cost: [Empty]; Annual

recurring cost: 105,210.



Cost element: Total; Initial cost: $8,766,279; Annual

recurring cost: $2,363,302.



Note: In thousands of fiscal year 2002 constant dollars.



Source: GAO analysis.



[End of table]



[End of section]



Appendix VII: Comments from the U.S. Department of State:



United States Department of State:



Washington, D.C., 20520:



Dear Ms. Westin:



We appreciate the opportunity to review your draft report, TECHNOLOGY 

ASSESSMENT: Using Biometrics for Border Security, GAO-02-952, GAO Job 

Code 460525.



The enclosed Department of State comments are provided for 

incorporation with this letter as an appendix to the final report, as 

well as technical comments.



If you have any questions concerning this response, please contact 

Columbia Barrosse, Office of Executive Director, Bureau of Consular 

Affairs, at (202) 663-2504.



Christopher B. Burnham

Assistant Secretary and Chief Financial Officer:



Signed by Christopher B. Burnham



Enclosure:



As stated.



cc: GAO/IAT - Mr. Richard Hung State/OIG - Mr. Berman State/CA - Mr. 

Frank Moss:



Ms. Susan S. Westin, Managing Director, International Affairs and 

Trade, U.S. General Accounting Office.



Department of State Comments on GAO Draft Report:



TECHNOLOGY ASSESSMENT: Using Biometrics for Border Security (GAO-02-

952, Job Code 460525):



The Department appreciates the thorough and balanced approach taken by 

GAO in its assessment of the use of biometrics for Border Security. We 

find the overall thrust of the report to be in keeping with our own 

considerations of how a biometrics component could be used in the 

admission of individuals into the United States and how it could be 

integrated into the existing process for visa and passport 

applications. We are particularly gratified to see the GAO report 

stress the need for high-level policy decisions to be made prior to 

execution of a biometrics program. Foremost among these are: a decision 

regarding the specific uses to be made of the biometrics data 

(identification of individuals, exclusion of dangerous or otherwise 

inadmissible individuals, etc.); and a cost benefit analysis that 

weighs effectiveness and security benefits of the program versus 

resource costs and probable implications or consequences of 

implementation (including economic, civil liberty and foreign policy). 

These policy decisions must be made before a selection of the options 

laid out in this study can be made, a final estimated cost reached, and 

execution and successful implementation of the program by all involved 

agencies take place.



It should be noted that the State Department has some additional 

options for implementation of a biometrics program that will be laid 

out in our own study. They do not necessarily generally conflict with 

the options set forth in the GAO report, though final estimated costs 

might differ.



[End of section]



Appendix VIII: Comments from the U.S. Department of Justice:



U.S. Department of Justice:



Washington, D.C. 20530:



OCT 3, 2002:



Ms. Nancy Kingsbury, Managing Director, Applied Research and Methods 

Issues U.S. General Accounting Office:



441 G Street, NW Washington, D.C. 20548:



Dear Ms. Kingsbury:



On August 30, 2002, the General Accounting Office (GAO) provided the 

Department of Justice (DOJ) copies of its draft report entitled 

TECHNOLOGY ASSESSMENT: Using Biometrics for Border Security. The DOJ 

is concerned that the report fails to adequately address some of the 

serious difficulties associated with such programs. The DOJ believes 

the report does not 1) properly consider an overall border security 

strategy; 2) adequately recognize the draft National Institute of 

Standards and Technology (NIST) certified standards recommendations for 

biometrics, tamper-resistant travel documents, or interoperability; or 

3) fully explore the advantages of some biometrics over others. In 

addition, the report contains a number of serious analytical weaknesses 

related to a misunderstanding of the false match rate metric and to 

performance data and levels. A proper understanding and use of 

biometrics is a critical component of increasing both security and 

efficiency at our border crossings. The GAO can play an important role 

in educating the government and the public as to the possibilities and 

limitations of such systems. We urge the GAO to reconsider major 

portions of this report, which currently rely on questionable 

information and interpretation, and to very carefully critique the 

report to ensure the accuracy of all the information presented.



An Overall Border Security Strategy.



Earlier this year the DOJ prepared an overall border security strategy 

to significantly improve border security and meet legislative intent 

and it has shared its strategy with the Office of Homeland Security, 

the Department of State and others. The U.S. Government is continuing 

to consider this strategy. The eventual direction selected will require 

a major investment in border systems and will need to be a foundation 

for future improvements. Since the existing border security processes, 

systems, and databases are fragmented and can be readily compromised, 

any substantial investment in the current state could be a throwaway 

and thus, it would not yield improvements commensurate with the huge 

investment required.



In addition, if the requirement to provide a biometric-based enrollment 

is limited to visa applicants (about 3 percent of the visits to the 

United States), the impact on preventing potential terrorists entry 

into the country would be marginalized. Unless enrolling visa 

applicants is just the first step in a larger process of using 

biometric-based enrollments, making a huge investment that improves 

borders security controls for only one of many border entry paths (e.g. 

visa holders, immigrants, Mexicans with border crossing cards; certain 

residents of visa-waiver countries; entrants through the Canadian 

border; U.S. citizens) should be challenged. Without strengthening the 

controls of the other paths, it would be easy for terrorists to enter 

via one of those paths.



NIST Standards.



The NIST study, required in the PATRIOT Act, is reaching its final 

recommendations based on empirical data and scientific methods. The 

Attorney General and the Secretary of State will rely on the NIST 

report with regard to the adoption of a technical standard for the 

design and development of a system to establish and verify unique 

identities. However, the GAO draft report appears to present 

information about biometrics inconsistent with the direction of NIST. 

The GAO team should examine the NIST direction to ensure that its 

report accurately reflects how various biometrics would fit in the 

overall context of the intended application. The intended application 

must: 1) employ a biometric that is able to establish and verify a 

unique identity in a hundreds of millions population, 2) be used to run 

a check against criminal records, and 3) operate with a very low risk 

of either false positive reads or the verification process being 

ineffective in different border, lighting, and weather conditions. To 

the extent possible, empirical evidence should support these 

requirements thereby mitigating the risk of making such a major 

investment only to discover that the biometric cannot meet the core 

requirements.



Advantages of Selected Biometrics:



in reviewing the use of biometrics, there are certain advantages to the 

use of fingerprints. Section 221 (b) of the Immigration and Nationality 

Act (INA) requires each alien applying for a visa to be registered by 

the Department of State unless waived at the discretion of the 

Secretary of State. Section 262 of the INA further clarifies this 

registration process to include the collection of fingerprints by 

stating that every alien in the United States not registered and 

fingerprinted under section 221 (b) who remains in the United States 30 

days or longer must apply for registration and be fingerprinted before 

the expiration of the 30 day period. We believe this constitutes a 

statutory mandate to register and collect fingerprints for all aliens 

applying for visas. When considering the variety of biometrics 

technologies amenable to support border control processes, the GAO 

should recognize this existing statutory requirement for the pre-

arrival collection of fingerprint biometrics for all aliens with visas 

seeking to travel to the United States.



Fingerprints also are the most effective biometric for computer 

identification on a large scale. In addition, unlike other biometric 

data, fingerprints are left at crime scenes. The ability to run latent 

(unidentified) fingerprints collected at the scene of criminal or 

terrorist incidents against the database of aliens present in the 

United States has immense law enforcement value. The National Security 

Entry-Exit Registration System (NSEERS) is already making use of this 

capacity in its fingerprintchecks at the border. Further, the report 

does not consider that the use of fingerprints would allow a search of 

the incoming visa applicants against the 43 million ten prints sets in 

Integrated Automated Fingerprint Identification System (IAFIS) to check 

for prior criminal history. Extrapolating data from the GAO study, 

approximately 900 persons with prior criminal activity would be 
screened 

out per year. While this number is statistically small compared to the 

total applicant pool, it is significant when one considers the type of 

person we are trying to prevent from entering the United States. In 
fact, 

the ability of fingerprints to provide quick, reliable matches at the 

border has been well demonstrated by the IDENT/IAFIS integration 

project. Running prints from aliens in secondary inspection and 

apprehended by the Border Patrol against this database led to 2,511 

arrests between January 1, 2002 and September 18, 2002. This project 

has been yielding approximately 70 hits per week.



Analytical Issues.



While the report provides an overview of biometric products that are 

typically used for data and facility access control for relatively 

small systems, it does not provide sufficient analysis of large systems 

such as those that will be required for effective Border Control. To 

prevent duplicate identification documents, the subjects enrolled in 

the Border Control system will have to be searched against each other. 

This capability will require that the system be of the same order of 

magnitude as that of IAFIS. It therefore follows that the biometrics 

used for such a system must have performance numbers that are of the 

same order of magnitude as IAFIS. We question the reported performance 

of the facial recognition based Mexican Federal Elections Institute 

system with respect to false alarms since all available biometric data 

points to the impossibility of conducting effective facial recognition 

on that scale. It is suspected that the system does not compare all new 

search facial records against the database of 60,000,0001t probably 

performs verification only and the database is likely to contain many 

duplicate records.



Although the report addresses performance issues, it fails to tie the 

performance requirements with realistic operational impact analysis due 

in large part to a misuse and misunderstanding the false match rate 

(FMR) metric. The FMRs and the False Non Matching Rates (FNMR) are 

dependent on the number of fingerprints captured, the type of image 

captured (rolled or plain), and the skill and experience of the 

individual capturing the fingerprint. To assess the impact of the FMR 

in a given operational setting it would be necessary to consider the 

size of each operational database and the workload. This relationship 

is not clearly explained in this report.



Failure to Properly Define and Use the FMR Metric. The reports entire 

analysis flows as if the size of the required biometric database is 

irrelevant. It is critical to differentiate between matching errors 

(FMR and FNMR) and decision errors (false accept rate and false reject 

rate.) The former are based on one-to-one comparisons and should be 

independent of the database size, while the later are based on 

transactions and depend on database size. The FMR is the probability of 

a false match when one search biometric is compared against one file 

biometric. It is a metric that is independent of the size of the 

database. It corresponds to the FNMR which is the probability of a non 

match when a search biometric is compared against its mated file 

biometric. An equal rate metric is a popular method for quickly 
comparing 

relative performance metrics for different biometric systems. However, 
it 

only makes sense if the two metrics are applied consistently. The 
improper 

use of the FMR is in part due to the lack of consistent standards 
within 

the industry in reporting their performance levels.



The FMR usually must be resolved by manual means. This has a serious 

impact on the operational staffing and facility requirements. To assess 

the impact of the FMR in a given operational setting it is necessary to 

consider the size of the operational database and the workload. The 

total number of false matches during operations that must be resolved 

by the operators during a typical day will be the FMR multiplied by the 

size of the file database multiplied by the daily workload. This 

relationship is not clearly explained in the report, worse the FMR is 

sometimes cited as a system metric that does not rationalize the number 

by the database size. The significance of this error is exponential.



Systems Performance Data and Levels. The report also provides incorrect 

performance data for the IAFIS and provides no performance data for 

other large biometric systems. These two errors lead the report to 

incorrect conclusions with respect to the viability of various 

biometric devices. IAFIS was tested rigorously during development and 

acceptance testing. The system also has been periodically retested to 

ensure that the performance levels are maintained and improved. In 

addition, daily statistics are collected for the FMR and failure to 

enroll rate. The LUIS performance levels indicate that only a multiple 

finger based system is capable of supporting the Border Control 

identification (enrollment) functionality.



The report does not address the fact that some biometrics are by nature 

multiple biometric. Most subjects have ten fingers, two eyes, and two 

hands. Nor does the report address the variations in fingerprint 

technology and the impact of these technologies on system performance. 

The more data that is captured the better the potential for achieving 

high performance levels. For IAFIS the primary biometric is ten rolled 

fingers. In effect it is like fusing ten different biometrics. This is 

what allows IAFIS to achieve its outstanding performance levels. Tests 

are currently being conducted to determine the impact on IAFIS of using 

flat fingerprint data and possibly fewer fingers. All of these factors 

will play an important role in the design of the Border Control 

systems.



There also is the issue of the amount of data that is captured. 

Performance levels can be improved by the simple process of storing and 

matching against multiple file data. That is, instead of keeping one 

facial image or one set of fingerprints on file, keep multiple copies 

on file. By having more copies on file the FNMR can be increased with 

corresponding trade-offs on the FMR. This clearly has implications:



on the size of the central matching system. Further, to evaluate the 

efficacy of the biometrics it is recommended that target performance 

levels be specified for all of pertinent metrics for identification and 

verification. In all likelihood different combinations of biometrics 

will be used for the two functions. Establishing goals also will 

provide the necessary signals for industry to improve their products or 

to make more effective use of the biometric information that may be 

available.



In conclusion, we note that the GAO draft report infers that any move 

toward biometrics be made slowly and cautiously. While we agree that it 

is important to proceed judiciously, we must also instill the sense of 

congressional urgency, both implied and expressed, in the PATRIOT Act 

and the Enhanced Border Security Act. The DOJ believes that the current 

border security processes are not effectively preventing terrorists and 

other criminals from entry into the United States. Most of the 

processes and systems were designed for a different set of problems 

decades ago. Adopting biometrics-based unique identification method is 

a key element in changing what exists today to meet these new 

challenges to our border security.



As noted by the above comments, we believe that the report falls short 

by not adequately addressing these significant issues. The DOJ urges 

you to consider its concerns in preparing the final GAO report on this 

important subject. If you have any questions regarding the Departments 

comments, you may contact Vickie L. Sloan, Director, Audit Liaison 

Office, on (202) 514-0469.



Sincerely,



Robert F. Diegelman

Acting Assistant Attorney General for Administration



Signed by Robert F. Diegelman



[End of section]



Appendix IX: GAO Contacts and Acknowledgments:



GAO Contacts:



Nancy R. Kingsbury (202) 512-2700; kingsburyn@gao.gov.

Naba Barkakati (202) 512-4499; barkakatin@gao.gov.



Acknowledgments:



Additional staff who made major contributions to this report were 

Venkareddy Chennareddy, Barbara Hills, Ashfaq Huda, Richard Hung, 

Elizabeth Johnston, John C. Martin, Eric Ow, Madhav Panwar, Penny 

Pickett, Tracy Pierson, David Plocher, and Karen Richey.



We gratefully acknowledge the time and assistance of the following 

people who reviewed a draft of this report: Dennis Carlton, 

International Biometric Group; Paul Collier, The Biometric Foundation; 

Larry Hornak, West Virginia University; Anil Jain, Michigan State 

University; Rick Lazarick, Transportation Security Administration; 

Peter Neumann, SRI International; Lee Tien, Electronic Frontier 

Foundation; Jim Wayman, San Jose State University; Charles Wilson, 

National Institute of Standards and Technology; and John Woodward, RAND 

Corporation.



We also appreciate the contributions provided by the following 

organizations during our meetings on biometrics and border security: 

Airports Council International; American Civil Liberties Union; 

American Immigration Lawyers Association; Biometric Technology Inc.; 

Border Trade Alliance; Cameron County Bridge Systems; Cogent Systems 

Inc.; Electronic Data Systems Corp.; Electronic Privacy Information 

Center; EyeTicket Corp.; Graphco Technologies Inc.; Identix Inc.; 

International Biometric Industry Association; International 

Organization of Masters, Mates, and Pilots; Iridian Technologies Inc.; 

Mitretek Systems Inc.; National Council La Raza; Recognition Systems 

Inc.; Sagem Morpho Inc.; and Viisage Technology Inc.



[End of section]



Bibliography:



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BioAPI Consortium. BioAPI Specification Version 1.1, March 16, 2001. 

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Blackburn, Duane M., Mike Bone, and P. Jonathon Phillips. Facial 

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U.S. General Accounting Office. Electronic Benefits Transfer: Use of 

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FOOTNOTES



[1] The term biometrics is commonly used to mean biometric technologies 

and the characteristics themselves.



[2] See 8 U.S.C. 1365a and 1722. These laws requirements reflect 

provisions of the Illegal Immigration Reform and Immigrant 

Responsibility Act of 1996 (Public Law No. 104-208, div. C, 110, Sept. 

30, 1996), the INS Data Management Improvement Act of 2000 (Public Law 

No. 106-215, June 15, 2000), the Uniting and Strengthening America by 

Providing Appropriate Tools Required to Intercept and Obstruct 

Terrorism Act of 2001 (USA PATRIOT Act) (Public Law No. 107-56, 403(c) 

and 414, Oct. 26, 2001), and the Enhanced Border Security and Visa 

Entry Reform Act of 2002 (Public Law No. 107-173, May 14, 2002). 



[3] The information in Chimera is to be accessible to federal law 

enforcement and intelligence officers who, under federal regulation, 

are responsible for investigating or identifying aliens (Enhanced 

Border Security and Visa Entry Reform Act, 202(a)(5) (8 U.S.C. 

1722)), to federal law enforcement officials to identify and detain 

individuals who pose a threat to national security (USA PATRIOT Act, 

414(b) (8 U.S.C. 1365a note)), and, at the discretion of the attorney 

general, to federal, state, and local law enforcement officials for law 

enforcement purposes (INS Data Management Improvement Act, 2 (8 U.S.C. 

1365a(f)(2)), amending the Illegal Immigration Reform and Immigrant 

Responsibility Act of 1996, 110).



[4] Enhanced Border Security and Visa Entry Reform Act, 201(c)(3) and 

201(c)(4) (8 U.S.C. 1356a note). The USA PATRIOT Act 403(a) 

(amending 8 U.S.C. 1105) has virtually identical requirements with 

regard to the State Departments receiving National Crime Information 

Center data.



[5] USA PATRIOT Act, 403(c)(2) and 403(c)(4).



[6] Enhanced Border Security and Visa Entry Reform Act, 203 (8 U.S.C. 

1723).



[7] The Immigration and Nationality Act of 1952, as amended (8 U.S.C. 

1101 et seq.), and titles 8 and 22 of the Code of Federal Regulations 

are the primary sources of U.S. immigration law.



[8] According to the Department of Justice, passports are not required 

of U.S. citizens returning from any point within the Western Hemisphere 

except Cuba.



[9] The visa waiver program permits nationals from designated countries 

to apply for admission to the United States for 90 days or less as 

nonimmigrant visitors for business or pleasure without first obtaining 

a U.S. nonimmigrant visa. The following countries participate: Andorra, 

Australia, Austria, Belgium, Brunei, Denmark, Finland, France, Germany, 

Iceland, Ireland, Italy, Japan, Liechtenstein, Luxembourg, Monaco, the 

Netherlands, New Zealand, Norway, Portugal, San Marino, Singapore, 

Slovenia, Spain, Sweden, Switzerland, the United Kingdom, and Uruguay 

(8 U.S.C. 1187, 8 C.F.R. 217.2).



[10] Passports may be denied for reasons set forth in 22 C.F.R. 51.70.



[11] The process for issuing immigrant visas, although similar to that 

for nonimmigrant visas, includes other procedures and checks such as 

the submission of an immigration petition to INS. About 628,000 

immigrant visas are issued each year.



[12] The State Department is adding 8 million criminal history alien 

records from the Federal Bureau of Investigation (FBI). These records 

include foreign-born individuals and individuals with unknown place of 

birth.



[13] Visas may be denied for reasons listed in the Immigration and 

Nationality Act, 212 (8 U.S.C. 1182).



[14] In January 2003, INS plans to publish regulations in response to 

the Enhanced Border Security and Visa Entry Reform Act to mandate 

electronic manifest transmission from carriers at air and sea ports of 

entry for all arriving and departing passengers.



[15] A Mexican border crossing card permits the holder to enter for 

business or pleasure and stay in the United States for 72 hours or 

less, going no farther than 25 miles from the border.



[16] P. Jonathon Phillips and others, An Introduction to Evaluating 

Biometric Systems, IEEE Computer 33:2 (2000): 56-63.



[17] FRVT 2000 was sponsored by the DOD Counterdrug Technology 

Development Program Office, Defense Advanced Research Projects Agency, 

and National Institute of Justice. The test goals were to know the 

strengths and weaknesses of each individual system, understand the 

current state of the art for facial recognition, and educate the 

community and general public on how to present and analyze results. 



[18] NPL in the United Kingdom is analogous to the National Institute 

of Standards and Technology (NIST) in the United States.



[19] Lisa Thalheim, Jan Krissler, and Peter-Michael Ziegler, Body 

Check: Biometric Access Protection Devices and Their Programs Put to 

the Test, trans. Robert Smith, ct Magazine 11 (2002): 114.



[20] JPEG members are experts nominated by national standards bodies 

and major companies to produce standards for continuous tone image 

coding. Joint refers to the groups status as a committee working on 

standards for both the International Organization for Standardization 

and International Telecommunication Union-Telecommunication. 



[21] According to a February 2001 study conducted for the FBI, WSQ and 

JPEG 2000 formats are similar enough that questions may emerge about 

migration of the FBI standard to the JPEG 2000 standard. Such questions 

would include weighing some advantages against other disadvantages of 

changing an accepted standard that is already widely used.



[22] From 1997 to 2001, INCITS operated under the name Accredited 

Standards Committee NCITS, National Committee for Information 

Technology Standards. From 1961 to 1996, NCITS operated under the name 

Accredited Standards Committee X3, Information Technology.



[23] Government information security reform provisions of the FY 2001 

Defense Authorization Act--for example, 44 U.S.C. 3534(a); Clinger-

Cohen Act of 1996--for example, 40 U.S.C. 11313(6); Paperwork 

Reduction Act of 1995--for example, 44 U.S.C. 3506(g); and Computer 

Security Act of 1987--for example, 40 U.S.C. 11332.



[24] U.S. General Accounting Office, Information Security: 

Opportunities for Improved OMB Oversight of Agency Practices, GAO/

AIMD-96-110 (Washington, D.C.: September 24, 1996).



[25] U.S. General Accounting Office, Computer Security: Improvements 

Needed to Reduce Risk to Critical Federal Operations and Assets, 

GAO-02-231T (Washington, D.C.: November 9, 2001).



[26] U.S. Bureau of the Census, Evaluating Components of International 

Migration: Estimates of the Foreign-Born Population by Migrant Status 

in 2000, Population Division Working Paper 58 (Washington, D.C.: 

December 2001).



[27] U.S. Immigration and Naturalization Service, Statistical Yearbook 

of the Immigration and Naturalization Service, 2000 (Washington D.C.: 

U.S. Government Printing Office, September 2002), 271-74.



[28] The Privacy Act of 1974 (5 U.S.C. 552a) and the Computer Security 

Act of 1987, Public Law 100-235 (15 U.S.C. 278g-3 and 4, 40 U.S.C. 

11331, and 40 U.S.C. 11332).



[29] 5 U.S.C. 552a(a)(4).



[30] 5 U.S.C. 552a(a)(2).



[31] 5 U.S.C. 552a(b), (j), (k).



[32] U.S. General Accounting Office, Social Security Numbers: 

Government Benefits from SSN Use but Could Provide Better Safeguards, 

GAO-02-352 (Washington, D.C.: May 31, 2002). 



[33] U.S. General Accounting Office, Identity Fraud: Prevalence and 

Links to Alien Illegal Activities, GAO-02-830T (Washington, D.C.: June 

25, 2002).



[34] Maquiladora refers to a Mexican company that imports, on a duty-

free basis, machinery, equipment, and materials for the manufacture of 

finished goods for subsequent export.



[35] For more information on cost-benefit analysis, see Office of 

Management and Budget, Guidelines and Discount Rates for Benefit-Cost 

Analysis of Federal Programs, Circular A-94 (Washington, D.C.: October 

29, 1992; rev. January 22, 2002).



[36] U.S. General Accounting Office, National Preparedness: 

Technologies to Secure Federal Buildings, GAO-02-687T (Washington, 

D.C.: April 25, 2002).



[37] U.S. General Accounting Office, Homeland Security: Challenges and 

Strategies in Addressing Short-and Long-Term National Needs, GAO-02-

160T (Washington, D.C.: November 7, 2001).



[38] We have a standing contract with NAS under which NAS provides 

assistance in convening groups of experts to provide information and 

expertise to our engagements. NAS uses its scientific networks to 

identify participants and uses its facilities and processes to arrange 

the meetings. Recording and using the information in a report is our 

responsibility.



[39] U.S. v. Byron C. Mitchell (Criminal Action No. 96-407-1, U.S. 

District Court for the Eastern District of Pennsylvania 1999).



[40] U.S. v. Carlos Ivan Llera Plaza, Wilfredo Martinez Acosta, and 

Victor Rodriguez (Criminal Action No. 98-362-10, 11,12, U.S. District 

Court for the Eastern District of Pennsylvania 2002).



[41] Ridges are the upper skin layer segments of the finger; valleys 

are the lower segments. 



[42] To allow for scarred or injured fingers, drivers typically 

enrolled two digits.



[43] FNMR analysis from system performance testing by Jim L. Wayman, 

U.S. National Biometric Test Center, College of Engineering, San Jose 

State University, San Jose, California.



[44] Other facial recognition technologies based on thermal patterns 

below the skin are not yet commercially viable.



[45] Fifteen different agencies are sponsors of FRVT 2002, including 

the Defense Advanced Research Projects Agency, the National Institute 

of Justice, and the Transportation Security Administration. NIST is 

selecting images and computing test scores.



[46] The annual Diversity Visa Lottery Program makes 50,000 immigrant 

visas available through a lottery to people who wish to come to the 

United States from countries with low immigration rates. Winners are 

chosen randomly from all qualified entries by the State Departments 

National Visa Center.



[47] The Schengen agreement, begun in 1985, is designed to facilitate 

travel within the European Union. Passengers flying between member 

countries now leave from domestic rather than international airport 

terminals, eliminating the need to present travel documents when 

entering and exiting. The Schengen agreement went into effect in 

Iceland on March 25, 2001.



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