NISTIR 7377

Summary of NIST Latent Fingerprint 

Testing Workshop

V. N. Dvornychenko

Michael D. Garris

 
NISTIR 7377

Summary of NIST Latent Fingerprint 

Testing Workshop

V. N. Dvornychenko

Michael D. Garris

Information Technology Laboratory

Information Access Division

November 2006

U.S. DEPARTMENT OF COMMERCE

Carlos M. Gutierrez, Secretary

TECHNOLOGY ADMINISTRATION

Michelle O’Neill, Acting Under Secretary of Commerce for Technology

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

William A. Jeffrey, Director

 
Summary of NIST Latent Fingerprint Testing Workshop 

V. N. Dvornychenko (vdvorny@nist.gov) and 

Michael D. Garris (mgarris@nist.gov) 

Abstract 

The central focus of this paper is on the Latent Testing Workshop, held on the National Institute 

of Standards and Technology (NIST) Campus April 5 to 6, 2006 and the lessons that were 

learned from it. The primary goal of the workshop was to gather information for the creation of a 

“Latent Challenge,” whose purpose is to stimulate Latent Automated Fingerprint Identification 

System (AFIS) vendors to submit their AFIS prototype systems for evaluation. To lay the 

foundations for such testing it is necessary that: 1) suitable test sets be identified and prepared; 2) 

the Application Programming Interface (API) is defined; and 3) the effective methods of 

performance scoring be defined. To provide background and context, past and present latent 

fingerprint activity at NIST is discussed. While the primary focus is on latent fingerprints, the 

paper also surveys relevant general biometrics activity. 

Keywords: biometrics; Electronic Fingerprint; Extended Fingerprint Feature Set; EFFS; 

fingerprint image quality; latent fingerprints; Latent Workshop; level-3 features; lights-out 

systems; matcher architecture; performance testing 

1. Introduction 

A Latent Testing Workshop was hosted at the National Institute of Standards and Technology 

(NIST) on April 5 to 6, 2006. In all, 61 participants representing U.S. and international 

commercial, government, and academic interests came together to discuss the needs of the latent 

fingerprint community; assess the state of the art in latent matchers; and to obtain the views of 

latent matcher vendors on their ability and willingness to field “semi-lights-out” latent search 

systems for testing purposes. (The concept of “semi lights out matchers” is introduced in Section 

2.6.) These topics were successfully explored, and in large measure resolved, in the workshop. 

The purpose of this paper is to present the findings of the Latent Testing Workshop and to show 

how these findings relate to past and future fingerprint activity at NIST. Section 2 provides 

background to NIST’s role in evaluating fingerprint biometrics; the concepts of “lights-out” and 

“semi-lights-out” latent processing are described; this material provides context as to why a 

latent workshop was necessary. Section 3 presents an overview of the Latent Testing Workshop 

including who presented on what topics, what lessons were learned, what are the perceived needs 

of the latent community, as well as additional topics. Section 4 concludes with directions for 

future work at NIST in response to the workshop. 

2. Background 

2.1 The Role of NIST 

The traditional role of NIST has been the assessment of technology, the establishment of metrics, 

and the setting of standards [1][2]. In this connection NIST has a long-standing interest in 

1
biometrics. This interest has primarily focused on fingerprints, but selective work has been done 

on iris prints, facial recognition and Deoxyribonucleic acid (DNA) [3][4]. 

Recently (2006), the USA Patriot Act specifically mandated that NIST develop and certify a 

standard for verifying the identity of persons applying for a visa or seeking to enter the United 

States, and that NIST provide technical support to the Attorney General and Secretary of State in 

evaluation of biometric identification systems for Entry and Exit Data System for U.S. borders 

[5][6]. 

Since that time, other agencies, including the U. S. Department of Homeland Security (DHS), the 

Federal Bureau of Investigation (FBI), and the U. S. Dept. of Justice (DOJ), have sought 

technical guidance from NIST in several evaluations of biometric systems which may be 

considered for homeland security [7][8]. 

2.2 Fingerprints as a Biometric 

Biometrics are measurements that assist in uniquely identifying (“individualizing”) a person 

[9][10][11]. Fingerprints are currently the biometric of choice – and are likely to remain so for 

the foreseeable future. There are several reasons for this. Fingerprints are less intrusive than 

certain other biometrics, for example iris and DNA, though more intrusive than, say, facial 

photos or voice prints. They also have very good discrimination/identification power. Perhaps 

what is most important, they have a long and favorable track record with police departments and 

the courts. As a result, a tremendous amount of money and time has been invested into the 

fingerprint infrastructure. 

Fingerprints may be roughly divided into three categories: 1) Rolled impressions, 2) Plain 

impressions or “flats,” and 3) Latent fingerprints. “Palmprints” form a separate but closely 

related category. Partly because of their large size, lack of standard databases, and their 

complexity, they have been slower to develop than other types, especially regarding automation. 

Recently, however there has been an effort to bring palmprints to a level of development 

approximating the other types. Accordingly, the FBI’s Criminal Justice Information Services 

(CJIS) Division is in the process of creating a repository of palmprints [12]. Commercial 

automated palmprint systems are becoming available. Examples of three types of fingerprints are 

provided in Figure 1. The soles of the feet also have friction ridge patterns similar to palms, but 

these are seldom encountered except for infant identification. 

In a rolled impression the finger is carefully rolled (or rocked) from one side to the other so as to 

obtain the impression of a greater area. This has advantages and disadvantages. While a greater 

coverage results, the impression is slightly distorted by the rolling process. In a plain impression 

(or “flat”) the finger is pressed down with a moderate pressure but not rolled. While the result 

covers a smaller area, there is less distortion. Rolled and plain impressions are typically taken 

from a cooperative subject, and are subject to a retake if lacking in quality (incomplete, smudged 

or otherwise unclear). As a result, they are typically of high quality and rich in information 

content. 

Latent fingerprints are generally inadvertently left at a scene, are not subject to retake, and tend 

to be of considerably lower quality and information content. A second difference is the method 

of capture. Rolled and flats are captured electronically, or else as an inked impression (which 

may be subsequently scanned to create an electronic version). As the name suggests, latents are 

often “hidden” or at least not readily apparent. Consequently, they must be developed to render 

2
them suitable for searches and identification. “Development” may be as simple as photographing 

the print, but usually involves some type of processing, such as dusting with a powder, or 

chemical treatment. 

Rolled and plain impressions may be used in one-to-many identifications, or in one-to-one 

verifications. In a fingerprint identification, or search, the given fingerprint is compared against 

many candidates comprising a gallery (also known as a repository or background), and zero, 

one, or more potential matches are reported back. For rolled or plain impressions, most 

identification systems will only report high-probability matches; for latents, many systems report 

back a fixed list of candidate matches regardless of similarity. 

In verification the given fingerprint is compared to a single candidate and a match/no-match 

decision is made in “real time,” that is, in seconds. Fingerprint verification systems are used in 

physical access control and in logical access (e.g., banking). They employ “instant” livescan 

devices to capture a subject’s fingerprint, and compare this to a stored exemplar. Since the 

subject requiring verification provides his/her purported identity, the captured fingerprint need 

only be compared with a single stored exemplar. The process of entering the stored exemplar in 

the system’s database is referred to as enrollment. 

Considerable amount of information on biometrics is now available online, for example refer to 

References [9][10][13][14]. 

Rolled Impression Plain Impression Typical Latent

Figure 1. Example of Rolled Impression, Plain Impression, and Latent 

2.3 Fingerprint Performance Testing 

In line with its historical mission, NIST has performed a number of fingerprint tests whose 

purpose was to: 

1. Verify and refine performance measurements of large operational fingerprint matchers, 

for example FBI/AFIS and US-VISIT IDENT [7][8]; 

2. Assess the state of the art (e.g., matching accuracy) of current vendor matching systems 

[15]; 

3
3. Determine the degree of interoperability between vendor systems when using common 

minutiae sets [16][17][18]; 

4. Assess the relative value of rolled vs. flat fingerprints in identification, and when 

matching against latent fingerprints [19]; and 

5. Discover and consolidate “multiple enrollments” (the same subject appearing more than 

once in a database); 

Much of this work is of considerable sophistication, and it is not possible to do it justice in a 

short summary. A great deal of information is found on the NIST website under 

Î http://fingerprint.nist.gov/

2.4 Need to Expand Scope of Latent Work 

As mentioned, only a relatively small part of NIST testing has involved latent fingerprints. 

Recently there has been a large increase in interest in latent systems, driven by a need to: 1) 

perform real-time screening against latent databases at ports of entry; 2) improving 

interoperability between the various latent search systems; 3) improving response time for latents 

acquired at the crime scene by Crime Scene Investigators (CSI); 4) reducing human involvement 

in processing unsolved latent files and “dead files.” 

From NIST’s standpoint these requirements lead to: 

3⁄4 A need to stimulate industry to produce latent search systems which are faster, more 

accurate, yet less labor intensive. 

3⁄4 NIST is interested in testing vendor systems to benchmark current performance levels 

and subsequent improvements. 

3⁄4 NIST would like these tests to be as automated as possible for the following reasons: a) 

simplify testing logistics; b) protect vendor proprietary data; and c) decouple the 

performance of the automated system from the performance of latent examiners. 

3⁄4 As will be shown, these requirements are best met by a “semi-lights-out” system. 

Before introducing this concept we must first discuss what is meant by a “lights-out system.” 

2.5 The Concept of a “Lights-Out System” 

A “lights-out” system is a computer system that normally requires no human intervention. In the 

present context, the system automatically performs all of the following: 

3⁄4 Accepts a set of fingerprints to be searched. Fingerprints may be rolled or plain, and may 

consist of several fingers to be searched jointly. 

3⁄4 Automatically extracts all the features required for searching 

3⁄4 Performs the search of the database (gallery) 

3⁄4 Determines if possible hits have sufficiently high scores so that no human verification is 

required (because of extremely high confidence in the result) 

3⁄4 Only these high-score/high-confidence candidates are output as “hits” 

Of course even in a lights-out system there will be occasional “problem cases” requiring human 

review. Only recently has technology reached a level making semi-lights-out systems possible, 

4
but most ten-print matchers currently in use are not yet fully lights-out matchers. However, it is 

fully expected that successive generations will increasingly move in this direction.

2.6 The Concept of a “Semi-Lights-Out System” 

Latent fingerprints contain considerably less information than other types (rolled or plain). 

Whereas a typical latent fingerprint might have fifteen usable minutiae, a typical rolled 

fingerprint will have sixty or more. In addition, rolled and plain fingerprints are generally 

searched as multi-finger searches of at least two fingers. Most latent searches consist of a single 

fingerprint – or more accurately, a single partial fingerprint. The reliability of a latent “hit” is 

therefore considerably less (on the average) than for ten-prints. Generally it is not possible to 

process them in a fully “lights-out” manner. 

Current processing for latents is characteristically very human-intensive. The following steps 

must be performed by human experts: 

1) Feature extraction is done almost entirely by human experts, with limited assistance from 

the machine 

2) The human expert examines an extensive candidate list to see if it contains possible hits 

3) The expert examines each possible hit to verify or reject it. (Of course by far the majority 

of examinations result in rejections.) 

In a “semi-lights-out” system steps (1) and (2) are automated, and humans are only required for 

final verification. Limited human assistance may, however, be accepted in step (1). This can take 

the form of: 

3⁄4 Probable finger orientation 

3⁄4 Probable finger number 

3⁄4 Identifying (“lassoing”) an area of interest (for example, to separate it from irrelevant 

background clutter) 

2.7 Why are “Semi-Lights-Out Systems” of Special Interest? 

There are time-sensitive scenarios in which the automation of feature extraction and screening of 

the output candidate list would be very useful. Examples include the following: 

3⁄4 In some applications there is a need to check an incoming fingerprint against an unsolved 

latent file, with a required turnaround time of seconds. In this particular case the feature 

extraction would probably be performed by humans at the time the latent is enrolled. 

However, “back end” processing would be required to ensure a candidate list of a length 

that can be processed within the time constraint. A human expert would of course make 

the final identification. 

3⁄4 Law enforcement agencies invariably have a large number of unprocessed latent images 

on file. They are unable to process these because they cannot spare examiners to mark up, 

search, and examine the returned candidates. Automated latent image searches and 

preliminary screening of candidates could identify high-probability hits with relatively 

little human effort. 

These are just two examples in which semi-light-out latent searching has the potential to make 

hits that otherwise would not be made. Again, we emphasize that in all cases the final 

5
identification decision is made by a human latent examiner — no one is proposing automating 

the final decision. 

6
2.8 Proposed Format of Latent Testing 

NIST would like to be able to assess the performance of latent search systems developed by 

vendors. Prior to the workshop NIST had concluded that testing would be optimized if conducted 

under a “semi-lights-out” environment. The reasons are: 

3⁄4 Simplifies testing logistics; 

3⁄4 Protects vendor proprietary data; and 

3⁄4 Decouples the performance of the automated system from the performance of latent 

examiners to the greatest degree possible. 

However, NIST was unsure if vendors could provide “semi-lights-out” systems for testing. 

2.9 Unresolved Questions Leading to the Need for a Workshop 

Since we are proposing that a “semi-lights-out” latent search system is the best environment for 

testing, it is important to determine if the latent fingerprint community and vendors are prepared 

to field such systems – if only for testing purposes. To answer this and similar questions, NIST 

decided to hold a Latent Testing Workshop. Specific topics to be answered during the workshop 

were: 

3⁄4 How are latent fingerprints being currently used by agencies? 

3⁄4 What are the specific needs of agencies using latent fingerprints? 

3⁄4 What improvements/innovations they would like to see? 

3⁄4 Do vendors believe a semi-lights-out system is within reach with present technology? 

3⁄4 If so, what might be the approximate performance level of such a system? 

3⁄4 Have there been large performance gains in the last couple years? 

3⁄4 Are vendors interested in providing a semi-lights-out latent system(s) for testing? 

3⁄4 Is NIST’s proposed testing concept workable and fair to the vendors? 

3⁄4 What is a reasonable time frame for fielding a test? 

3⁄4 What changes in proposed NIST testing methodology does the latent community 

suggest? 

3⁄4 How difficult is it to acquire additional latent exemplars for testing purposes? 

3⁄4 What are promising sources of latent test data? 

3⁄4 What are promising new features (“Extended Fingerprint Feature Set (EFFS)”) for latent 

matching? 

3⁄4 Are “special purpose” testing sets desirable? 

7
3. The NIST Latent Testing Workshop 

The two-day Latent Testing Workshop was held at NIST, Gaithersburg, Maryland on April 5 to 

6, 2006. There were 61 participants in the workshop. A breakdown of participants by 

professional affiliation is given below: 

Affiliation of Workshop Participants 

Organization Number of Participants 

United States 49

Government 20 

State/Local Law Enforce. 8 

Commercial 20 

Academic 1 

Foreign 12

Government 6 

Commercial 5 

Academic 1 

Total 61

Table 1. Breakdown of participants by professional affiliation 

Twenty-seven presentations were given, followed by a wrap-up discussion. The two sections 

following provide a summary of what was learned. Additional information may be obtained from 

the NIST website Î http://www.itl.nist.gov/iad/894.03/latent/ and from Î fingerprint.nist.gov 

The first website contains almost all presentations given, but in some cases in slightly redacted 

form. In one instance (presentation number 2) it was not possible to post the presentation at all. 

8
3.1 Categorization of Workshop Presentations by Topics 

This section categorizes each presentation in terms of a main topic, and one or more secondary 

topics. On the NIST website the presentations are in alphabetic order by presenter name. Table 2

is provided to facilitate the transition between the two (alphabetic order and presentation 

number). In its electronic form, this table contains “hot links” which can take one directly to the 

website presentation. 

Cross Reference for Presentations 

Presentation 

Sequence 

No. 

Alphabetic 

No. 

(website) 

Speaker’s Name Title of Presentation 

0 12 Marty Herman and 

Vladimir Dvornychenko

Opening Remarks 

1 21 Stephen Meagher Latent Processing at the FBI/LPU 

2 28 Kasey Wertheim Military Latent Needs 

3 9 Danny Greathouse Need for Rapid Turnaround at US-VISIT 

4 23 Thomas Smith Need for AFIS Vendors to Improve Search Capability

5 22 Francis P. Senese Problems Experienced with Livescans 

6 25 B. Scott Swann Needs and Applications of Latents at FBI/CJIS 

7 19 Deborah Leben Applications ULW at DHS 

8 20 Gordon Low Needs and Applications of Latents at CA Dep't of Justice 

9 6 Jeri Eaton and 

Wade Petroka

Needs and Applications of Latents at King County, WA, Sheriff 

Office

10 24 Ambika Suman Evaluating Automated Finger and Palm Mark (latent) Searching 

11 3 Mark Branchflower Implementation of Remote Latent Search Capability at Interpol

12 7 Jean-Christophe Fondeur Experience in Lights-out Processing 

13 4 Wally Briefs Maximizing Latent Identification Performance 

14 2 Behnam Bavarian Thresholds and Parameters for Automatic Decisions

15 11 Masanori Hara Thoughts on Automatic Latent Processing and Matching 

Algorithms 

16 16 Tom Hopper ULW Approach for Sharing Latent Identification Services 

17 11 Nigel Allinson and 

Ian Gledhill

Wireless Transmission of Fingerprints 

18 13 Austin Hicklin CDEFFS -- Extended Feature Sets

19 17 Anil Jain and Yi Chen High Resolution Matching Using Level 3 Features 

20 27 Phillip Wasserman Level 3 Feature Detection Using Support Vector Machines

21 26 Elham Tabassi Quality Measure Workshop Lessons Learned 

22 28 Kasey Wertheim Latent Quality Measures 

23 14 Austin Hicklin Quantifying Latent Quality

24 18 George Kiebuzinski Lessons Learned during IAFIS Source Selection 

25 10 Patrick Grother Offline Biometric Testing at NIST

26a 29 Stephen Wood Test Sets at NIST

26b 5 Vladimir Dvornychenko Latent Test Sets "Wish List" 

27 8 Michael Garris Proposed Latent Testing Methodology

28 15 Austin Hicklin CDEFFS Committee Meeting

Table 2. Workshop speakers and presentations 

9
3.2 Lessons Learned from Workshop 

We believe the workshop was very successful and answered most of the questions we hoped to 

resolve. These included: 

1. Is the community, particularly the vendors, favorable toward automated testing of latent 

search systems? 

We found the responses to be generally favorable. Several vendors indicated that they had 

made significant progress in automated feature extraction, and the prioritization of 

candidates. This suggests that an automated search system could be ready for testing in the 

near future. 

2. Are sufficient test data available? 

Prior to the workshop there was concern at NIST, as well as among other participants, that it 

might prove difficult to acquire a sufficient number of latent mated pairs for a really high 

quality test. By “mated pairs” is meant 1) a latent print along with 2) its mated rolled or 

plain-impression “exemplar.” (Of course both a rolled and a plain impression would be ideal. 

Normally the latent is used as the “probe” or “search” and the rolled (or plain-impression) is 

seeded into the “gallery.” However, sometimes a “reverse search” is conducted in which a 

rolled impression is searches against a gallery of latents.) 

Even assuming a statistically sufficient number of pairs are identified, it is highly desirable 

that these come from as diverse sources as possible so as to represent a good cross-section of 

the latent user community. Of particular concern is that the latents should not be largely 

selected from the population of AFIS hits. Such a selection would bias the results toward 

latents which exhibit characteristics “preferred by current-generation AFIS systems.” 

In the course of the workshop a number of potential sources for latent test subjects were 

identified. Most of the sources were participants in the workshop, or else were identified by 

participants. The identified sources also fulfill the “diversity” requirements. It therefore 

appears that a fairly large quantity of latents, of diverse origin, may become soon available. 

3. Do vendors think that such testing can be fairly conducted? 

On this issue there was some concern expressed. Some vendors raised the point that the 

series of one-to-one matches proposed by NIST, and based on the System Development Kit 

(SDK) model, would allow little or no data sharing between searches and that this would 

result in a suboptimal candidate list. Other vendors expressed the opinion that NIST was 

overly focused on the “ROC”1

 approach as a performance measure, and that for latents there 

existed better measures of performance, such as rank statistics. 

4. The workshop also engendered a great deal of interest in image quality. It quickly became 

clear that two different types of image quality measures will be needed. The first is geared 

toward automated systems (computer oriented). The second is geared toward 

judiciary/evidentiary considerations (human oriented). 

 

1

 “ROC” is an older term which stood for “Receiver Operating Characteristics.” The more modern term is DET for 

“Detection Error Tradeoff” 

10
Below we provide additional details. The topics generally follow the workshop program. 

3.3 Needs of the Latent Community 

3.3.1 Rapid Turnaround 

A number of presentations stressed the need for rapid turnaround in processing latents. The 

typical scenario proposed was that of a latent print lifted at a crime scene by CSI, with a need to 

have this latent “screened” as rapidly as possible. The presentations stressed that the current 

typical turnaround time of two or three days is unacceptable. 

What is patently needed is a method of rapid screening with a turnaround time of a few hours at 

most. This would give the criminal(s) less time to make good their escape. This initial screening 

may be at a reduced accuracy level if required to meet the time constraint. If the situation 

warrants it, this initial screening may be followed up with a more labor-intensive, but more 

accurate, search. 

To provide this rapid response capability, three things are needed: 1) a good automated method 

of latent fingerprint image quality assessment for determining which latents are “more 

matchable,” 2) a fast method of transmitting these latent to a central processing site, and 3) a 

semi-lights-out method of processing the image. 

Semi-lights-out systems are not currently operational. However, several presentations (e.g., 

Allinson, P-17) presented information on rapid latent fingerprint transmissions. The presentation 

by FBI/CJIS (Swann, P-6) also stressed the shortage of latent experts, and the need for assistance 

via automation. 

3.3.2 Workstations 

Currently in the US there are several types of latent workstations in use. The three most common 

are: 1) Universal Latent Workstation (ULW) (developed by FBI/CJIS), 2) Remote Fingerprint 

Editing Software (RFES) (developed by Lockheed Martin2

 for the FBI) and 3) workstations 

developed by major AFIS vendors. Several presentations (#6, #7, #14) stressed the need to bring 

out new workstations combining the best features of ULW and RFES. At least one presentation 

(Bavarian, P-14) specifically recommended that ULW and RFES be retired and replaced by a 

workstation conforming to published standards for feature extraction. 

3.3.3 Compression 

“Data compression” is used for reducing data transmission times and data storage requirements. 

Latent fingerprints are rarely compressed because of a fear of losing information due to image 

degradation caused by compression/decompression. However in some cases the benefits of 

compression outweigh the losses, such as when it is desired to rapidly transmit the fingerprint to 

a central location using “handheld” wireless communication devices. 

 

2

 This workshop was held for the US Department of Homeland Security in accordance with Section 303 of the 

border Security Act, codified as 8U.C.S.1732. Specific hardware and software products identified in this report were 

used to conduct work described in this document. In no case does identification of any commercial product, trade 

name, or vendor imply recommendation or endorsement by NIST, nor does it imply that the products and/or 

equipment identified are necessarily the best available for the purpose. 

11
Only a few of the presentations dealt with compression per se. The presentation by Allinson (P17) was the most detailed. The presentation discussed how the United Kingdom (UK) is using a 

handheld device to rapidly transmit latent fingerprints (called “marks” in the UK) to a central 

location for rapid screening. The compression algorithm used is JPEG2000. (JPEG stands for 

Joint Photographic Experts Group; 2000 is the year of the standard was introduced.) The 

researchers found that JPEG2000 slightly outperformed Wavelet Scalar Quantification (WSQ). 

The presentation by Branchflower (P-11) also mentions compression. 

3.3.4 Other Needs 

The presentation by Senese (P-5) discussed a problem encountered with a livescan device. The 

problem manifested itself as an incorrect or garbled ridge flow. (It was apparently caused by a 

“stitching” problem internal to the device when putting together several sub-images). While this 

occurred with an older model, similar problems still occur in newer models, and raise concerns 

in regard to standards, testing and certification. 

3.4 Matcher Architecture 

Although not singled out in the workshop program as an area of special interest, a number of 

presentations went into depth on the computer hardware/software architecture of current 

matchers. Some presenters proposed methods for matcher improvement. The presentations by 

Bavarian (P-14) and Hara (P-15) stand out in this regard. Bavarian characterizes advanced 

matchers as being “progressive matchers” and/or “fusion matchers.” Progressive matchers may 

be viewed as having a sequential architecture in which each stage acts as a filter to reduce the 

volume entering the next stage. This is a traditional design; the FBI/IAFIS basically falls into this 

category. 

Figure 2. Multi-Stage Progressive Matcher (adapted from Bavarian) 

 Match 

Report

Type A 

Data 

Data

Type A

Type B 

Data 

Type C 

Data 

Data

Type B

Data

Type C

Data

Type D

Type D 

Data 

Match Report 

Thresholds 

Selectivity 

Reliability 

12
Each “generic” matcher in the above diagram employs a different type of data. For example, 

“Type A Data” might refer to descriptive data such as gender and age; “Type B Data” might 

refer to fingerprint pattern classification; “Type C Data” might be minutiae; while “Type D 

Data” might refer to third level features. Regardless of the actual data type, for a candidate to 

pass through a given stage (and enter the next stage) its degree of similarity to the search 

exemplar must exceed some predefined threshold. Thus in the above diagram only candidates 

passing all four stages would emerge on the final “match report.” Of course it is not necessary 

there be exactly four stages; some matchers might have more, while most would probably have 

fewer. 

In this type of architecture it is common to refer to the initial stages as “filters” or “bulk filters.” 

There is however a subtle distinction. A matcher might be employed as a “filter,” but it is not 

necessarily true that a filter can always become a matcher. A necessary requirement for a 

matcher is that it can be “throttled down” until it passes only a few candidates (say less than 

five). With a filter this may not be possible. Thus, it may be possible to “throttle down” the filter 

to where it only passes 10 % of input, but attempts at further reduction might result in nothing 

passing the filter. 

Regardless of how many stages the actual matcher has, it is virtually a certainty that with current 

matchers one of these will be a “minutiae matcher.” This remains true for single-stage matchers. 

(The only exceptions appear to be matchers which use correlation techniques to directly match 

the fingerprint images. Prototypes of this type of matcher have been made using optical 

matching.) 

Minutiae, it will be recalled, are defined as the endings or bifurcations of ridges. Figure 3 shows 

an example of extracted minutiae that are being matched between two fingerprint images. 

Right: The rolled impression “mate” of the 

latent on left. The corresponding 

minutiae are identified on the mate, and 

are connected via blue arrows. 

Left: Latent fingerprint with 

five (5) minutiae 

identified (“extracted”) 

Figure 3. Example of Minutiae Matching 

13
A second type of matcher, fusion matcher, may be viewed as two or more matchers that work in 

parallel and independently of each other. Once all the component matchers have finished, a 

separate stage combines the information output by each individual matcher. This combining 

logic is generally at the “score level,” in that the input into the combining logic consists of the 

output scores of the component matchers, possibly supplemented by ancillary information. 

Figure 4. Multi-Stage Fusion Matcher (adapted from Bavarian) 

The simplest way to combine the output scores is via straightforward addition, though this may 

not produce highest performance. To improve performance, a normalization step often precedes 

score fusion. The purpose of normalization is to make all the scores comparable in magnitude, 

and also to have scores reflect the intrinsic reliability of each component matcher. Some 

researchers have found that combining scores by score multiplication is more effective than 

addition. This may be because multiplication does not require normalization. Additional 

information on data fusion can be found in References [21][22]. 

Hara (P-15) also goes into architecture in detail, but the emphasis is somewhat different. The 

basic approach is that of “cost/benefit.” Five different matchers (or matcher stages) with 

increasing complexity are considered. The relative processing cost (in terms of time and 

computer resources) as well as match accuracy is estimated for each matcher. The cost goes up 

progressively, so that the fifth matcher has a cost of about 200 times the first. Hara’s presentation 

then goes on to outline a cost/benefit model for selecting the most cost-effective combination of 

matchers. In general this appears to be an excellent approach for identifying optimal 

combinations of matcher stages; but at a time of rapidly declining hardware costs, the “cost” of a 

stage might decrease drastically in less than a year. 

Match 

Report

Type A Data 

Matcher 

Data

Type A

Type B Data

Matcher

Data

Type B

Data

Type C

Type C Data

Matcher

Fusion and 

Intelligent 

Decision 

Module

14
Two presentations were directed toward architecture algorithms for implementing level-3 

feature, Jain and Chen (P-19) and Wasserman (P-20). Chen presented some interesting data for 

implementing level-3 features, including some results using score fusion. The data presented 

shows about 10 % increase in performance. The paper also shows that going up in resolution 

from 500 pixels per inch (ppi) [19.7 pixels per mm (ppmm)] to 1000 ppi [39.4 ppmm] adds about 

5 % to accuracy. 

Wasserman’s presentation (P-20) is concerned specifically with a Support Vector Machine 

(SVM) architecture. The presentation provides a good general introduction to SVMs. These are 

the modern descendants of neural nets, and are presently receiving considerable attention. It 

appears that the underlying theory of these machines is better developed than was the case for 

neural nets. The paper presents some work using sweat pores as features. The results obtained 

were impressive (100 % correct classification), but the test set was too small for strong 

conclusion. Also of interest is the fact that 500 ppi appears to provide adequate resolution for 

using pores under some conditions. 

Two presentations, Garris (P-27) and Grother (P-25), presented top-level architecture diagrams 

showing how to structure latent matcher tests. Garris presents several diagrams showing the 

fusion of human and machine data. Figure 5, adapted from Garris, illustrates an architecture in 

which human data is merged with machine-generated data so as to boost performance. 

Branchflower (P-11) provides some interesting diagrams for WAN-based matcher network 

systems. 

Latent 

Image Latent 

Encoder

Matcher

Tenprint 

Encoder

Hybrid Latent 

Features 

Machine Tenprint 

Features Tenprint 

Image 

Similarity 

Score 

Human Latent 

Features 

Figure 5. Hybrid Latent Encoding 

3.5 Current Performance of Latent Matchers 

A number of papers discussed the performance of present-generation latent matchers. 

The presentation by Swann (P-6) provided insight into the performance of the FBI/IAFIS. The 

results confirm the tremendous importance of image quality. For example, when employing a 

mix of image qualities representative of actual case work (Test Set SD-27) the hit rate was 54 % 

when matched against rolled impressions, and 39 % when matched against plain impressions 

(flats). When using the higher quality New York DCJS latent images a hit rate of 94 % was 

15
obtained against rolled, and 63 % against flats. Even higher performance was obtained using 

Secret Service data: 97 % against rolled and 71 % against flats. 

The presentation by Suman (P-10) also gives some performance data, but does not provide actual 

reliability figures (although providing references). The presentation includes the interesting 

statistic that out of about 100,000 monthly searches, 4000 are positively identified, for an 

“identification rate” of 4 %. (Although these numbers appear to be low, they are actually fairly 

good; most latent prints may be expected to belong to younger perpetrators who are not yet in the 

criminal master file.) The “identification rate” for latent searches by the FBI/LFPU is about 2 %. 

A number of vendor presentations also provided some performance data. The vendor data seem 

to support the idea of 70-80 % reliability on “good” latents when matched against a combination 

of rolled and flats. Table 3 summarizes the results. 

Performance of Current-Generation Latent Matcher 

Quality of Latents Type of Mate 

Rolled Plain Mixed rolled/plain 

Good and Better 94 % 63 % 78 % 

Average Case Work 54 % 39 % 47 % 

Table 3. Representative latent matcher performance on a large background 

(40+ million subjects) 

3.6 Projected Performance of Latent Matchers 

A number of vendors, for example Fondeur (P-12) and Briefs (P-13), presented data on measured 

performance of semi-lights-out systems. The vendors are cautious about presenting specific 

numbers (understandably so), and tended to provide “delta numbers” or else employ graphs 

without numerical scales. Nevertheless a good deal can be inferred from their data. Two separate 

performance losses are identified, “front-end” and “back-end.” Front-end losses result from 

automated feature extraction done by the machine. For example, when using “good” latents only, 

it appears that performance decreases by about 20 % when using machine-encoded features 

versus human-encoded features. 

Back-end losses result from the need to restrict the candidate list to only those candidates the 

computer considers “probable hit.” This is essential, otherwise the human is overwhelmed by the 

sheer number of candidates that need examination. Good back-end processing can potentially 

reduce the candidate list by a factor up to one hundred or so. According to the vendor’s 

projections, the additional performance drop incurred by “backend” processing is about 15 %. 

It is possible to make some very rough, order-of-magnitude, performance projection based upon 

the available data. We make the following assumptions: 

3⁄4 Latent images to be searched are all of “good” quality or better; 

3⁄4 Searches are performed against a very large background (about 50 million subjects); 

3⁄4 The mates (in the background) consist of a mix of rolled and plain impressions 

3⁄4 Feature extraction will be automated; 

3⁄4 The number of candidates presented to the human will be a small number (say 3 times the 

number of expected hits) 

16
We perform our estimates in two independent ways. In the first method we start with the IAFIS 

performance, which assume a large background and a mix of plain and rolled mates. However, 

these IAFIS numbers apply to human encoding and humans verifying a large number of 

candidates (say ten for each search). We need to adjust for this. Thus, we begin with a 78 % 

reliability, subtract 20 % for auto-encoding, then another 15 % for backend candidate reduction. 

The result is a net value of 43 % for the semi-lights-out reliability. This is summarized in Table 

4. 

Semi-Lights-Out Performance 

(Estimated from IAFIS Data) 

Action Resulting Value 

Begin with “human” assist value 78 % 

Subtract 20 % for auto-encoding 58 % 

Subtract 15 % for backend processing 43 % (final estimate of accuracy) 

Table 4. Estimated semi-lights-out performance extrapolated from IAFIS data 

In the second method we begin with vendor-supplied reliability for auto-encoded search against 

a medium background (2.5M fingers) and no backend processing. We then make two 

corrections. The first is for backend processing (automated candidate reduction) and the second 

is for the small size of the background. In making the background adjustment we will assume a 

1.5 % performance drop for each factor of two increase in the size of the background. The 

results are summarized in Table 5. 

Semi-Lights-Out Performance 

(Estimated from Vendor Data) 

Action Resulting value 

Begin with auto-encoded value on medium 

background 

71 % 

Subtract 15 % for backend processing 56 % 

Subtract 12 % for background increase 44 % 

Table 5. Estimated semi-lights-out performance extrapolated from vendor data 

Interestingly, both methods appear to lead to values in the low forties. This may appear 

somewhat low, but is in fact quite respectable for a preliminary screener. However, it must be 

kept in mind that these numbers are very preliminary. 

3.7 Latent Quality Measures 

Among the topics were given emphasis in the workshop program were “Latent Quality 

Measures.” The purpose of a quality measures is to assess the suitability of a fingerprint for 

some specific purpose, for example automated searching [23][24][25]. The quality measure 

algorithm might, for example, assign a value between zero and unity to an input fingerprint 

17
image, depending upon the algorithm’s assessment of how suitable the particular fingerprint is 

for automated searching. Assuming that 1.0 indicated the highest possible value, a computed 

value of 0.8 to 1.0 would indicate “very good;” a value between 0.6 to 0.8, “good;” 0.4 to 0.6, 

“fair;” 0.2 to 0.4, “poor;” and a value less than 0.2, “unusable.” The computed score can then be 

used to predict the success rate of a search. It can also be used to screen out fingerprints of very 

low value, as these would have a negligible success rate, and would waste system resources. 

Figure 6. Representative latent fingerprints showing wide range of quality 

Three presentations had as their primary topic “latent quality measures:” Tabassi (P-21), 

Wertheim (P-22), and Hicklin (P-23). The presentation by Tabassi stressed lessons learned in 

developing the NIST Fingerprint Image quality (NFIQ) algorithm. The NFIQ algorithm is geared 

toward flat fingerprints and uses a scale of 1 to 5, with 5 being lowest quality. Experiments have 

been performed to correlate the NFIQ with 1) the True Acceptance Rate (TAR) and 2) the False 

Acceptance Rate (FAR). (A high TAR is indicative of a good ability to recognize a true mate, 

and generally means good performance. A high FAR means many false candidates, and is 

generally indicative of poor performance.) 

Experiments at NIST verified that a high correlation exists between the computed NFIQ value 

and the matcher False Alarm Rate (FAR). The higher the NFIQ value, the lower the quality, and 

Ugly Unusable

Excellent Good Bad

18
the higher the FAR. (Recall that FAR means False Identification Rate or False Alarm Rate.) 

Similarly, a low NFIQ score indicates good quality, and generally produces high TAR. 

It is believed that standard fingerprint quality measures such as the NFIQ algorithm cannot be 

simply transferred in toto to latent images. For one thing, since latents contain considerably less 

information the quality assessment needs to be more delicate. A “missing” portion of the 

fingerprint image is a relatively routine occurrence, and by itself does not indicate low quality. 

Also, even though most of the area of a fingerprint is blurred, the existence of a sufficiently large 

clear area might render the latent usable. Finally, since “semi-lights-out” matchers are immature, 

we do not have a clear understanding what their “preferences” might be. The presentations by 

Wertheim and Hicklin looked into the types of additional information that may be considered in 

quantifying latent quality. Wertheim defined the “Elements of Latent Difficulty” that he uses in 

training latent examiners [P-22, slide 2]: 

3⁄4 Quantity – percent or area of full print present 

3⁄4 Clarity – Focus, resolution, detail etc. 

3⁄4 Contrast – % of Grayscale range utilized, Average (shift), etc. 

3⁄4 Pressure – Vertical pressure, ridge to furrow thickness, etc. 

3⁄4 Slippage – Lateral pressure, ridge flow distortion, smearing, etc. 

3⁄4 Background – Substrate distortion, texture interference, etc. 

3⁄4 Focal Points – Core, Delta, Occasional features, Major flow convergence/divergence, etc. 

Hicklin (P-23) underscored the issue that quality depends on the use of an image, and that 

characteristics that would constitute poor quality for an automated search may allow for great 

distinctiveness when performing human comparisons. The relation of quality to use is especially 

true for latents, because localized quality values can be used to improve the matching process. 

Hicklin defined quality metrics in terms of levels: 

3⁄4 Overall quality metrics (like NFIQ) provide an overall assessment of quality, tuned for a 

specific use 

3⁄4 Representation-based metrics (like the count of high-quality minutiae) are measurements 

of how well a given feature extraction task could be performed 

3⁄4 Feature-specific and localized quality metrics are assessments of the quality at a given 

point or small area within an image (such as the quality for a specific minutia) 

Discussions during the workshop introduced an additional complexity. It became clear that one 

needs to consider two types of latent quality measures, one geared toward machine searches, 

(machine oriented) and second toward court evidence (human oriented). These may require two 

separate paths of development. It is likely that NIST would initially concentrate on the first type. 

3.8 Proposed New Features 

Another special topic covered in the workshop was that of new features. These are features not 

currently used, but proposed as future performance enhancers Four presentations had new 

features as their primary topic: Hicklin (P-18 and P-28), Jain and Chen (P-19), Wasserman (P19
20). At least two other presentations included new features as secondary topics. The ANSI/NIST 

Committee to Define Extended Fingerprint Feature Sets (CDEFFS) (P-18 and P-28) is in the 

process of defining a new ANSI/NIST standard for such features. 

Within the current context, “features” basically mean information that will assist in either the 

search, or the verification of a candidate; of course the source of these features is the original 

fingerprint image. From an information theoretic standpoint, extracting features may be 

considered to be a type of data compression, the features themselves defining the compressed 

data. In this context, we should recall that data compression usually results in information loss; 

though in a data-rich environment some form of compression is essential. 

One may define three categories of features: 1) features which assist in the search process – these 

are of primary interest; 2) features which do not assist in the search process, but are useful in 

ruling out a candidate (negative evidence) – these are of interest especially in connection with a 

semi-lights-out system; and finally 3) features which are (currently) only of interest to human 

experts and cannot be used by machines. Though important, these are of less interest in the 

present context. 

Features are traditionally divided into three levels: 1) level-1 is the ridge-flow classification 

level; 2) level-2 is the minutiae level; and level-3) is the ridge detail level (dots, pores, 

protuberance, edge shapes). Recently is has been proposed that there should be a level-4, 

consisting of 3D features. The presentation by Hicklin (P-18) briefly covers 3D features. 

Examples of “new features” are found in Figure 7. 

“Happy Faces” are not 

Proposed New Features 

Ridge Path Analysis 

(Augmented Level-2 Feature) 

Dots and Short Ridges 

(Examples of Proposed 

Level-3 Features) 

Figure 7. Examples of newly proposed features 

Many of the newly proposed features are level-3 features, for which Figure 7 (middle) provides 

some examples. The presentation by Jain and Chen (P-19) provides a detailed discussion of how 

these features might be used for search enhancement. Wasserman (P-20) focuses on using pores 

as the level-3 feature, and in addition employs a novel architecture. As features, “pores” while 

interesting and promising are not without problems. For one thing “pores” might require higher 

resolution (usually at least 1000 ppi) for best performance. This is a problem since most present 

20
day databases (files or galleries) have been scanned at 500 ppi. This may reduce the utility of 

pores in the near future, until higher resolution files become common. A second problem is that 

pores only manifest themselves in a minority of cases, even when scanned at high resolution. By 

some estimates less than 10 % of fingerprints exhibit good pore data. The reason appears to be 

that some degree of perspiration must occur for pores to manifest themselves strongly. It would 

appear that pores by themselves may only provide moderate performance gains. 

Similar restrictions apply to many of the other level-3 features. That is, the features might be 

sporadic in appearance and may require greater resolution than currently used in practice. Thus, 

while many of the proposed features exhibit promise, a sorting-out process will be required to 

determine their relative effectiveness. This might require several years. 

The “happy face” in the above figure was included for several reasons. The first of course is 

whimsy. But there are also more serious ones. A latent examiner seeing the “happy face” in one 

print and not in its purported mate might dismiss the “match.” (Assuming that the central 

portions of both images are clear.) This illustrates the power of exclusionary features. 

There are many strange characteristics/features in fingerprints which “jump out” at a human, but 

are unfortunately very difficult to program into a machine. This brings us to our third point: 

enhancements of computer performance are often better accomplished by following a path for 

which the computer is uniquely suited, rather than trying to mimic a human. 

3.9 Test Sets 

The presentation by Wood (P-26a) summarized the NIST in-house latent test sets. There are two: 

SD-27 and SS-1000. The first contains 258 exemplars with a wide range of quality. This data set 

is available with marked-up minutiae on the latent, as well as the matching minutiae identified on 

the ten-print. Although in most respects this is an excellent test set it has two shortcomings: 1) it 

is of rather modest size (258), and 2) since it is publicly available, vendors may be overly 

familiar with it (and may have over-trained their systems to this set). 

SS-1000 is a much larger set containing about 1000 exemplars. However, this data has not yet 

been completely marked up (in the manner of SD-27), nor is it publicly available. In addition, 

since all the exemplars were obtained from AFIS hits (from latent submitted by the Secret 

Service), there is concern that the data are skewed toward “latents liked by FBI/IAFIS”. 

SD-27 

Database Size 258 latent prints from 233 subjects 

Origin Selected by FBI from FBI casework 

Purpose Originally selected to test IAFIS; later 

became an open-source test set for AFIS 

systems development. 

History Nominally 300 prints selected from FBI 

case files. Divided into three groups: 1) 

100 “good”, 2) 100 “bad” (representative 

of typical case work), and 3) 100 “ugly” 

(more difficult). Later reduced to 258 prints 

21
for various reasons. 

Size of images Type 13 records of varying size (TBD) 

Mates 233 criminal ten-print cards obtained from 

CJIS, Clarksburg, WV. Type 14 records. 

Compression Latent uncompressed; ten-prints 

compressed via WSQ 

Ancillary Data Complete “mark ups are available in two 

forms: 1) all apparent minutiae on latent 

(“ideal minutiae”), and 2) all minutiae 

having matching minutiae on ten-print 

(“matched latent minutiae”). These are 

available as type 9 records, as are the rolled 

images. 

Comments Some of these are quite difficult, 

particularly in a “lights out mode.” Publicly 

available. 

Table 6. Summary of SD-27 test set 

SS - 1000 

Database Size 1000 latents corresponding to about 600 

ten-prints 

Origin Secret Service 

Purpose Unknown (presumed for testing) 

History Selected by Secret Service form cases 

identified by IAFIS (operational data, 

2001-2004) 

Size of images Type 13 records of varying size (TBD) 

Mates Criminal ten-prints obtained from CJIS 

Compression Latent uncompressed; ten-prints 

compressed via WSQ 

Ancillary Data Unknown (TBD) 

Comments Generally much easier than SD -27. May 

be more suitable for “lights out mode.” Not 

publicly available. 

Table 7. Summary of SS-1000 test set 

The presentation by Dvornychenko (P-26b) outlined how NIST proposes to remedy the 

identified shortcomings of our data. First, we need to acquire more exemplars. Second, we need 

to employ more varied data sources. This will prevent the skewing of data toward large agencies 

and/or large AFIS systems. It has been suggested that as few as 50 exemplars from (say) ten 

smaller law enforcement agencies would help balance the composition of the data. The total 

22
number of exemplars in the “final” test set might be on the order of 5,000. Based upon feedback 

obtained in the workshop, it appears that a test set of this size would not be difficult to put 

together. Several very promising sources were identified, and are being actively pursued. 

The subject of “special test sets” was also raised in a number of presentations, including those by 

Meagher (P-1), Suman (P-10), Grother (P-25), and Dvornychenko (P-26b). The purpose of these 

“special test sets” is to identify problems which might otherwise escape notice. Problems might 

escape notice for any number of reasons, including: 1) the condition causing the problem is not 

identified, 2) there are too few exemplars with this condition, and 3) the condition occurs in 

conjunction with other factors which mask it. 

The kinds of fingerprints that would go into the a special test set are: 1) latents developed or 

lifted by the various common processes in use (e.g., chemical, fluorescent, photo, etc.) and 

preferably from the same subject; 2) latents with special pattern classes (e.g., high curvature 

whorls, accidentals, etc.), 3) fingerprints exhibiting graded degrees of shear (elastic deformation) 

from low to very high. As mentioned, such a database could be used for discovering hidden 

problems and hence improving performance. It is probable that to acquire such a database would 

require the use of volunteers. 

4. Conclusions 

We now come to an important topic: How should NIST follow up the workshop initiative in 

creating a “latent challenge?” Our basic approach is four-pronged: 

1) We will collect, electronically scan (digitize), categorize and archive latent fingerprint 

images along with their mates. We are currently actively engaged in this process and plan to 

expand the scope of the effort. In the process of scanning latents we also plan to selectively 

collect palmprint images. 

2) We will continue to refine our proposed Application Program Interface (API) for latent 

fingerprint matching software in concert with other agencies. Our experience in fingerprint 

testing performed at NIST, together with lessons learned during the workshop, will guide us 

in putting together the proposed standards (API) for the software. We fully expect this to be 

an iterative process in which we would issue a preliminary specification for review and 

comments, followed by a revised version. The process might well require several iterations. 

3) We will revisit methods of measuring the performance of latent matchers. We received a 

number of comments during the workshop along the lines that NIST is overly reliant on ROC 

type performance measurements, and that these are sub-optimal for latent searches. We will 

therefore conduct a review of theoretical alternative performance measurement concepts and 

issue the findings . 

4) We will continue to assist groups that are actively engaged in defining new features for 

computer matching. We will make assessments as to which of these features might 

reasonably be included in the matching software submitted for testing, and what special 

provisions (if any) need to be made. 

23
References 

[1] NIST Special Publication 500-245, Information Technology: American National Standard 

for Information Systems –Data Format for the Interchange of Fingerprint, Facial, & Scar 

Mark & Tattoo (SMT) Information, September 2000. Î Î 

http://fingerprint.nist.gov/standard/index.html 

[2] F.L. Podio, et al, “Roles for the National Institute of Standards and Technology (NIST) in 

accelerating the Development of critical Biometric consensus Standards for the US 

Homeland Security and the Prevention of ID Theft,” 2003. Î Î

www.itl.nist.gov/div893/biometrics/documents/White%20Paper%20Roles%20for%20NIST

%20FP&MH%20March11%202003.PDF

[3] NIST Image Group’s Mugshot / Facial Research (Website) Î Î

http://www.itl.nist.gov/iad/894.03/face/face.html 

[4] Iris Challenge Evaluation (Website) Î http://iris.nist.gov/ice/

[5] "Use of Technology Standards and Interoperable Databases With Machine-Readable, 

Tamper-Resistant Travel Documents," a joint report to Congress from Department of Justice, 

Department of State, and National Institute of Standards and Technology. 

[6] NIST Image Group’s Patriot Act Research (Website) Î Î

http://www.itl.nist.gov/iad/894.03/pact/pact.html

[7] C.L. Wilson, M.D. Garris, C.I. Watson, NISTIR 7110, “Matching Performance for the USVISIT IDENT System Using Flat Fingerprints,” May 2004. Î Î

ftp://sequoyah.nist.gov/pub/nist_internal_reports/ir_7110.pdf

[8] S.S. Wood and C.L. Wilson, NISTIR 7112, “Studies of Plain-to-Rolled Fingerprint 

Matching Using the NIST Algorithmic Test Bed (ATB),” April 2004. Î Î

ftp://sequoyah.nist.gov/pub/nist_internal_reports/ir_7112.pdf

[9] Biometrics Technology: Identification and Verification (Website) Î Î

http://www.itl.nist.gov/iad/programs.html#BIOMETRICS

[10]ITL Biometrics Resource Center Website: About Biometrics Î Î

http://www.itl.nist.gov/div893/biometrics/about.html

[11]Biometric Authentication Technology: From the Movies to Your Desktop Î Î

www.itl.nist.gov/div893/biometrics/Biometricsfromthemovies.pdf

[12]B.S. Swann (FBI/CJIS), “Needs and Applications of Latents at FBI/CJIS,” presentation at 

Latent Testing workshop, NIST, April 2006. Î Î

http://www.itl.nist.gov/iad/894.03/latent/workshop/proc/P6_Swann_LatentOverview_2.pdf

[13]Information Access Division (IAD) Webpage Î http://www.itl.nist.gov/iad/

[14]NIST Image Group’s Fingerprint Research (Website) Î http://fingerprint.nist.gov/

[15]Fingerprint Vendor Technology Evaluation (FpVTE) (Website) Î Î

http://fpvte.nist.gov/index.html

[16]SDK Tests Website Î http://fingerprint.nist.gov/SDK/index.html 

[17]MINEX04 Website Î http://fingerprint.nist.gov/minex04/index.html 

24
[18]Ongoing MINEX (Website) Î Î 

http://fingerprint.nist.gov/minex/index.html

[19]S.S. Wood, et al, NIST, “Latent Test Sets: Current Inventory,” presentation at Latent Testing 

workshop, NIST, April 2006. Î Î

http://www.itl.nist.gov/iad/894.03/latent/workshop/proc/P26a_S_Wood_ssw_latent_data_v2.

pdf

[20]Latent Testing Workshop (Website) Î http://www.itl.nist.gov/iad/894.03/latent/ 

[21]E. Tabassi, G. Quinn, P. Grother, “When to Fuse Two biometrics,” Computer Vision and 

Pattern Recognition, Biometrics Workshop, NY, 2006. 

[22]B. Ulery, A. Hicklin, C. Watson, et al, NISTIR 7346, “Studies of Biometric Fusion,” Sept. 

2006. Î http://www.itl.nist.gov/iad/894.03/pact/ir_7346.pdf

[23]NIST Fingerprint Image Software (NFIS2) Î Î

http://fingerprint.nist.gov/NFIS/index.html

[24]NIST Fingerprint Image Quality (Website) Î Î

http://fingerprint.nist.gov/NFIS/index.html#nfiq 

[25]Biometric Quality Workshop (Website) Î Î

http://www.itl.nist.gov/iad/894.03/quality/workshop/index.html 

25