Studies of Plain-to-Rolled Fingerprint

Matching Using the NIST

Algorithmic Test Bed (ATB)

NISTIR 7112

Stephen S. Wood

Charles L. Wilson

April 2004
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Studies of Plain-to-Rolled Fingerprint Matching

Using the NIST Algorithmic Test Bed (ATB)

Stephen S. Wood & Charles L. Wilson

(from the National Institute of Standards & Technology)

EXECUTIVE SUMMARY

A series of fingerprint matching studies have been conducted on an experimental laboratory system called the

Algorithmic Test Bed (ATB). The ATB is a system used to test the functional characteristics of the Automated

Fingerprint Identification System (AFIS) component of the Federal Bureau of Investigation’s (FBI) Integrated

Automated Fingerprint Identification System (IAFIS). It was supplied by Lockheed Martina

, the vendor of IAFIS,

whose technology it shares. The ATB includes a built-in gallery of nearly 1.2M subjects, effectively a 3%

representative sample of the FBI’s criminal master file, with some room for additions. The ATB provides broad

control over its operating modes and set points.

The ATB, like IAFIS, was originally designed to identify (and verify) subjects using submitted (predominantly rolled)

fingerprint cards. The database against which the submitted cards were to be matched was likewise predominantly

composed of rolled images from fingerprint cards. As demand rose for the use of plain (or flat or slap) images,

ascertaining the operational characteristics of this matcher with plain submissions became important.

These studies were organized in three major groups. Studies of plain-to-rolled matching as a function of data

source made up the first group. Studies of plain-to-rolled matching using large sets of operational data made up the

second. Studies of plain-to-plain matching made up the third.

The studies answered nine major questions:

(1) how accurate is plain-to-rolled identification? is it less accurate than rolled-to-rolled?

Failure to identify (1-TAR) can be 2% or less. Accuracy can be as good as with rolled prints of average quality.

(2) how accurate is plain-to-rolled identification with fewer than ten fingers?

Accuracy with fewer than ten fingers can exceed accuracy with ten fingers, but performance penalties can be

expected (see next question).

(3) are performance penalties incurred by adopting plain-to-rolled identification? if penalties are incurred,

how great are they?

Performance (throughput) deteriorates markedly as the amount of data is reduced. Using plain rather than

rolled fingerprints in 10-finger or 8-finger matching can be expected to result in a three-fold reduction in

throughput. Reducing the number of fingers from ten to two can be expected to reduce throughput by a factor

of ten or more. These effects are independent of one another, and thus multiplicative.

 

a Specific hardware and software products identified in this report were used in order to adequately support the

development of technology to conduct the performance evaluations described in this document. In no case does

such identification imply recommendation or endorsement by the National Institute of Standards and Technology,

nor does it imply that the products and equipment identified are necessarily the best available for the purpose.
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(4) are there significant differences in accuracy and performance between the fingerprint images supplied by

two different models of livescan devices? if there are differences, how great are they?

There are significant differences in both matching accuracy and performance. In this case, the inferior set of

images showed a rate of failure to identify (1-TAR) 68% to 94% higher than that shown by the superior, and a

filter rate 24% to 60% higher.

(5) does gallery size affect accuracy? if there is an effect, how can it be characterized?

Within the range of gallery sizes covered by these studies (160,000 to 42,000,000), gallery size did not affect

overall accuracy (TAR).

(6) does the ATB accurately model the behavior of IAFIS?

In a word, yes.

(7) how accurate is plain-to-plain identification?

Failure to identify (1-TAR) can be 2% or less. There is more variability than evident with rolled-to-plain.

(8) how accurate is plain-to-plain identification with fewer than ten fingers?

As with rolled-to-plain, accuracy with fewer than ten fingers can exceed accuracy with ten fingers, with a

failure rate of well under 1%. There is more variability than evident with rolled-to-plain, and performance

penalties can be expected.

(9) are performance penalties incurred by adopting plain-to-plain identification?

Yes. Filter rates can approach 50%, which means matching every other subject. (Compared to the 2% filter

rate typical of rolled-to-rolled tenprint matching, a 50% rate implies a 25-fold increase in matching workload.)

We were able to draw other conclusions as well:

There were clear differences, but the interclass difference (between rolled and plain) was smaller than the intraclass

differences (between BCII and Identix and between CrossMatch and Smiths Heimann).

There were similarities among all the sets; the invariance of TAR over gallery size and the essentially linear

relationship of FAR to gallery size were both notable.

The ATB works most accurately (i.e., has the lowest failure rate, or (1-TAR)) with two index fingers. The addition of

more fingers raises its throughput markedly but with a concomitant reduction in TAR. The use of six or more

fingers reduces the FAR to almost zero.
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1. INTRODUCTION

NIST recently conducted a series of fingerprint matching studies using an experimental

laboratory system called the Algorithmic Test Bed (ATB). NIST’s specimen of the ATB, a

system used to test the functional characteristics of the AFIS component of the FBI’s Integrated

Automated Fingerprint Identification System (IAFIS), includes a gallery of nearly 1.2M

subjects; it provides broad control over its operating modes and set points [6]. This report

focuses on one aspect of those studies: the matching of plain to rolled (and plain to plain)

fingerprint imagesb

.

The importance of measuring the efficacy of matching plain to rolled and plain to plain images

stems from (at least) three sources:

(1) the desirability of rapidly and expeditiously fingerprinting subjects without

compromising the ability to compare the fingerprints thus captured to those vast sets of

rolled fingerprints already in legacy databases: although capturing plain fingerprints is

faster and easier than capturing rolled fingerprints, rolled fingerprints predominate in

existing databases [5];

(2) the passage of PL 107-56 and PL 107-173, that effectively require NIST to determine the

efficacy of both plain-to-rolled and plain-to-plain matching (as potential solutions to

some of the many mandates of these laws) [1,2]; and

(3) the implicit requirements imposed by the Daubert decision on the criminal justice

community for the statistical data necessary to provide the quantitative, scientific basis

for the continued use of expert testimony in the field of fingerprint identification and

verification [7,8,9].

There were two additional reasons for performing these studies:

(1) it is of value (particularly to both NIST and the FBI) to know whether the ATB closely

models the IAFIS with respect to (w.r.t.) fingerprint matching, and therefore whether

fingerprint matching experiments performed on the ATB can properly be used to make

statements and predictions about IAFIS; thus we sought a calibration of the ATB, using

the operational IAFIS as a baseline; and

(2) it is also of value (particularly to both NIST and the Ohio BCII) to know whether

different mechanisms of acquiring images produce images of different matchability; thus

we made a comparison, w.r.t. the accuracy of fingerprint matching, of images from

different sources, acquired by different means.

Six sets of data were used, each from a different source. These six sets were respectively

referred to as:

 

b

 Although we speak of matching images, the actual process of matching generally operates on ‘features’ or

‘templates’ which the matching system has extracted from the images in a preceding process.
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(1) BCII (Ohio Bureau of Criminal Identification and Investigation),

(2) TXDPS (Texas Department of Public Safety),

(3) INS10 ([former] Immigration and Naturalization Service (INS) tenprint),

(4) INS_ben ([former] INS benefits),

(5) INS_plain ([former] INS border apprehensions), and

(6) BCC (Department of State (DoS) Border Crossing Cards).

These studies of this data fell into three broad groups.

The first group of studies, which looked at the effects of the source and type of data on plain-torolled matching, used only data from set 1, the Ohio BCII data.

The second group of studies, which examined the accuracy and throughput of plain-to-rolled

matching using large sets of operational data, used only data from sets 2, 3, and 4, the Texas

Department of Public Safety data, the INS tenprint data, and the INS benefits data, respectively.

The third group of studies, which explored the accuracy and throughput of plain-to-plain

matching, used some data from set 1, together with data from two large sets, 5 and 6,

respectively the INS border apprehensions and the DoS Border Crossing Cards.

Of these, four sets (1, 2, 3, and 5) had been captured as law enforcement data, while the

remaining two sets (4 and 6) had been captured as civil administrative data.

The studies addressed nine questions pertinent to automated fingerprint identification.

(1) how accurate is plain-to-rolled identification? is it less accurate than rolled-to-rolled?

(2) how accurate is plain-to-rolled identification with fewer than ten fingers?

(3) are performance [in the sense of speed or throughput] penalties incurred by adopting

plain-to-rolled identification? if penalties are incurred, how great are they?

(4) are there significant differences in accuracy and performance between the fingerprint

images supplied by different models of livescan devices and different data sources? if

there are, how great?

(5) does gallery size affect accuracy? if there is an effect, how can it be characterized?

(6) does the ATB accurately model the behavior of IAFIS?

(7) how accurate is plain-to-plain identification?

(8) how accurate is plain-to-plain identification with fewer than ten fingers?

(9) are performance penalties incurred by adopting plain-to-plain identification?

These studies used the ATB in its normal operational mode: that is, filteringc

 aggressively [6].

 

c

 Filtering, together with several other terms, is described in the glossary (Appendix E). The term refers to reducing

the number of potential candidates in order to reduce the number of matches to be evaluated. Because matching

requires much more work than filtering, filtering aggressively increases throughput.
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2. DATA

The characteristics of the six data sets used in this test are summarized below in table 1. A

detailed description of each set is provided in the six subsections immediately following. The

preparation of each data set is covered in section 3. Data analysis and the configuration of the

matcher are described in section 4. Results are discussed in section 5. Conclusions are presented

in section 6.

The first column of table 1 identifies the data set (or subset) by name and number. The second

refers to its role in the plain-to-rolled matches: whether it provided members of the probe, the

galleryd

, or both. The third column is similar, but refers to the plain-to-plain matches. The

fourth column refers to the number of subjects whose images we used in these studies. The fifth

column refers to the nature of the population from which the images were taken. The sixth

shows the predominante process by which the images were captured in digital form; the phrase

‘mostly ink’, in column six, refers to a set of images, most of which were digitized by optically

scanning inked cards. The seventh describes the initial type of image; the word ‘segment’, in

column seven, means that the images of the eight fingers were segmented from two slap-4

images by a computer code.

Data Set Number,

Data Set Name

role:

plainrolled

role:

plainplain

working

size

criminal

civil

livescan

rescan

mostly ink

rolled

plain

segment

ATB background gallery gallery 1.2M criminal mostly ink rolled

1 Ohio BCII - - - - - -

 1.1 BCII master probe 925 criminal mostly ink rolled

 1.2 Identix probe 925 “ livescan rolled

 1.3 CrossMatch probe pr/ga 923 “ livescan segment

 1.4 SmithsHeimann probe pr/ga 925 “ livescan segment

 1.5 IAFIS master gallery 925 “ mostly ink rolled

 1.6 BCII plain pr/ga 630 “ mostly ink segment

 1.7 Identix plain pr/ga 904 “ livescan segment

 1.8 IAFIS plain pr/ga 793 “ mostly ink segment

2 TXDPS [TXDPS] probe 60K criminal mostly ink segment

3 INS10 [DHS10] probe 48K criminal rescan segment

4 INS_ben probe 60K civil livescan segment

5 INS_plain [DHS2] pr/ga 300K criminal livescan plain

6 BCC [DOS] pr/ga 274K civil livescan plain

(Table 1 ATB data sets)

2.1 The Ohio BCII data (set 1) initially comprised five subsets of comparable images, each

subset nominally containing a tenprint record for each of the 925 subjects involved in the study

 

d

 A probe is a set of records of nominally unknown subjects for which we seek mates in a set of known subjects, or

gallery; see glossary.

e

 Subsets 1.1, 1.5, 1.6, and 1.8, together with sets 2, 3, and 4, all seem to have been acquired and digitized using

more than one process; however, in each one process seems to have predominated. 
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[14]. The State of Ohio’s Bureau of Criminal Identification and Investigation (BCII) provided

the first four of the subsets on CDROMs; the Federal Bureau of Investigation’s (FBI’s) Criminal

Justice Information Services (CJIS) Division similarly provided the fifth subset.

The same 925 subjects supplied the images within each subset, but a different method was used

to acquire the images of each subset. Two of the subsets were acquired from criminal master

files (CMF) from BCII and from the FBI, respectively; the other three were acquired by each of

three models of livescan fingerprint machines (Identix TP600/2000, CrossMatch CMT 442, and

Smiths Heimann LS2-Check RJ0444, respectively [14]). In both CMF subsets inked images

predominated, although both subsets also contained substantial numbers of livescan images (see

Appendix D). Of the three livescan machines, the Identix produced both rolled and plain

impressions, whereas the other two produced only plain impressions.

The first subset (1.1, BCII) of data set 1 was taken from the BCII’s master file. The second

through fourth subsets (1.2, Identix; 1.3, CrossMatch; 1.4, Smiths Heimann) were acquired

(apparently in the last week of June through the first week of July of 2003) by fingerprinting

each subject on each of the three makes of livescan machines. CJIS provided the fifth subset of

data (1.5, IAFIS), which it created by extracting the images for each of the 925 subjects from the

master files of its Integrated Automated Fingerprint Identification System.

Three additional subsets of data were derived from set 1: 1.6, BCII_plain; 1.7, Identix_plain; and

1.8, IAFIS_plain. They are described in some detail under data preparation for plain-to-plain

matching (section 3.3)

Early in the course of the experiment, the existence of three major difficulties with using the

BCII subset to seed the gallery became apparent. First, images of three subjects were missing

from the BCII subset, although these images were later supplied. Second, there were some

discrepancies in the ‘FBI number’f

 field in the cross-reference table. Third and most important,

because the images in the FBI’s master file were often not the same as those in the BCII’s master

file, comparability between the FBI’s results and NIST’s results would be compromised. In

order to overcome these difficulties, the IAFIS subset was acquired and used to seed the gallery.

It is important to note that there were three fundamentally different types of sources of these

subsets: CMF in rolled and plain tenprint card format (BCII, IAFIS), livescan in rolled and plain

tenprint card format (Identix), and livescan in plain-only format (CrossMatch, SmithsHeimann).

Most of both the BCII and IAFIS subsets had been optically scanned from paper tenprint cards.

An extremely common tenprint card, used by the FBI as well as much of the United States law

enforcement community, the FD-249, appears in Figure 1. The FD-249 has distinct areas

allocated for each of 14 original images: ten rolled images (in the boxes labeled with the finger

 

f

 FBI number is a number assigned by the FBI to identify a subject’s records; see glossary
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positiong

 and name, for example ‘1. R. THUMB’), two plain thumb images, two slap-4 images.

In these BCII and IAFIS cards, inked images predominated (Appendix D has a full discussion).

(Figure 1 FBI Form FD-249) [4]

Visual inspection of a small sample of records showed a minor difference between the BCII and

IAFIS records: substitution of livescan images for inked images had proceeded frequently on a

 

g

 ‘finger position’, by convention, is a number from 1 (right thumb) through 10 (left little); see glossary
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finger-by-finger basis in the former but generally on a subject-by-subject basis in the latter.

The Identix subset consisted of livescan images in tenprint card format (i.e., with 14 original

images, 10 rolled and four plain). These images had remained in digital (electronic) form since

the time of their initial acquisition; they had not been rendered onto paper and then scanned.

The records in the CrossMatch and Smiths Heimann subsets each consisted of livescan images

taken from three original images (right slap-4, thumb ‘slap-2’ (i.e., left thumb and right thumb

captured simultaneously), left slap-4), segmentedh

 automatically, and used to generate a record in

tenprint card format.

There is no representation that the same livescan machine was used for all of the acquisitions by

a given make; in the case of Identix, the records themselves indicate that two different (but

substantially similar: TP-600 and TP2000) models were used. The CrossMatch machine(s) was

identified as a model ‘442' (now supplanted by the ‘500’). The Smiths Heimann machine(s) was

identified as a model LS2 Check.

2.2 TXDPS data

The TXDPS data (set 2) was obtained from the Texas Department of Public Safety (TXDPS),

and consisted of images obtained by scanning conventional tenprint fingerprint cards from the

TXDPS master file. Inked cards predominated. However, not all of the fingerprint cards used

the same layout as the FD-249. Some used substantially different positions for the impressions

of the fingers. It is evident that there were problems physically registering some of the cards in

the optical scanner, and in some cases even in selecting a scanner format appropriate to the card.

Despite these problems, most images seemed to be properly scanned and registered. Although

the data set was considerably larger, the ATB study only addressed the first 60K (after

consolidationi) [4]. The reason underlying the limitation to 60K is elaborated under Data

Preparation (below).

2.3 INS10 data

The INS10 (also DHS10) data (set 3) was obtained from the Immigration and Naturalization

Service (INS), which then became the Bureau of Immigration and Customs Enforcement (BICE),

and is now part of the U. S. Department of Homeland Security (DHS). In other reports this data

may also be referred to as DHS10 data. Like the BCII Identix data (subset 1.2), it for the most

part originally consisted of livescan images in tenprint card format (i.e., with 14 original images,

 

h ‘Segmentation’ can refer in this context to two distinct processes. The first occurs when a multi-finger (e.g., slap4) image is resolved into its constituent fingerprint images. The second occurs when a tenprint card (e.g., an FD249) is optically scanned and segmented into its 14 constituent fingerprint image areas. We shall refer to the first as

segmentation and to the second as scanning (or scanning and registration).

i

 ‘Consolidation’ in this case refers to elimination of additional images pertaining to a given subject; see glossary
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10 rolled and four plain). Unlike the BCII Identix data, most of it was then printed onto paper

cards and subsequently re-scanned prior to its receipt by NIST [4]. Although 54K records were

initially available, after consolidation only 48K unique subjects remained.

2.4 INS_ben data

The INS_ben data (set 4) was obtained from the Immigration and Naturalization Service. It is

referred to as INS benefits data. Like the BCII Identix data (subset 1.2), it for the most part

originally consisted of 14 livescan images in tenprint card format, and remained in electronic

form from its acquisition until its receipt by NIST. Together with the BCC data (set 6), it is civil

in origin. As with the TXDPS data (set 2), the ATB study only addressed the first 60K (after

consolidation).

2.5 INS_plain data

The INS_plain (also DHS2) data (set 5) was obtained from the INS IDENT (Automatic

Biometric Fingerprint Identification) System. It was taken from INS (or DHS) recidivist cases:

repeat ‘contact’ with border crossing offenders. These plain (flat) pairs of fingerprints of left and

right index fingers were captured in the field by border patrol operations, for the most part in the

American Southwest, using DFR-90 livescan units [4]. Although over 600K subjects were

available, scheduling limitations and our experimental design constrained our probe set to 60K.

2.6 BCC data

The BCC (also DOS) data (set 6) was obtained from the U. S. Department of State. Like the

INS_plain data set, it consists of plain pairs of fingerprints of left and right index fingers

captured using DFR-90 livescan units. Unlike the INS_plain data set, the images were captured

in an office environment (consular offices) as part of a civil procedure (issuance of Border

Crossing Cards) [4]. About 274K subjects were available, but scheduling limitations and our

experimental design constrained our probe set to 60K.
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3. DATA PREPARATION

In preparing data, there were two absolute limits that affected set size: the allocated size of each

of the two ATB repositories. The repository with a built-in gallery of 1.2M rolled tenprint

records had additional space for only 60K more. The entire capacity of the (initially) empty

repository was 600K. These limits of 60K and 600K had no effect on small sets (set 1). They

profoundly affected the choices made for sets 2, 4, 5, and 6.

3.1 Data Preparation - Group 1

Each subset of set 1 appeared in its own directory, and had its own numbering schema. For the

four subsets provided by BCII, BCII also provided a cross-reference table showing the four

corresponding file names for each of the 925 subjects. The FBI provided an equivalent crossreference table. We used these records as originally provided after performing some simple

manipulations to ensure consistent file names, to insert fields required by the EFTS specification

[12], and so on; no modifications to the images themselves were required.

3.2 Data Preparation - Group 2

Sets 2, 3, and 4 were all prepared differently from set 1. In each of these sets there were no

(intentional) correspondences of records from different sources. Rather, the two problems were

segmentation and consolidation.

Sets 2, 3, and 4 each contained both rolled and plain images. To generate two records (one of

rolled images, one of plain) for each subject, the slap-4 images were segmented. The only

records used were those from which the segmentation software extracted eight imagesj

. The

rolled images generated the gallery subset of each set, while the plain generated the probe. This

generation was performed for the NIST VTB studies [4] using NFIS software [3]. Records

produced in the course of those studies were available to and used by the ATB studies.

The consolidation of all three sets is described in Appendix E.

At the end of segmentation and consolidation, we had extracted probe sets of 60K records from

sets 2 and 4, and 48K records from set 3. These we randomized into 10 (or 8) subsets of 6K each

in order to develop standard deviations (and thus a statistically valid measure of our confidence

in our numerical results) for our mean TAR and FAR scores.

3.3 Data Preparation - Group 3

Group 3 contained five subsets of set 1, together with two large sets, 5 and 6.

 

j

 Were the segmentor to extract images of three or fewer fingers from a slap-4 image, we would not be sure which

fingers they were.
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3.3.1 Data preparation - group 3 - plain subsets of set 1

There were three subsets of set 1 whose tenprint card records originally had contained both

rolled images and plain images: the BCII criminal master file records, the Identix livescan

records, and the IAFIS criminal master file records. We had already created subsets 1.1, 1.2, and

1.5, respectively, from the rolled images for the group 1 studies. We now created three new

subsets, 1.6 BCII plain, 1.7 Identix plain, and 1.8 IAFIS plain, from the plain images. We used

these, together with subsets 1.3 and 1.4, for the group 3 studies.

To create these three new subsets, we used a NIST Fingerprint Image Software (NFIS) code to

extract the two plain thumb and two slap-4 images from each tenprint record [3]. We

decompressed the two slap-4 images using another code from NFIS, and converted them to a Sun

raster format [11]. We segmented them on the ATB’s HP n-class processor using a segmentor

code provided by CJIS. The segmentor was not always able to identify and extract four single

finger images from each slap-4 image, which is eight single finger images per subject. It was not

successful in extracting all eight such images for 295 subjects in the case of the BCII master

cards, for 21 subjects in the case of the Identix cards, and for 132 subjects in the case of the

IAFIS master cards. We kept all records for which it did extract eight images, but we discarded

all others. We recompressed all retained images at a low compression (near 5.), and then created

an EFTS record from these eight recompressed images and the two still-compressed plain

thumbs.

Although the decompression and recompression of an image can present difficulties, it was

unavoidable: the ATB will not accept uncompressed images. Use of an unagressive setting for

recompression served to minimize any possible damage from degradation of the image.

Thus we were able to create new plain subsets with 630 subjects from the BCII master tenprint

cards, 904 from the Identix, and 793 from the IAFIS master. Existing plain subsets CrossMatch

and Smiths Heimann held 923 and 925, respectively, for a total of 4175 plain records in the five

plain subsets. The number of subjects common to any pairing of plain subsets ranged from 545

(BCII master – IAFIS master) to 923 (CrossMatch – Smiths Heimann).

3.3.2 Data preparation - group 3 - large sets

Sets 5 and 6 were all prepared similarly to sets 2, 3, and 4 (in group 2) w.r.t. both segmentation

and consolidation.

Sets 5 and 6 presented their own problems. The ATB was designed as a rolled-to-rolled tenprint

matcher, and was provided with a background gallery built from 1.2M rolled tenprints.

Although it is perfectly feasible to conduct searches of fewer than ten fingers (one can specify

which fingers are to be employed in the search), it is not even possible to conduct a search

against a gallery of plain images when the entire gallery is consists of rolled images, and thus has
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no plain images against which to search. It was therefore necessary to provide our own

background gallery. The ATB came with an empty (reusable) repository with the capacity for

600K subjects, into which we loaded our background gallery of plain images.

The ATB requires a minimum of 5 seconds to characterizek

 a subject’s record; it does not matter

whether the record contains 1, 2, or 10 fingerprint images. To build a gallery of 600K subjects,

each record containing the two index fingers, requires at a minimum 3M seconds (or 35 days) of

characterization time, in addition to the time necessary to load the records into gallery. Loading

the 600K characterized records into the gallery itself requires about one week. Additional time is

required to characterize the probe set. Other ATB functions (e.g., searching, adding records to a

gallery) cannot be carried on at the same time as characterization.

These scheduling constraints, coupled with externally mandated priorities, compelled that the

gallery size for the initial plain-to-plain matching be much smaller than the 600K capacity.

When the first 60K INS_plain records had been characterized (both probe set and gallery set), we

performed a study using a 60K probe set and 60K gallery set. As machine time became

available, we were able to characterize the entire 274K records in the BCC set, and perform a

BCC study using a 60K probe set against a 274K gallery set. As more time became available,

we were able to characterize 240K more INS_plain records, or 300K in all. We added these to

the 274K BCC records, so that we would be able to conduct still more studies, searching each

60K probe set against the combined 574K gallery.

 

k

 characterization, as viewed by the ATB, means extraction of the information used to make matches; see glossary
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4. DATA ANALYSIS

For all three groups of studies, we used our own scoring codes rather than the ATB’s code to

score and analyze the raw results produced by the ATB.

4.1 Data Analysis - Group 1

In tenprint mode the ATB identified 24 records in the BCII data with candidate matches in the

1.2M background gallery. For each of these 24 records, we compared the FBI number of the

candidate from the background gallery to the FBI number of the IAFIS image used to seed the

gallery. In all cases except one, the FBI numbers corresponded. The FBI confirmed that the

exceptional case was a consolidation: the same subject had appeared in its database twice, under

two different FBI numbers. It further confirmed that the consolidation has since been rectified in

its master files.

For each of the 24 FBI numbers described above, we replaced the FBI number reported with the

number of the corresponding record in our subset 5 (and purged duplicated reports of a match)

prior to scoring. That is, we created a virtual consolidated gallery GC

 (see Appendix G) by

ignoring any matches to elements of (G - GC).

4.2 Data Analysis - Group 2

For sets 2, 3, and 4, we had already checked for consolidations. However, as mentioned above,

these data sets contained enough probe images that it was possible to obtain even more

meaningful statistics. We subdivided each actual probe set into randomly selected subsets of 6K

each (a special randomizing code was written), searched with each of these subsets, and then

determined the mean and standard deviation of the measures of the accuracy (e.g., TAR, FAR) of

these probes. This in turn quantified our confidence in these measures of accuracy.

4.3 Data Analysis - Group 3

Group 3 can be subdivided into the plain subsets of the Ohio BCII data and the two large sets of

two-finger plain data, 5 and 6.

4.3.1 Data analysis - plain subsets of BCII

The subsets of set 1 with plain images (1.3, CrossMatch; 1.4, Smiths Heimann; 1.6, BCII_plain;

1.7, Identix_plain; and 1.8, IAFIS_plain) were analyzed in a somewhat different manner. In all,

20 searches were conducted. Each subset was in turn treated as a probe; that probe was then

used to search against each of the remaining four subsets, which took turns serving as the gallery.

For example, with 1.3 as the probe, a search was made with 1.4 as the gallery, then with 1.6 as

the gallery, then with 1.7 as the gallery, and then with 1.8 as the gallery; next, with 1.4 as the

probe, a search was made with 1.3 as the gallery, then with 1.6 as the gallery, then with 1.7 as the
Studies of Plain-to-Rolled Fingerprint Matching

Page 14 of 47

gallery, and then with 1.8 as the gallery; and so on.

The analysis of the BCII plain-to-plain matching is covered in more detail in the discussion of

results.

4.3.2 Data analysis - large sets of two-finger plain

Sets 5 and 6 were analyzed in much the same manner as sets 2, 3, and 4. However, for these two

sets we were conducting plain-to-plain matching, so that searching against the 1.2M rolled

background supplied by the ATB would have been meaningless. Instead we used two types of

galleries. The first type was composed of images from the same source or origin as the relevant

probe set. The second type was a composite: the union of the set 5 targetl

 and the set 6 target.

As with sets 2, 3, and 4, both automated consolidation and random partition of the 60K probe

into subsets (or subprobes) of 6K each were employed.

4.4 Matcher Configuration

The ATB was used in the default configuration, as delivered by Lockheed Martin (LM). This

configuration is essentially that used by IAFIS. Details of this configuration appear in Appendix

A. When computing the true accept rate (TAR) we treated the ATB as if it only returned the

rank-1 score, although when determining the false accept rate (FAR), we counted any reported

mis-identificationm.

 

l

 a target is a set of fingerprint records of known subjects; it is a superset of a gallery; see glossary

m TAR and FAR are described in the glossary (qv); the choice of a pessimistic scoring methodology for TAR stems

from the fact that the ATB models an operational matcher; in an operational matcher, the cost of a human review of

a long list of candidates is not trivial: the ideal length of the list in the case of a match is 1, and in the case of a nonmatch is 0
Studies of Plain-to-Rolled Fingerprint Matching

Page 15 of 47

5. RESULTS

The results for each group are discussed in turn.

5.1 Results from Group 1

Accuracy of plain-to-rolled identification

Table 2 shows that plain-to-rolled identification matching can be quite accurate. This accuracy is

shown by a TAR of 97.5% using the Smiths Heimann plain images, the same as the mean TAR

obtained using rolled (i.e., BCII and Identix) images. The Smiths Heimann plain subset

produced 23 failures to identify in 925 cases, while the rolled Ohio BCII master file images

yielded 27 failures and the rolled images from an Identix livescan resulted in 19 failures. Table 2

also shows that plain-to-rolled can be significantly less accurate than rolled-to-rolled

identification matching: the results using images from the CrossMatch show a value of (1-TAR)

that is 68% greater than the aggregate from rolled-to-rolled matching.

[10-FINGER

 vs IAFIS rolled]

TAR 1-TAR

(FRR)

FAR filter

rate

BCII: rolled, mostly paper 97.1% 2.9% 0.0% 1.3%

Identix TP600/2000: rolled 97.9% 2.1% 0.0% 1.3%

CrossMatch 442: flat-4 95.8% 4.2% 0.0% 7.2%

SmithsHeimann LS2: flat-4 97.5% 2.5% 0.0% 4.5%

(Table 2 BCII series, matches incorporating both index fingers, 10-finger)

Accuracy of plain-to-rolled identification with fewer than ten fingers

Tables 3 and 4 show that plain-to-rolled identification matching with fewer than ten fingers can

also be quite accurate. Table 3 shows that eight finger (i.e., excluding the thumbs) matching

achieved a TAR of 98.2% using the Smiths Heimann plain images, the same as the mean TAR

obtained using the equivalent rolled images. Table 4 shows that two finger matching achieved a

TAR of 98.4% using the Smiths Heimann images. This latter is a respectable TAR but inferior

to the mean TAR of 99.1% achieved using rolled images. As with table 2, the use of eight finger

and two finger plain images from a different source can also achieve results of markedly higher

inaccuracy (3.5% vs. 1.8% and 2.7% vs. 1.6%, respectively).

[8-FINGER (2-5,7-10)

 vs IAFIS rolled]

TAR 1-TAR

(FRR)

FAR filter

rate

BCII: rolled, mostly paper 97.9% 2.1% 0.0% 3.9%

Identix TP600/2000: rolled 98.5% 1.5% 0.0% 3.9%

CrossMatch 442: flat-4 96.5% 3.5% 0.0% 15.5%

SmithsHeimann LS2: flat-4 98.2% 1.8% 0.0% 10.5%

(Table 3 BCII series, matches incorporating both index fingers, 8-finger)
Studies of Plain-to-Rolled Fingerprint Matching

Page 16 of 47

[2-FINGER (2,7)

 vs IAFIS rolled]

TAR 1-TAR

(FRR)

FAR filter

rate

BCII: rolled, mostly paper 98.7% 1.3% 1.2% 29.8%

Identix TP600/2000: rolled 99.5% 0.5% 0.4% 29.8%

CrossMatch 442: flat-4 97.3% 2.7% 0.8% 50.7%

SmithsHeimann LS2: flat-4 98.4% 1.6% 0.9% 40.9%

(Table 4 BCII series, matches incorporating both index fingers, 2-finger)

Tables 5 and 6 show the results of plain-to-rolled identification matching using the fingers of

only one hand (the right). Table 5 shows that employing four fingers (i.e., the right hand

excluding the thumb), the ATB achieved a TAR of 94.3% using the Smiths Heimann plain

images and 90.9% using the CrossMatch. Both of these were markedly inferior to either of the

rolled-to-rolled results, which had a mean TAR just under 97.5%. Table 6 shows that using two

fingers (the right index and middle) gave TARs that were almost the same as with four fingers.

[4-FINGER (2-5)

 vs IAFIS rolled]

TAR 1-TAR

(FRR)

FAR filter

rate

BCII: rolled, mostly paper 96.8% 3.2% 0.3% 16.6%

Identix TP600/2000: rolled 98.1% 1.9% 0.3% 16.6%

CrossMatch 442: flat-4 90.9% 9.1% 0.0% 39.1%

SmithsHeimann LS2: flat-4 94.3% 5.7% 0.0% 30.6%

(Table 5 BCII series, matches incorporating only one index finger, 4-finger)

[2-FINGER (2,3)

 vs IAFIS rolled]

TAR 1-TAR

(FRR)

FAR filter

rate

BCII: rolled, mostly paper 97.3% 2.7% 1.9% 39.2%

Identix TP600/2000: rolled 97.8% 2.2% 1.7% 35.9%

CrossMatch 442: flat-4 90.9% 9.1% 1.2% 56.4%

SmithsHeimann LS2: flat-4 94.9% 5.1% 1.6% 48.5%

(Table 6 BCII series, matches incorporating only one index finger, 2-finger)

Performance penalties incurred when using plain-to-rolled identification

Tables 2 through 6 illustrate the other notable effect of the use of plain-to-rolled identification: a

very significant increase in the filter rate.

The ATB, like most AFISes, removes from consideration a large fraction of the subjects in its

database (the gallery) before embarking on the CPU-intensive task of feature-based matching.

The ATB refers to the percentage not removed as the ‘filter rate’. A filter rate of 2% means that

only 24,000 subjects from a gallery of 1,200,000 were actually ‘matched’ against a particular

probe. (We have found a correlation approaching 1 between the reported filter rate and the time

taken to conduct a search. Filter rate in effect is the inverse of throughput; high filter rates mean

slow performance.)

Viewing even the more optimistic (i.e., Smiths Heimann) case for plain fingerprints in Tables 2
Studies of Plain-to-Rolled Fingerprint Matching

Page 17 of 47

and 3 shows, in both ten and eight finger matching, that a change from rolled to plain

fingerprints carries with it very significant performance penalties of over 240% and almost

170%, respectivelyn

. Table 4 shows that the equivalent penalty for two finger matching is 37%.

Tables 5 and 6 show the penalties for a change from rolled to plain fingerprints for single-handed

four finger matching to be 110%, and for single-handed two finger matching to be 39%.

Although the relative penalty for the shift from rolled to plain is smaller for reduced numbers of

fingers, the absolute penalty is larger. In the case of reduced numbers of fingers, the baseline

(i.e., rolled images) has already incurred a substantial penalty of its own: 200% for the reduction

from ten to eight fingers, nearly 2200% for the reduction from ten to two fingers. This large

absolute penalty is also noted for single-handed four finger and two finger matching.

Differences between the livescan devices

Tables 2 through 6 show that in every case the images acquired by the CrossMatch had a rate of

failure to identify (1-TAR) that was between 60% and 100% higher than the rate for the images

acquired by the Smiths Heimann. The same tables also show a filter rate between 15% and 60%

higher for the CrossMatch. For these particular sets of images, the superiority for ATB matching

of the set from the Smiths Heimann device is clear. The underlying reason is not clear.

The Smiths Heimann uses a larger platen. Matching becomes harder (slower) as the amount of

data decreases, as shown by the degradation in throughput seen in tables 2 through 4 when the

number of fingers is reduced and when the area of the images is reduced (from rolled to plain).

Whether the difference in platen size causes the difference in accuracy and filter rate is uncertain.

Accuracy of the ATB as a model of IAFIS

Tables 7 and 8 compare the results of our ten and eight finger match experiments, using the ATB

and its built-in background database of approximately 1.2M subjects, with the results of similar

experiments conducted by the CJIS Division of the FBI, using the operational IAFIS with its live

database of approximately 42M subjects.

That the filter rates are quite similar is evident; the rates from the ATB are in every case slightly

smaller, averaging about 3.5% smaller than those from the IAFIS.

A similarity in failure rates is also evident; the rates from the ATB are in every case between 0%

and 50% greater than those from the IAFIS, averaging about 15% larger (although the magnitude

of the greatest difference is only 0.6%).

Thus the ATB with 1.2M subjects is a reasonable model of IAFIS with 42M subjects; the ATB’s

predictions of performance are a trifle optimistic, its predictions of accuracy a bit pessimistic.

 

n

 (e.g., (4.5 / 1.3) - 1 > 2.4)
Studies of Plain-to-Rolled Fingerprint Matching

Page 18 of 47

[10-FINGER using ATB] TAR 1-TAR filter

rate

Identix TP600/2000: rolled 97.9% 2.1% 1.3%

CrossMatch 442: flat-4 95.8% 4.2% 7.2%

SmithsHeimann LS2: flat-4 97.5% 2.5% 4.5%

[10-FINGER using IAFIS]

IAFIS: Identix 98.3% 1.7% 1.4%

IAFIS: CrossMatch 95.9% 4.1% 7.5%

IAFIS: SmithsHeimann 98.1% 1.9% 4.6%

(Table 7 BCII series, comparing ATB to IAFIS, 10-finger)

[8-FINGER (2-5,7-10) using ATB] TAR 1-TAR filter

rate

Identix TP600/2000: rolled 98.5% 1.5% 3.9%

CrossMatch 442: flat-4 96.5% 3.5% 15.5%

SmithsHeimann LS2: flat-4 98.2% 1.8% 10.5%

[8-FINGER using IAFIS]

IAFIS: Identix 99.0% 1.0% 4.0%

IAFIS: CrossMatch 96.5% 3.5% 16.1%

IAFIS: SmithsHeimann 98.6% 1.4% 10.9%

(Table 8 BCII series, comparing ATB to IAFIS, 8-finger)

Greater inherent accuracy of rolled-to-rolled(?)

Consider the TARs shown in tables 2 through 4. The mean TAR for rolled ten-finger prints,

which we shall call TARm(r,10) is 97.5 [dropping the ‘%’]; the variance TARv(r,10) is 0.8. The

mean TAR for plain ten-finger prints, TARm(p,10) is 96.65; the variance TARv(p,10) is 1.7.

We show these values for the ten-finger, eight-finger, and two-finger cases in table 9, where we

also show the difference between the means (Var[iance] of Means) and the average value of the

variances (Mean of Var[iance]s).

# fingers (n) TARm(r,n) TARv(r,n) TARm(p,n) TARv(p,n) Var of Means Mean of Vars

10 97.5 0.80 96.65 1.7 0.85 1.25

 8 98.2 0.60 97.35 1.7 0.85 1.15

 2 99.1 0.80 97.85 1.1 1.25 0.95

(Table 9 BCII series, comparing accuracy of rolled-to-rolled and plain-to-rolled)

We first note that in every case, given a number of fingers on which to match, the mean TAR for

the rolled-to-rolled class of matching is higher than the mean TAR for the plain-to-rolled class of

matching. That supports the hypothesis that the rolled-to-rolled class of matching is inherently

more accurate, but fails to prove it. We also note that in two of the three instances there is less

average variation between classes (Var of Means) than there is within class (Mean of Vars): the

differences are greater than can be explained solely by a rolled-to-rolled versus plain-to-rolled

classification. Even with extremely small classes (class-size = 2 for each class), the assertion

that rolled-to-rolled is inherently more accurate is not proven.
Studies of Plain-to-Rolled Fingerprint Matching

Page 19 of 47

5.2 Results from Group 2

Tables 10 through 15 below summarize the data obtained when matching the next three data sets,

2, 3, and 4. Two of the three types of matches shown (10- and 2-finger) are in nature directly

comparable to BCII matches. The third type of match (6-finger) is in nature roughly comparable

to the 8-finger BCII match.

[10-FINGER] TAR 1-TAR

(FRR)

FAR filter

rate

TXDPS: paper 90.4% 9.6% 0.0% 8.1%

INS10: livescan -> paper 82.1% 17.9% 0.0% 8.9%

INS_ben: livescan 89.1% 10.9% 0.0% 7.8%

(Table 10 TXDPS,INS10,INS_ben plain-to-rolled series, matches incorporating both index fingers, 10-finger)

[6-FINGER (2-4,7-9)] TAR 1-TAR

(FRR)

FAR filter

rate

TXDPS: paper 96.3% 3.7% 0.1% 12.9%

INS10: livescan -> paper 86.9% 13.1% 0.1% 15.2%

INS_ben: livescan 87.6% 12.4% 0.0% 17.8%

(Table 11 TXDPS,INS10,INS_ben plain-to-rolled series, matches incorporating both index fingers, 6-finger)

[6-FINGER (1-3,6-8)] TAR 1-TAR

(FRR)

FAR filter

rate

TXDPS: paper 92.1% 7.9% 0.0% 17.5%

INS10: livescan -> paper 84.5% 15.5% 0.1% 18.6%

INS_ben: livescan 90.5% 9.5% 0.0% 14.2%

(Table 12 TXDPS,INS10,INS_ben plain-to-rolled series, matches incorporating both index fingers, 6-finger)

[4-FINGER (2-3,7-8)] TAR 1-TAR

(FRR)

FAR filter

rate

TXDPS: paper 97.0% 3.0% 1.4% 21.8%

INS10: livescan -> paper 88.2% 11.8% 1.2% 24.7%

INS_ben: livescan 89.4% 10.6% 0.6% 26.8%

(Table 13 TXDPS,INS10,INS_ben plain-to-rolled series, matches incorporating both index fingers, 4-finger)

[4-FINGER (1-2,6-7)] TAR 1-TAR

(FRR)

FAR filter

rate

TXDPS: paper 92.0% 8.0% 0.4% 30.9%

INS10: livescan -> paper 83.7% 16.3% 0.4% 32.8%

INS_ben: livescan 90.5% 9.5% 0.3% 23.3%

(Table 14 TXDPS,INS10,INS_ben plain-to-rolled series, matches incorporating both index fingers, 4-finger)

[2-FINGER (2,7)] TAR 1-TAR

(FRR)

FAR filter

rate

TXDPS: paper 97.4% 2.6% 44.7% 38.1%

INS10: livescan -> paper 88.1% 11.9% 35.3% 42.8%

INS_ben: livescan 89.7% 10.3% 29.2% 44.4%

(Table 15 TXDPS,INS10,INS_ben plain-to-rolled series, matches incorporating both index fingers, 2-finger)
Studies of Plain-to-Rolled Fingerprint Matching

Page 20 of 47

The data sets forming group 2, sets 2, 3, and 4, are not homogeneous. The inhomogeneity is

more evident in accuracy (TAR) than it is in filter rate.

Although the TXDPS data exhibits an accuracy not far removed from that of the plain subsets of

group 1 (i.e., BCII subsets 1.3 and 1.4), both the INS10 and the INS_ben data exhibit markedly

lower accuracy. This is particularly evident in the 6-finger and 2-finger results.

However, it should be noted that the filter rates of sets 2, 3, and 4 are quite similar for 10-finger,

6-finger, and 2-finger results. It should also be noted that both the 6-finger and 2-finger filter

rates are comparable to the 8-finger and 2-finger rates for the plain subsets of group 1.

These dissimilarities aside, it should be noted that of the 18 available comparisonso

 of TAR(s,n)

with TAR(s,m<n), where s is set and n is finger count, in 16 cases TAR(s,n) < TAR(s,m). This is

the same phenomenon that was noted within group 1: TAR decreases as the number of fingers

increases.

The 4-finger statistics are, as one might expect, intermediate between the 2-finger and 6-finger

statistics. Although the group 2 statistics are not strictly equivalent to the 4-finger statistics from

group 1 (the 4-finger group 1 results were based on four fingers from a single hand), the values

roughly agree. Of interest is the comparison between tables 11 and 12, and also between 13 and

14. In both cases the use of the thumb (tables 12 and 14) produced a modest improvement for

INS_ben data, but an even greater degradation for both INS10 and TXDPS data.

Not all matchers had such mediocre results with sets 3 and 4 [4]. The qualities which cause data

to match well may vary from matcher to matcher.

Effect of gallery size on matcher accuracy

Other results from unpublished earlier studies involving the sets in group 2 support our

conclusion that TAR is largely independent of the size of the (seeded) gallery while supporting

the conclusion that FAR is a positive monotonic function of (unseeded) gallery size. These are

summarized below in tables 16 through 18.

gallery size

(seeded)

filter rate seconds

per probe

:(TAR) Φ(TAR

)

:(FAR) Φ(FAR

)

 1229 K 8.1% 2.03 90.4% 0.34 0.035% 0.020

 640 K 8.1% 1.13 90.5% 0.32 0.015% 0.020

 320 K 8.5% 0.53 90.6% 0.34 0.0050% 0.0081

 160 K 8.3% 0.27 90.6% 0.33 0.0017% 0.0053

 80 K 8.6% 0.17 90.6% 0.29 0.0017% 0.0053

(Table 16 effects of gallery size on matching accuracy and performance: TXDPS data)

 

o

 viewing the most conventional cases: 10-finger; 6-finger (2-4,7-9); 4-finger (2-3,7-8); and 2-finger (2-7)
Studies of Plain-to-Rolled Fingerprint Matching

Page 21 of 47

gallery size

(seeded)

filter rate seconds

per probe

:(TAR) Φ(TAR

)

:(FAR) Φ(FAR

)

 1223 K 8.9% 2.43 82.1% 0.69 0.042% 0.024

 634 K 8.8% 1.26 82.2% 0.67 0.025% 0.024

 314 K 9.1% 0.62 82.4% 0.64 0.015% 0.014

 154 K 9.2% 0.32 82.5% 0.61 0.0083% 0.0089

 74 K 9.8% 0.19 82.6% 0.66 0.0062% 0.0086

(Table 17 effects of gallery size on matching accuracy and performance: INS10 data)

gallery size

(seeded)

filter rate seconds

per probe

:(TAR) Φ(TAR

)

:(FAR) Φ(FAR

)

 1229 K 7.8% 1.93 89.1% 0.60 0.0050% 0.0081

 640 K 7.7% 0.94 89.4% 0.28 0.0034% 0.0071

 320 K 7.8% 0.49 89.6% 0.30 0.0017% 0.0053

 160 K 8.0% 0.26 89.8% 0.28 0.0000% 0.0000

 80 K 8.4% 0.16 90.0% 0.31 0.0000% 0.0000

(Table 18 effects of gallery size on matching accuracy and performance: INS_ben data)

When the total gallery size is 80K (74K), all but 20K comes from the seeded mates of the probe

set; at this point, the subject data set rather than the IAFIS Criminal Master File (CMF)

predominates in the gallery. We can thus ignore anomalies introduced by galleries of this size.

Figure 2 shows that the time required to process each probe is a linear function of the ‘effective

gallery size’, which is the product of the gallery size and the filter rate.

Figure 2 - search time as a function of effective gallery size

0

0.5

1

1.5

2

2.5

0 20 40 60 80 100 120

effective gallery size (thousands)

search time (seconds)

TXDPS

INS10

INS_ben
Studies of Plain-to-Rolled Fingerprint Matching

Page 22 of 47

Figure 3 shows that the rate of successful identification, TAR, is essentially independent of

gallery size.

Figure 3 - TAR as a function of gallery size

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

0 200 400 600 800 1000 1200 1400

gallery size (thousands)

TAR

TXDPS

INS10

INS_ben

Figure 4 shows that the rate of mistaking a subject not in the gallery for a subject in the gallery,

FAR, increases as the size of the unseeded composite gallery increases.

Figure 4 - FAR as a function of gallery size

0

0.00005

0.0001

0.00015

0.0002

0.00025

0.0003

0.00035

0.0004

0.00045

0.0005

0 200 400 600 800 1000 1200 1400

gallery size (thousands)

FAR

TXDPS

INS10

INS_ben
Studies of Plain-to-Rolled Fingerprint Matching

Page 23 of 47

5.3 Results from Group 3

Group 3 includes the Ohio BCII plain subsets and the large INS_plain and BCC sets

5.3.1 Results from group 3 - BCII plain subsets

We enrolled each of the five plain fingerprint subsets of set 1 (Ohio BCII data) in the empty

repository, resulting in an unconsolidated target with 4175 records but only 925 subjects. This

target physically was one unconsolidated gallery. We treated it logically as five disjoint, fully

consolidated galleries.

We used each plain subset as a probe to search against each of the other subsets as a gallery. To

accomplish this, we searched with each probe against the entire target, ignoring all match scores

that did not correspond to the desired gallery set. We conducted those searches in five different

modes: 10-finger, 8-finger, 2-finger (both index fingers), 4-finger (right hand only), and 2-finger

(right index and right middle). Only those subjects common to a probe-gallery combination

were considered in scoring and analyzing that probe-gallery combination.

Tables 19 through 23 show the results of the searches described above and in 4.3 (qv). The five

different search modes (10-, 8-, 2-, 4-, and 2-finger, as described above), are shown in the bottom

left entry of each table.

The mean values for FAR and TAR were computed by probe subset, defined as the mean FAR

(TAR) over all galleries other than the gallery that also constitutes the probe:

FARPi = 3 FAR(Pi,Gk i) / 4 and TARPi = 3 TAR(Pi,Gk i) / 4

Likewise, FAR and TAR means were computed by gallery subset:

FARGi = 3 FAR(Gi,Pk i) / 4 and TARGi = 3 TAR(Gi,Pk i) / 4

For each subset i, the mean of its two FARs and TARs were taken, and are shown in the line

labelled ‘probe,gal mean’: (FARPi + FARGi) / 2 and (TARPi + TARGi) / 2. These are accuracy

metrics for that subset (e.g., CrossMatch) for that class of matching (e.g., ten-finger).

The means of all such FAR and TAR are shown in the last line. They are overall accuracy

metrics for that class of matching. The aggregate filter rate is also shown in the last line of each

table. It is an inverted performance (throughput) metric for that class of matching: the higher the

value, the lower the throughput.

The plain-to-plain searches were conducted without the 1.2M IAFIS background, which consists

of rolled images. Although the TAR values thus obtained may fairly be compared to the TAR

values of the equivalent plain-to-rolled searches, there is no assurance that the FAR values are

also comparable: the gallery sizes differ by three orders of magnitude.
Studies of Plain-to-Rolled Fingerprint Matching

Page 24 of 47

Table 19 shows that in the aggregate, plain-to-plain ten-finger identification matching using the

ATB was substantially less accurate than plain-to-rolled matching (see table 2). The most

interesting value in table 19 is the mean TAR achieved, 90.3%, using the IAFIS master plain

subset as the gallery (because the IAFIS master rolled subset was used to seed the gallery in the

plain-to-rolled matching, we used the corresponding plain as the basis of comparison). The

resultant value for (1-TAR), 9.7%, is 2.9 times the mean, 3.35%, of the two plain-to-rolled (1-

TAR) values in table 2.

Table 19 also shows that plain-to-plain matching took 50% more resources than plain-to-rolled

matching. Table 2 shows a mean filter rate for plain-to-rolled matching of less than 6%,

whereas Table 19 shows a plain-to-plain filter rate of 9%.

However, when we look at the same data another way, we find that when we use only plain

fingerprint records that were originally built as plain fingerprint records (i.e., only CrossMatch

and Smiths Heimann records), we achieve a value for (1-TAR) of 1.7%. This represents a

reduction in error by more than half an order of magnitude, and is better than any of the plainto-rolled ten-finger results (cf table 2). [The results on the main diagonal (e.g., Identix:Identix)

are almost perfect. This has no real significance: it would be notable only if it were not true.]

At this point we simply haven’t enough data to explain this disparity. Although we can suggest

some likely causes, that would provide speculation rather than information. However, both the

magnitude of the disparity and its potential operational implications warrant further study.

GALLERY: BCII master Identix CrossMatch SmithsHeimann IAFIS master

TAR FAR TAR FAR TAR FAR TAR FAR TAR FAR

PROBE:

BCII master 100.0% 0.0% 88.8% 0.0% 89.8% 0.0% 91.1% 0.0% 87.7% 0.0%

Identix 88.3% 0.0% 100.0% 0.0% 89.9% 0.0% 91.2% 0.0% 88.5% 0.0%

CrossMatch 92.5% 0.0% 92.5% 0.0% 100.0% 0.0% 98.3% 0.0% 91.5% 0.0%

SmithsHeimann 93.8% 0.0% 93.9% 0.0% 98.3% 0.0% 99.9% 0.0% 93.3% 0.0%

IAFIS master 88.6% 0.0% 89.9% 0.0% 89.4% 0.0% 91.4% 0.0% 100.0% 0.0%

gallery mean 90.8% 0.0% 91.3% 0.0% 91.8% 0.0% 93.0% 0.0% 90.3% 0.0%

probe mean 89.4% 0.0% 89.5% 0.0% 93.7% 0.0% 94.8% 0.0% 89.8% 0.0%

probe,gal mean 90.1% 0.0% 90.4% 0.0% 92.8% 0.0% 93.9% 0.0% 90.0% 0.0%

fingers 1-5,6-10 TAR 91.4% FAR 0.0% filter 9%

(Table 19 Ohio BCII series, plain-to-plain matches incorporating both index fingers, 10-finger)

Another statistic of interest is the mean TAR for each livescan probe set (i.e., Identix,

CrossMatch, Smiths Heimann) for the card-based gallery sets (i.e., BCII master, IAFIS master)
Studies of Plain-to-Rolled Fingerprint Matching

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compared to the mean TAR of that same probe set for the livescan gallery sets. The former

means are, respectively, 88.4%, 92.0%, and 93.6%, whereas the latter are 90.6%, 95.4%, and

96.1%, an absolute improvement of 2.7%. (The results are more dramatic when we reverse the

role of probe and gallery; in this case the former means are, respectively, 89.4%, 89.6%, and

91.2%, whereas the latter are 93.2%, 94.1%, and 94.8%, an absolute improvement of nearly 4%.)

Accuracy of plain-to-rolled identification with fewer than ten fingers

Tables 20 and 21 show that plain-to-plain identification matching using fewer than 10 fingers

can also be quite accurate.

Note that the overall TAR shown in table 20, using eight fingers, was 93.9%. At first glance,

this is much inferior to the results shown in table 3. Just as with the ten-finger results shown in

table 19, when we use only plain fingerprint records that were originally built as plain fingerprint

records, we achieve a value for (1-TAR) of 1.3%. This is superior to any of the plain-to-rolled

ten-finger results. Here we are also struck by a marked asymmetry: using the Identix images in

the probe and either the CrossMatch or Smiths Heimann in the gallery, the (1-TAR) figures were

not particularly good; the mean was 5.9%. However, when the roles were reversed, there was a

dramatic improvement; the mean decreased to 1.8%. Again, further investigation is needed.

When we consider the two-finger results shown in table 21, we no longer see any asymmetry in

the Identix-to-CrossMatch or Identix-to-Smiths Heimann data. We do see that the best results

are superior to the best two-finger plain-to-rolled results (cf table 4), although in this case the

best TAR values, 99.7%, are provided by pairing Identix and Smiths Heimann images. As with

table 20, however, the overall TAR, 97%, is inferior to any of the two-finger plain-to-rolled

results (cf table 4). Although the FAR values appear to be remarkably improved, we have

already noted that the change in gallery size makes this apparent improvement signify little.

When we repeat for eight fingers the comparison of the mean TAR for each livescan probe set

for the card-based gallery set (i.e., BCII master, IAFIS master) to the mean TAR for the livescan

gallery set, we find that means of 91.2%, 94.7%, and 96.4% have increased to 94.1%, 98%, and

98.8%, an improvement of almost 3%. (Again, the results are more dramatic when we reverse

the role of probe and gallery; in this case the former means are, respectively, 92.2%, 90.9%, and

93.1%, whereas the latter are 98.2%, 96.2%, and 96.6%, an absolute improvement of nearly 5%.)

In the corresponding case for two fingers, the improvement is even more marked: from 96.8%,

95.1%, and 96.8% to 99.2%, 98.9%, and 99.4%, reducing the failure rate from 3.8% to 0.8%.

(Once again, the results are also dramatic when we reverse the role of probe and gallery; in this

case the former means are, respectively, 97.1%, 95.2%, and 96.8%, whereas the latter are 99.2%,

99.0%, and 99.4%, reducing the failure rate from 3.6% to 0.7%.)
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GALLERY: BCII master Identix CrossMatch SmithsHeimann IAFIS master

TAR FAR TAR FAR TAR FAR TAR FAR TAR FAR

PROBE:

BCII master 99.5% 0.0% 90.4% 0.0% 90.0% 0.0% 91.6% 0.0% 89.0% 0.0%

Identix 90.4% 0.0% 99.8% 0.0% 93.6% 0.0% 94.6% 0.0% 92.1% 0.0%

CrossMatch 94.3% 0.0% 97.5% 0.0% 99.9% 0.0% 98.6% 0.0% 95.1% 0.0%

SmithsHeimann 95.7% 0.0% 98.9% 0.0% 98.8% 0.0% 99.7% 0.0% 97.2% 0.0%

IAFIS master 90.1% 0.0% 93.9% 0.0% 91.8% 0.0% 94.6% 0.0% 99.7% 0.0%

gallery mean 92.6% 0.0% 95.2% 0.0% 93.5% 0.0% 94.8% 0.0% 93.3% 0.0%

probe mean 90.3% 0.0% 92.7% 0.0% 96.3% 0.0% 97.7% 0.0% 92.6% 0.0%

probe,gal mean 91.4% 0.0% 93.9% 0.0% 94.9% 0.0% 96.2% 0.0% 93.0% 0.0%

fingers 2-5,7-10 TAR 93.9% FAR 0.0% filter 15%

(Table 20 Ohio BCII series, matches plain-to-plain incorporating both index fingers, 8-finger)

GALLERY: BCII master Identix CrossMatch SmithsHeimann IAFIS master

TAR FAR TAR FAR TAR FAR TAR FAR TAR FAR

PROBE:

BCII master 100.0% 0.0% 96.8% 0.2% 95.2% 0.0% 96.2% 0.3% 94.5% 0.0%

Identix 96.6% 0.0% 99.9% 0.1% 98.8% 0.0% 99.7% 0.1% 97.0% 0.3%

CrossMatch 95.1% 0.0% 98.8% 0.0% 99.8% 0.1% 99.0% 0.1% 95.1% 0.0%

SmithsHeimann 96.3% 0.0% 99.7% 0.1% 99.2% 0.2% 99.8% 0.2% 97.4% 0.3%

IAFIS master 94.3% 0.0% 97.4% 0.1% 95.1% 0.1% 97.5% 0.3% 99.9% 0.1%

gallery mean 95.6% 0.0% 98.2% 0.1% 97.1% 0.1% 98.1% 0.2% 96.0% 0.1%

probe mean 95.7% 0.1% 98.0% 0.1% 97.0% 0.0% 98.2% 0.1% 96.1% 0.1%

probe,gal mean 95.6% 0.1% 98.1% 0.1% 97.0% 0.1% 98.1% 0.2% 96.0% 0.1%

fingers 2,7 TAR 97.0% FAR 0.1% filter 46%

(Table 21 Ohio BCII series, plain-to-plain matches incorporating both index fingers, 2-finger)

As tables 22 and 23 demonstrate, failure to use both index fingers results in a significant drop in

accuracy, a pattern previously evident in tables 5 and 6.
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GALLERY: BCII master Identix CrossMatch SmithsHeimann IAFIS master

TAR FAR TAR FAR TAR FAR TAR FAR TAR FAR

PROBE:

BCII master 99.8% 0.0% 91.1% 0.0% 86.6% 0.0% 91.3% 0.0% 84.0% 0.0%

Identix 90.4% 0.0% 100.0% 0.0% 94.0% 0.0% 97.8% 0.0% 87.3% 0.0%

CrossMatch 86.8% 0.0% 95.5% 0.0% 99.6% 0.0% 95.8% 0.0% 84.3% 0.0%

SmithsHeimann 91.0% 0.0% 98.7% 0.0% 95.2% 0.0% 100.0% 0.0% 89.5% 0.0%

IAFIS master 84.2% 0.0% 88.9% 0.0% 85.0% 0.0% 88.5% 0.0% 99.9% 0.0%

gallery mean 88.1% 0.0% 93.5% 0.0% 90.2% 0.0% 93.3% 0.0% 86.3% 0.0%

probe mean 88.3% 0.0% 92.4% 0.0% 90.6% 0.0% 93.6% 0.0% 86.6% 0.0%

probe,gal mean 88.2% 0.0% 93.0% 0.0% 90.4% 0.0% 93.5% 0.0% 86.5% 0.0%

fingers 2,3,4,5 TAR 90.3% FAR 0.0% Filter 38%

(Table 22 Ohio BCII series, plain-to-plain matches incorporating one index finger, 4-finger)

GALLERY: BCII master Identix CrossMatch SmithsHeimann IAFIS master

TAR FAR TAR FAR TAR FAR TAR FAR TAR FAR

PROBE:

BCII master 100.0% 0.0% 92.5% 0.3% 87.8% 0.0% 91.8% 0.2% 85.7% 0.2%

Identix 92.1% 0.0% 100.0% 0.0% 95.0% 0.0% 98.9% 0.1% 88.7% 0.1%

CrossMatch 87.0% 0.0% 95.1% 0.0% 99.6% 0.0% 96.1% 0.0% 84.7% 0.0%

SmithsHeimann 90.6% 0.2% 98.5% 0.2% 95.2% 0.1% 99.7% 0.3% 89.3% 0.1%

IAFIS master 85.0% 0.4% 90.0% 0.0% 85.7% 0.1% 90.3% 0.1% 99.9% 0.1%

gallery mean 88.7% 0.1% 94.0% 0.1% 90.9% 0.1% 94.3% 0.1% 87.1% 0.1%

probe mean 89.4% 0.2% 93.7% 0.1% 90.7% 0.0% 93.4% 0.2% 87.7% 0.2%

probe,gal mean 89.0% 0.1% 93.9% 0.1% 90.8% 0.0% 93.8% 0.1% 87.4% 0.1%

fingers 2,3 TAR 91.0% FAR 0.1% Filter 54%

(Table 23 Ohio BCII series, plain-to-plain matches incorporating one index finger, 2-finger)

Performance penalties incurred when using plain-to-plain identification

Tables 19 through 21 (above) illustrate that the use of plain-to-plain matching for identification

causes, just as did the use of plain-to-rolled matching, a very significant increase in the filter rate.

This can most readily be seen by examining the filter rates for these tables: 9%, 15%, and 46%,

which are marginally higher than the mean plain-to-rolled filter rates of 6%, 13%, and 46%

shown in tables 2 through 4.
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5.3.2 Results from group 3 - large sets of two-finger plain

Tables 24 and 25 below summarize the data obtained when matching the last two data sets, 5 and

6. The results can most instructively be compared to those in table 21. It is also useful to

compare them to the plain-to-rolled results in table 15.

The filter rates of 45% to 49% agree quite closely with the 46% obtained in table 21, and are not

far removed from the 38% to 44% reported in table 15. The TARs, around 94% in both tables 24

and 25, are somewhat inferior to all of those in table 21, and bracketed by the 88% to 97%

reported in table 15.

There is rough agreement; that it is rough should not surprise us, given the disparate nature of the

data sources.

Note that these two tables are instructive in themselves. First we observe that the combination of

the INS_plain and BCC data in the 574K gallery introduced no anomalies; the results are

reasonable, and what one might expect from increasing the size of either gallery with more

images from its own source. Next we observe that, as expected, FAR does increase with gallery

size. Third, we note in table 25 that use of a non-random probe set can skew the results:

compare the FAR of 4.3% in line 1 (60K probe selected at random from all 274K subjects) to

the FAR of 5.8% in line 2 (60K probe consisting of the first 60K subjects).

probe / gallery filter rate seconds

per probe

:(TAR) Φ(TAR

)

:(FAR) Φ(FAR)

1st 60K / 60K INS_plain 48.7% <1 94.4% 0.34 0.74% 0.094

1st 60K / 574K combined 48.1% 4.04 94.0% 0.24 9.88% 0.424

(Table 24 effects of gallery size & composition on matching accuracy and performance: INS_plain data)

probe / gallery filter rate seconds

per probe

:(TAR) Φ(TAR

)

:(FAR) Φ(FAR

)

random 60K / 274K BCC 44.8% 1.64 94.0% 0.21 4.3% 0.23

 1st 60K / 274K BCC 44.8% 1.64 93.9% 0.27 5.8% 0.31

 1st 60K / 574K combined 45.8% 3.79 93.9% 0.28 8.0% 0.34

(Table 25 effects of gallery size & composition on matching accuracy and performance: BCC data)

5.4 The Choice of Fingers

The ATB offers important choices. Whether to match using plain or rolled images is one. How

many fingers to use is another. The selection of which fingers to use is a third.
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The ATB, like IAFIS, has a strong preference for index fingers.

Tables 5 and 6 illustrate this, the former very dramatically. Table 5, with four fingers (2,3,4,5)

available, shows a failure rate (1-TAR) between 2.4 and 3.5 times as great as table 4, with only

two fingers (2,7) available; however, it has a noticeably better filter rate. Table 6, with the same

number of fingers as table 4 but with only one index finger (2,3), shows failure rates similar to

table 5, with filter rates only moderately poorer than table 4.

This preference is also evident in plain-to-plain matching. Tables 22 and 23, when compared to

tables 20 and 21, show a marked drop in accuracy when only one index finger is used. This most

evident when comparing 21 and 23: the overall failure rate (1-TAR) for two-finger matches is

3% in the former case, 9% in the latter.

This supports the view of the ATB (and IAFIS) as an index finger matcher preceded by a ten

finger filter.

A matter for investigation is whether this preference for index fingers is a property of fingers or

of the ATB: how well would the ATB perform if it were presented with finger 2 images in the

finger 2 position but with finger 3 images in the finger 7 position (i.e., if we were to mislead it)?
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6. INTERPRETATION / SUMMARY / CONCLUSIONS

We asked nine questions. We have answered all of them:

(1) how accurate is plain-to-rolled identification? is it less accurate than rolled-to-rolled?

Failure to identify (1-TAR) can be 2% or less. Accuracy can be as good as with rolled prints

of average quality.

(2) how accurate is plain-to-rolled identification with fewer than ten fingers?

Accuracy with fewer than ten fingers can exceed accuracy with ten fingers, but performance

penalties can be expected (see next question).

(3) are performance penalties incurred by adopting plain-to-rolled identification? if

penalties are incurred, how great are they?

Performance (throughput) deteriorates markedly as the amount of data is reduced. Using

plain rather than rolled fingerprints in 10-finger or 8-finger matching can be expected to

result in a three-fold reduction in throughput. Reducing the number of fingers from ten to

two can be expected to reduce throughput by a factor of ten or more. These effects are

independent of one another, and thus multiplicative.

(4) are there significant differences in accuracy and performance between the fingerprint

images supplied by two different models of livescan devices? if there are differences, how

great are they?

There are significant differences in both matching accuracy and performance. In this case,

the inferior set of images showed a rate of failure to identify (1-TAR) 68% to 94% higher

than that shown by the superior, and a filter rate 24% to 60% higher.

(5) does gallery size affect accuracy? if there is an effect, how can it be characterized?

Within the range of gallery sizes covered by these studies (160,000 to 42,000,000), gallery

size did not affect overall accuracy (TAR).

(6) does the ATB accurately model the behavior of IAFIS?

In a word, yes.

(7) how accurate is plain-to-plain identification?

Failure to identify (1-TAR) can be 2% or less. There is more variability than evident with

rolled-to-plain.

(8) how accurate is plain-to-plain identification with fewer than ten fingers?

As with rolled-to-plain, accuracy with fewer than ten fingers can exceed accuracy with ten

fingers, with a failure rate of well under 1%. There is more variability than evident with

rolled-to-plain, and performance penalties can be expected.
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(9) are performance penalties incurred by adopting plain-to-plain identification?

Yes. Filter rates can approach 50%, which means matching every other subject. (Compared

to the 2% filter rate typical of rolled-to-rolled tenprint matching, a 50% rate implies a 25-fold

increase in matching workload.)

We were able to draw other conclusions as well:

There were clear differences, but the interclass difference (between rolled and plain) was

smaller than the intraclass differences (between BCII and Identix and between CrossMatch and

Smiths Heimann).

There were similarities among all the sets; the invariance of TAR over gallery size and the

essentially linear relationship of FAR to gallery size were both notable.

The ATB works most accurately (i.e., has the lowest failure rate, or (1-TAR)) with two index

fingers. The addition of more fingers raises its throughput markedly but with a concomitant

reduction in TAR. The use of six or more fingers reduces the FAR to almost zero.
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7. FUTURE INVESTIGATION

The major topics to be covered in this study have been addressed.

There are details that can be added.

* We intend to develop best-fit and best-fit-linear equations for TAR and filter-rate

* We intend to investigate behavior when 2,3 is treated as if it were 2,7

However, the nature of the qualities in fingerprint images needed for consistently successful

matching with two plain index fingers requires further investigation.
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REFERENCES

 (1) Public Law 107-56 (USA PATRIOT ACT); 107th United States Congress, Washington,

D.C.; 26 October 2001

(2) Public Law 107-173 (Enhanced Border Security and Visa Entry Reform Act of 2002);

107th United States Congress, Washington, D.C.; 14 May 2002

(3) M D Garris, C I Watson, R M McCabe, C L Wilson; “User’s Guide to NIST Fingerprint

Image Software (NFIS)” NISTIR 6813; National Institute of Standards and Technology,

Gaithersburg, Maryland; August 2001

(4) C L Wilson, C I Watson, M D Garris, A Hicklin; “Studies of Fingerprint Matching Using

the NIST Verification Test Bed (VTB)” NISTIR 7020; National Institute of Standards and

Technology, Gaithersburg, Maryland; 07 July 2003

 (5) Federal Bureau of Investigation; “The Science of Fingerprints: Classification and Uses”;

U.S. Government Printing Office, Washington D.C.; 1984.

 (6) “NIST Algorithm Test Bed (ATB) User Manual”; Lockheed Martin Corporation,

Orlando, Florida; 12 September 2002

 (7) Daubert v. Merrell Dow Pharmaceuticals (92-102); United States Supreme Court,

Washington, D.C.; 28 June 1993; <http://supct/law/cornell.edu/supct/html/92-102.ZS.html>

(as of 20040123)

(8) Kumho Tire Co. v. Carmichael (97-1709); United States Supreme Court, Washington,

D.C.; 23 March 1999; <http://supct/law/cornell.edu/supct/html/97-1709.ZS.html> (as of

20040123)

(9) J L Wayman; “When Bad Science Leads to Good Law: the Disturbing Irony of the

Daubert Hearing in the Case of U.S. v Byron C. Mitchell”; San Jose State University, San Jose,

California; 02 February 2000; <http://www.engr.sjsu.edu/biometrics/publications_daubert.html>

(as of 20040123)

(10) S A Rizvi, P J Phillips, H Moon; “The FERET Verification Testing Protocol for Face

Recognition Algorithms” NISTIR 6281; National Institute of Standards and Technology,

Gaithersburg, Maryland; October 1998

(11) J D Murray, W vanRyper; “Encyclopedia of Graphics File Formats”; O’Reilly and

Associates, Inc., Sebastopol, California; 1994

(12) “Electronic Fingerprint Transmission Specification” CJIS-RS-0010 (V7); Criminal
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Justice Information Services Division, Federal Bureau of Investigation, Washington, D.C.; 29

January 1999

(13) R M McCabe; "Data Format for the Interchange of Fingerprint, Facial, Scar Mark &

Tattoo (SMT) Information"; American National Standard ANSI/NIST-ITL 1-2000; National

Institute of Standards and Technology , Gaithersburg, MD; July 2000

(14) "National WebCheck Program Pilot Project Preliminary Report"; Ohio Attorney General

Bureau of Criminal Identification and Investigation; London, Ohio; December 2003
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 APPENDIX A:

ATB SETTINGS

The settable search characteristics used were all default values:

TP CMF Caxi Filter Rate: 0.01

TP CMF ARG Low Threshold: 2000

TP CMF ARG High Threshold: 16000

TP CMF ARG Final Threshold: 3000

TP CMF Filtering Threshold: 1.1216

The settable quality characteristics used were all default values except as noted:

Missing Finger Threshold: 9

Criminal TP Search Reject Threshold: 81 [from LM; default was 69]

In addition, LM stated that it had made the following modifications (to improve usability in the

NIST environment) before delivering the ATB, none affecting match scores and only one

potentially affecting performance (throughput):

• Disabled FNU validation – this allows subjects to be seeded into the repository without

having to meet the normal FNU naming conventions

• Disabled loading of Civil, SLC and ULF repositories – this allows the system to run the

AFIS Segmentp

 Control and Processing Server functions on a single server (reduces the

amount of memory consumed)

• Increased the number of TP search candidates passed back to Segment Control to 30 – by

setting the tunable ARG Final threshold to 0, this enables NIST to receive the top 30 TP

candidates versus just the candidates the AFIS segment would normally nominate.

• Disabled automatic cascading of 1 to 1 Verification to TP search – normally if a 1 to 1

Verification submission does not result in the provided FNU matching the provided

imagery, a normal TP search is automatically performed. This was disabled so that just

the 1 to 1 Verification could be analyzed.

• Added the capability to disable RRI filtering – an environment variable was added that

will allow the AFIS segment to perform TP/TP searches without the RRI filter. The

normal AFIS segment does not provide a mechanism to disable this filter.

 

p

 in Appendix A, a segment is a portion of IAFIS; the three major segments are known as AFIS, III, and ITN
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APPENDIX B:

POSSIBLE BIASES: DATA SET 1 (BCII)

(A) Only those subjects (1) who were incarcerated, and (2) for whom useable fingerprint images

were on file, were used to provide data for this study. Although it is not known whether

condition (2) was a selection criterion, condition (1) was necessarily a criterion. If one makes

the reasonable assumption that criminals who have ‘good quality’ (for our purposes, this means

matchable using existing AFIS technology) fingerprints are more likely to be apprehended and

thus incarcerated than criminals who have ‘poor quality’ fingerprints, then there is some bias in

this study: it is a measure of the most effective way to keep on apprehending those whom we

now apprehend. This is not necessarily an unwanted result.

The reasonableness of the assumption can be tested by denying it; to deny it would be to deny

that the operation of IAFIS aids in the apprehension and incarceration of criminals.

(B) Because the BCII data were collected for a special study, the data may not be truly

representative of operational data with all its warts and blemishes.

(C) The criteria used for selecting the subjects from the available incarcerated prisoners are not

known to the NIST investigators.

(D) If TAR is dependent on the size of the gallery C(G), then the results might not extrapolate

well from the ATB to the gallery(ies) of the actual application(s). However, tables 6 and 7

showed that any such effects in this case would be small.

(E) There was no evident randomization of the order in which a given subject was fingerprinted

on a given type of livescan machine (Identix, CrossMatch, Smiths Heimann). It is not

inconceivable that subjects tended to become {more | less} cooperative with each subsequent

fingerprinting.

(F) The world of operational fingerprint identification is largely concerned with matching a

subject’s recent images with those acquired at some time in the past. Should there be a shortterm (short-lived) component of matchable features (e.g., caused by nicks and scrapes), that

component might add a bias favoring images acquired at nearly the same time.

From a cursory inspection of the ‘Date Printed’ [2.038] and ‘Date’ [1.05] fields in each record,

the Smiths Heimann samples were obtained on July 1, 2003, whereas both the Identix and

CrossMatch samples were obtained on June 25-30, 2003. However, it is also evident that the

data in the records is not pristine, so it is reasonable that the dates may not accurately reflect the

date when the subject was printed. (The protocol used to create and process the samples has not

been requested from the Ohio BCII.)
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POSSIBLE BIASES: DATA SETS 2, 3, 4, 5, 6

(A) The data sets used excluded records for which automatic segmentation failed to extract eight

plain fingerprint images. This in turn may have tended to exclude subjects whose fingerprint

images are not distinct.

(B) The automatic classification of potential consolidations as confirmed consolidations (and

their subsequent removal) may have reduced the frequency of false matches.
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APPENDIX C:

WHY TWO FINGERS ARE ‘BETTER’ THAN TEN (and why ten fingers are ‘better’ than two)

and

WHY PLAIN ARE ‘BETTER’ THAN ROLLED (and why rolled fingers are ‘better’ than plain)

Comparing tables 1 and 3, one is immediately struck by the fact there are in the aggregate almost

twice as many failures to identify with ten fingers as there are with only two. For each of the

four data sets with ten fingers available (i.e., 1, 2, 3, and 4) in these studies, accuracy (TAR) with

two fingers is higher than with ten.

Both the explanation for this phenomenon and the rationale for using ten fingers can be found in

the filter rate. The BCII filter rate was 1.3% for ten fingers, 29.8% for two. Viewed another

way, the ATB performed (925 * 0.013 * 1.2M ≅) 15M detailed matches to obtain a correct

identification of 898 subjects (and no misidentifications) when using ten fingers, and performed

(925 * 0.298 * 1.2M ≅) 330M detailed matches to obtain a correct identification of 913 subjects

(and 11 misidentifications) when using two fingers. Using ten fingers, it incorrectly filtered

(screened) out 27 mates in the process of avoiding 1095M matching operations, but made no

misidentifications. Using two fingers, it incorrectly filtered out only 1 mate in the process of

avoiding 780M matching operations, but made 12 misidentifications. Using two fingers, it used

22 times as many computer matching operations to increase the number of identifications by less

than 2%.

A comparison of tables 9 and 11 reveals that the same phenomenon is exhibited by the other data

sets.

Filtering, as performed on the ATB, uses some gross information (characterization) about all

available fingers to reduce the number of detailed matches performed, and thus maintain the

required throughput.

There are some direct consequences of this mode of operation of the ATB.

Plain images are smaller (ceteris paribus) than rolled images, and smaller images contain less

information (cet. par.) than larger images. The less information presented to the characterization

process, the fewer mates (incorrectly) filtered out before matching. Thus:

Provided that the fingerprints remain of sufficiently high quality (i.e., high ‘matchability’ by

the matcher in question), the use of a reduced number of plain impressions rather than ten rolled

impressions can be expected to result in a modest increase in the number of correct

identifications (TAR) coupled with a dramatic increase in the time required to process each

subject. Such a shift in the operating point of the system would require more resources to sustain

the same throughput.
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APPENDIX D:

ANALYSIS OF SOURCE MEDIUM OF IAFIS IMAGES

In an attempt to quantify the statement “inked images predominated”, 100 records – properly,

sets of images – of the 925 supplied from the FBI CMF were examined.

There are three caveats. First, the 925 records supplied by the FBI were not a random selection

from the IAFIS CMF, but rather the CMF records for the subjects in the BCII set. Second, the

100 were not a random selection from the 925, but rather the first 100 in file name order (i.e.,

#100656MA0 through #180862RB8). Third, a set of 100 is rather small for the generation of

precise statistics.

Visual examination showed that the 100 records came from the following sources:

50 cards, definitely inked: determination was made as follows:

 2 reversal of ridge & valley due to very high pressure squeezing out ink

20 improper registration of images

28 overlap of images (includes 1 also counted as a reversal)

 1 adhesive patch (covering up previous image)

14 cards, probably inked - cards with nothing conclusive to indicate that they were

not inked [note that #144905AC5 might well have been a rescan, and that

#142811CA4 seemed to be composed of images from different cards]

19 cards, printed from livescan, then rescanned

12 Digital Biometrics, Inc (DBI) (of which 10 showed evidence of a separate

placement of finger 10)

 7 Identix

17 livescan [note that images 2, 6, 11, & 12 of 130610WB8 were quite poor: artifacts

from WSQ compression might be present]

Although one might argue that, knowing nothing else, the 14 should be allocated to ink and

printed from livescan in proportion to the ratio of the 69 cards already so classified ‘definitely

inked’ : ‘printed from livescan’ (that is, 10 inked, 4 printed from livescan), in fact all but one of

the 14 presented the appearance of inked cards.

One may reasonably conclude that over 60% of the records came from inked cards, somewhat

less than 20% came directly from livescan records, and about 20% came from rescanning cards

that had been printed from livescan records.

Only one (#142811CA4) of the 100 records appeared to contain images from different sources;

in the records from the Ohio CMF the presence of images from different sources was much more

frequent.
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APPENDIX E:

CONSOLIDATION

Fingerprint matchers are a subset of biometric matchers. Much of the analysis of biometric

matchers employs generally accepted methodologies and terminologies, which are well

described in the existing literature [4,10]. A necessary component of these is the concept of

consolidation: bringing together into one record all images pertaining to a given subject. In the

case of a system (such as the IAFIS or the ATB) which allows only one [active] record for each

subject, that means purging all aditional records.

Consolidation is necessary to remove ambiguity and error from the process of scoring the

matcher’s accuracy.

The frequency of consolidations (of subjects with more than one record) in operational

fingerprint databases is high enough to render almost meaningless measures of accuracy (e.g.,

FAR and TAR) unless the consolidations are identified and eliminated. The methodology for

performing the elimination is not as important as its result: removing too many records is

preferable to removing too few. The methods we employed to eliminate consolidations in the

three sets of data forming group 2 are illustrative of some of the techiniques that can be used.

To address the problem of consolidation in the TXDPS data (set 2), we first seeded the

background gallery with the rolled images of the gallery subset. We then created a virtual probe

set from the gallery subset (by telling the ATB to treat the gallery subset as a probe set). Finally,

we searched the entire seeded gallery with the virtual probe set. In effect, we probed both the

original background portion of the gallery and the seeded portion of the gallery with the rolled

images of the seeded gallery itself; any match was potentially an additional record of the subject

from the seeded gallery set.

Ignoring matches of images to themselves, there were four possible classes of matches: probe set

to seeded portion of gallery set (high score and medium score), and probe set to background

portion of gallery set (high score and medium score).

We resolved both classes of probe set to seeded portion of gallery set matches by visually

comparing the images; all of the high scores and most of the medium scores were in fact

consolidations (the ATB does not report low scores when in normal operating mode).

The FBI resolved both classes of probe set to background gallery matches for the TXDPS data:

we sent our images together with the FBI number of the reported match to the IAFIS facility in

Clarksburg, WV. (We could not compare the images ourselves: we do not have the images of

the 1.2M records in the background gallery, but rather only the feature vectors.) The FBI

reported that all of the high scores were in fact consolidations, and also reported which of the

medium scores were (or were not) consolidations.
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For the remaining two sets, INS10 and INS_ben, we treated as a consolidation any high or

medium score match to the background gallery. We were able to do so because of the proven

reliability of the matcher itself as a detector of probable consolidations; we did so to avoid the

additional time that would have been required by manual resolution.

We removed all records representing consolidations with the background gallery from further

consideration. In data set 2, we allowed the record with the lowest ID number in a group of

consolidated records in the gallery set to remain. Because we incrementally built and

consolidated data sets 3 and 4, this process would have presented too much opportunity for error

when adding more records to the set. Instead, in those two sets we removed from consideration

all records associated with a consolidation.
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APPENDIX F:

GLOSSARY

We assume in defining ‘false accept’, ‘false reject’, ‘true accept’, and ‘true reject’ that both

query and target sets are fully consolidated.

AN2K American National Standard ANSI/NIST-ITL 1-2000; a standard for the

exchange of biometric data (cf EFTS) [13]

characterize, characterization – see feature vector

consolidation for each subject in the [target | gallery | query | probe] set, the

identification and elimination from that set of any additional fingerprint

records for that subject; also, any such additional record; also, the very

existence of any such additional record; see Appendix E

efts, EFTS an efts record is a record compliant with the FBI’s Electronic Fingerprint

Transmission Specification [12]; EFTS is somewhat more restrictive than

AN2K, so that an EFTS-compliant record is almost always compliant with

AN2K; the ATB accepts efts records, but allows the omission of some

mandatory fields [6]

enrollment the entry of a subject’s fingerprint record into the target set

false accept the nomination as a match a subject that is not the mate; the false accept

rate (FAR) is the rate w.r.t. number of probes of ‘false accept’ responses;

false accept is defined only when the probe is an impostor

FAR – see false accept

false reject the failure to nominate as a match the subject that is the mate; the false

reject rate (FRR) is the rate w.r.t. number of probes of ‘false reject’

responses; false reject is defined only when the probe has a mate

FBI number a six to nine character alphanumeric identifier assigned by the FBI to

identify a subject; it is used (when known) as a definitive identifier by

most law enforcement agencies in the United States; the existence of two

records within IAFIS, with different FBI numbers but representing the

same subject, would constitute a consolidation (qv)

feature vector the ATB’s extract of the matching information of the subject’s images; the

ATB denotes the process of extraction as ‘characterization’
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[to ] filter [verb] to create a subset of the original gallery with the goal of restricting

matching to this subset, thus reducing the number of actual matches to be

performed

finger position a number used to denote a particular finger by its position on a tenprint

card; 1 - right thumb, 2 - right index, ..., 6 - left thumb, ..., 10 - left little

fingerprint record a set of fingerprint images of a single subject captured at a single session

(e.g., the images on a tenprint card)

FRR – see false reject

gallery [set] a set of fingerprint records of known subjects against which a search is

conducted; a consolidated gallery is a gallery with exactly one record per

subject; a gallery set is a (possibly improper) subset of the target set [10]

identification the determination of which subject (if any) in the gallery corresponds to

the current subject of interest in the probe; also referred to as 1-to-n or

one-to-many matching

impostor a probe that has no mate in the gallery

impression an image, whether using ink or a ‘livescan’ optical device, of a finger; a

plain (or ‘flat’) impression is made by pressing the bottom surface of the

terminal phalange of the finger or thumb against a flat surface, whereas a

rolled impression is made by rotating the finger or thumb around its

longitudinal axis without slip while causing the same two surfaces to bear

match an indication from the matching system that a particular subject within the

gallery bears a notable similarity to the subject in question from the probe;

also, the particular subject in the gallery

match score a numeric score showing the degree of similarity in a match; generally

presumed to be a non-negative increasing monotonic function

mate the subject in the gallery corresponding to the subject in the probe (or vice

versa)

probe [set] a set of fingerprint records of unknown subjects to be identified or verified

that is used in performing a search; a consolidated probe is a probe with

exactly one record per subject; a probe set is a (possibly improper) subset

of the query set [10]
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query [set] the set of fingerprint records of unknown subjects to be identified or

verified; a consolidated query is a query with exactly one record per

subject [10]

rank-1 match the match with the score showing the greatest degree of similarity when

more than one match is reported for a member of the probe set; otherwise,

the match, if any [in analyzing the ATB results, the rank-1 match was

treated as the only reported match]

registration the placement of a tenprint card on a scanner such that the fingerprint

image areas on the card physically correspond to the scanner’s construct

of them

repository – see target set

[to] seed [verb] given a gallery set consolidated with respect to the probe set (that

is, with (presumably) no mate to any subject in the probe set), to enroll

into the gallery exactly one mate for each subject in the probe

segmentation the resolution of a multi-finger (e.g., slap-4) image into its constituent

fingerprint images

slap-4 a set of four fingerprints – the four fingers of either hand – taken

simultaneously; necessarily these are plain impressions rather than rolled

submission the presentation to the ATB of a probe; the images so submitted

target [set] the set of fingerprint records of known subjects; a consolidated target is a

target with exactly one record per subject; to the ATB, the target is also

known as the repository [6][10]

tenprint card a card, such as FBI forms FD-249 or FD-258, intended to capture a

fingerprint record; the exemplars have locations for fourteen images: ten

for a rolled impression of each digit, two for a plain impression of each

thumb, and two for a plain impression of the four fingers (taken

simultaneously) of each hand; the latter two are referred to as slap-4

images

true accept the nomination as a match a subject that is the mate; the true accept rate

(TAR) is the rate w.r.t. number of probes of ‘true accept’ responses; true

accept is defined only when the probe has a mate
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TAR – see true accept

true reject the nomination of no subject as a match when in fact there truly is no

mate; the true reject rate (TRR) is the rate w.r.t. number of probes of ‘true

reject’ responses; true reject is defined only when the probe is an impostor

TRR – see true reject

verification the process of testing whether the query image is a mate of the target

image; also referred to as one-to-one matching; note that the ATB’s design

emphasizes its primary role as an identification (vs. verification) matcher
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APPENDIX G:

NOTATION in black and white

P probe set (any) {pi}

PI probe set (impostor) {pi

: ∀pi ∈ PI (!∃gk ∈ G ∋ pi M gk)}

in English: there is no mate

PM probe set (mated) {pi: ∀pi ∈ PM (∃gk ∈ G ∋ pi M gk)}

in English: there is a mate

M ate: (pi M gk) iff ∃ a subject sj ∋ both pi and gk are

images of fingerprints taken from sj

[thus M is transitive and commutative]

R eported Match:(pi R gk) iff matcher reports some gk as the rank-1 match

for pi

G gallery set (any) {gi}

GC gallery set (consolidated) {gi: ∀gi ∈ GC

 (!∃gk i ∈ GC ∋ gi M gk)}

or {gi: ∀gi ∈ GC

 (∀gk i ∈ GC !(gi M gk))}

in English: there is no mate within the gallery

From the definitions of PM, GC, and M:

∀pi ∈ PM (C {gk ∈ GC ∋ pi M gk} = 1)

in English: each member of the mated probe set has exactly 1 mate in the consolidated gallery

PI ∪ PM = P

PI ∩ PM

 = Μ

FAR, FRR, TAR, TRR:

False Accept: (pi R gk) ∧ !(pi M gk) ∧ pi ∈ PI

False Reject: !(pi R gk) ∧ (pi M gk) ∧ pi ∈ PM

True Accept: (pi R gk) ∧ (pi M gk) ∧ pi ∈ PM

True Reject: !(pi R gk) ∧ !(pi M gk) ∧ pi ∈ PI

FAR = C {False Accept} / C (PI)
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FRR = C {False Reject} / C (PM)

TAR = C {True Accept} / C (PM)

TRR = C {True Reject} / C (PI

)

Note that for each probe there is exactly one result: False Accept  False Reject  True

Accept  True Reject. However, each probe is in exactly one of PI or PM.

For those in PI, only FAR and TRR are possible (defined). Similarly, for those in PM,

only TAR and FRR are possible. Thus FAR + TRR = 1, and TAR + FRR = 1. Hence,

FAR + TRR + TAR + FRR = 2.

April 2004