Studies of Biometric Fusion 

Appendix A 

Terminology, Experimental Design and Data Description 

Brad Ulery,1 Austin Hicklin,1 Craig Watson2

1 Mitretek Systems 

2 National Institute of Standards and Technology 

29 June 2006 

Abstract 

This document describes the datasets and matchers used in these studies, and also their baseline performance prior to 

data fusion. The primary dataset is the multi-modal NBDF06 dataset, which contains fingerprint and face images 

from over 185,000 subjects; the FpVTE MST fingerprint dataset and the BSSR1 fingerprint and face dataset were 

also used. This document also summarizes the terminology used throughout these analyses. 
Studies of Biometric Fusion 

Contents 

1 Introduction ....................................................................................................................................3

2 Terminology....................................................................................................................................3

3 Description of datasets...................................................................................................................4

3.1 NBDF06 (NIST Biometric Data Fusion 2006) Data .............................................................4

3.1.1 Types of biometric samples.................................................................................................................... 5

3.1.2 Number of subjects................................................................................................................................ 5

3.1.3 Matchers ............................................................................................................................................... 6

3.1.4 Derivation of genuine and imposter matcher scores ............................................................................. 6

3.1.5 Fingerprint image quality..................................................................................................................... 7

3.1.6 Data integrity issues in NBDF06 data: detection and results ............................................................ 10

3.2 FpVTE MST Data...................................................................................................................11

3.2.1 Types of biometric samples.................................................................................................................. 11

3.2.2 Number of subjects.............................................................................................................................. 11

3.2.3 Matchers ............................................................................................................................................. 11

3.2.4 Derivation of genuine and imposter matcher scores ........................................................................... 11

3.3 NIST Biometric Scores Set, Release 1 (BSSR1)...................................................................12

3.3.1 Types and numbers of biometric samples............................................................................................ 12

3.3.2 Matchers ............................................................................................................................................. 12

3.3.3 Derivation of genuine and imposter matcher scores ........................................................................... 12

4 Pre-fusion matcher performance................................................................................................13

4.1 Baseline performance for NBDF06 data.............................................................................14

4.2 Baseline performance for BSSR1 data.................................................................................16

4.3 Baseline performance for FpVTE MST data ......................................................................18

5 References......................................................................................................................................19

29 June 2006 2/20 
Appendix A Terminology, Experimental Design and Data Description 

1 Introduction 

This document describes the datasets and matchers used in the various analyses in “Selected Topics in 

Biometric Fusion”, and shows the baseline performance of the matchers on those datasets, before fusion 

techniques were applied. 

This document also includes an overview of terminology as used in these studies. 

2 Terminology 

Biometric data derives from a population of subjects (persons). Biometric samples (generally images) are 

collected from each subject. Taken together, the population and sample collection process are a major 

determinant of overall performance. Therefore it is important to identify the source of sample data in a 

performance evaluation. 

In multi-biometric systems, more than one sample is collected per subject. The samples collected can 

come from multiple instances (such as different fingers), multiple modalities (such as face and 

fingerprint), or can be multiple samples of the same instance. 

Samples are usually preprocessed to produce templates, which are representations of the information 

that facilitate efficient processing. 

Samples from subjects previously encountered are included in the gallery (database). Subsequent 

encounters with subjects are known as probes (searches). Subjects that exist in both the probe and gallery 

are known as genuines (and the corresponding samples are mates). Probes that do not exist in the gallery 

are known as impostors (the samples are non-mates). 

The accuracy of a biometric system is based on its ability to distinguish subjects previously known to the 

system (genuines) from subjects not known to the system (imposters). The comparison of a probe 

template and a gallery template produces a matcher score, which is a measure of sample similarity. The 

measurement scales and the distributions of the scores are unique to each system. In a perfect system, 

there would be no overlap between the genuine and imposter score distributions. 

A decision criterion is applied to scores in order to classify pairs of subjects as genuines or imposters. 

Regardless of whether a decision is correct or not, a pair of subjects is said to match if the system classifies 

them as genuines; otherwise the pair is said to be a non-match. Note that “match” is a determination, 

while “genuine” is ground truth, which may be unknowable. The decision criterion is often simply a 

score threshold. 

In multi-biometric systems, multiple scores per subject may be combined to better discriminate genuines 

from imposters. This process of combining scores is often modeled as having two stages called 

normalization and score-level fusion. Normalization generally refers to a transformation of a univariate 

score distribution (such as rescaling), whereas score-level fusion is a function of multiple scores per 

subject. Decision-level fusion is a function of multiple decisions per subject. 

Unfortunately, common usage of the terms normalization and score-level fusion makes simplifying 

assumptions about the underlying process that are not always valid. Often, normalization is based 

exclusively on score distributions as when scores are simply rescaled and recentered, or when 

transformed to probabilities or likelihood ratios. Sometimes, normalization, like fusion, refers to 

combining additional information, as when individual scores are adjusted based on information such as 

probe image quality, or gallery subject-specific score distributions. Depending upon how scores are 

combined in fusion, normalization is not always required. 

29 June 2006 3/20 
Studies of Biometric Fusion 

Given multiple scores for each probe subject (i.e., prior to fusion), the decision criterion may be a higherdimensional decision boundary. For a given fusion method, the set of possible score thresholds 

corresponds to a family of decision boundaries. 

A Receiver Operating Characteristic (ROC) curve plots the trade-off between classification errors that 

result from varying a score threshold. An ROC measures the ability of a system to discriminate genuines 

from imposters. The vertical axis of the ROC depicts the True Accept Rate (TAR) or equivalently the False 

Reject Rate (FRR = 1-TAR). TAR is the fraction of genuines that are correctly classified. The horizontal 

axis of the ROC depicts the False Accept Rate (FAR), which is the fraction of impostors that are 

misclassified. Note that empirical measurements of an ROC are subject to statistical uncertainty and 

measurement errors. 

An ideal ROC curve would include a point at TAR=1, FAR=0. Practically, a biometric system cannot 

achieve such perfect performance. When comparing two ROCs, one may be consistently superior (its 

TAR is higher at every FAR), or the two curves may cross. We say that one system is more accurate than 

another when its ROC is consistently superior. Many factors can be controlled to optimize an ROC, such 

as the biometric collection process, the choice of matcher(s), and the method of fusion. System designers 

must additionally select an optimal threshold — a point on the ROC — for their design objectives and 

operational requirements. Optimization of the overall ROC is within the scope of this work, but selection 

of an optimal threshold is beyond scope. Determining appropriate thresholds for such decisions require 

knowledge of system scale, estimates of prior probabilities of genuine and imposter subjects, and 

risk/cost functions for false rejections and false accepts. These factors are system specific, and therefore 

such thresholds only serve as examples in these analyses. 

3 Description of datasets 

The datasets used in these studies came from three sources: 

• NBDF06: This operational dataset includes criminal arrest fingerprints and face images (mugshots) 

from more than 185,000 subjects. Three of the more accurate fingerprint matchers from the NIST SDK 

tests were used [SDK], as well as three recent commercially available facial recognition systems, 

which are required to remain anonymous. Most of our analyses use the NBDF06 dataset. The source 

of the NBDF06 dataset cannot be publicly released. 

• FpVTE-MST: The Fingerprint Vendor Technology Evaluation [FpVTE] Medium-Scale Test dataset 

contains 10,000 right index fingerprints from 5,600 subjects, from flat (single finger) or slap (four 

finger simultaneous) livescan devices; this study used one pair of mated fingerprints from each of 

3,240 subjects. Fingerprint matching scores from fourteen of the participating systems were used in 

these studies. 

• BSSR1: The NIST Biometric Scores Set, Release 1 [BSSR1] contains face and fingerprint matching 

scores from two face and one fingerprint matcher. The BSSR1 dataset has the advantage of being 

publicly available, unlike the other datasets. BSSR1 is used only to a limited extent in these studies. 

A detailed explanation of these datasets follows. 

3.1 NBDF06 (NIST Biometric Data Fusion 2006) Data 

The bulk of the empirical results reported in these analyses used matcher scores based on fingerprint and 

face data from this operational database. 

29 June 2006 4/20 
Appendix A Terminology, Experimental Design and Data Description 

3.1.1 Types of biometric samples 

Each subject had one or more sets of face and fingerprint images. Each face/fingerprint set contained the 

following samples, collected at the time of the subject’s arrest: 

• Face: One frontal face image. The face images used are frontal, 24-bit color JPEG images compliant 

with ANSI/NIST-ITL 2000 image format specifications; the images are compliant with Best Practice 

Application Level 30 requirements [MugshotBP] except for size: image size is typically 384 x 480 

pixels, smaller than the 480 x 600 minimum mandated by the Best Practices document. The images 

have controlled 3-point lighting, 18% gray backgrounds (with some exceptions), and uniform full 

frontal pose. The face occupies approximately 50% of the width of each image. The expressions are 

not controlled. A visual review of a sample of the face images shows that the images are fairly typical 

of recent mugshot photographs, and better than some databases such as BCC or US-VISIT (POE). 

• Slap fingerprints: Slap livescan fingerprints from all ten fingers. Fingerprints were collected on FBIcertified livescan devices [FBI-Cert]. The four-finger slap images were segmented into individual 

fingerprint images using the NIST segmenter [NFIS]. Automated measures were used to identify 

probable segmentation failures, notably cases in which segmentation boxes touched or overlapped; 

these cases, comprising approximately 5% of the total, were excluded from the dataset. It would not 

be quite correct to regard these exclusions as failures to enroll (FTE) for several reasons: 

° These were probable segmentation failures, which are distinct from failures to enroll: failure to 

segment indicates a problem with the association of an individual fingerprint image with its 

finger position, and does not imply anything about the quality or content of the fingerprint 

features. 

° Today, slap segmentation is required at the time of collection in order to be compliant with FBI 

standards for identification slaps [EFTS-7.1]. The cases excluded in this evaluation presumably 

would have been flagged for recapture at the time of collection if the collection devices used had 

performed slap segmentation. 

• The dataset as tested should be expected to contain some incorrectly segmented fingerprints, 

although small samples of manually inspected fingerprints (ones that resulted in low matcher scores) 

did not reveal segmentation failures; see [SlapSeg] for an evaluation of segmentation accuracy and a 

discussion of issues in slap segmentation. 

• Rolled fingerprints: Rolled fingerprints were not used in these studies. 

3.1.2 Number of subjects 

Each subject had one or more face/fingerprint sets, as follows: 

• NM = Non-Mated (imposter) subjects: 122,000 subjects had one face/fingerprint set each (a single 

encounter of each subject). 

• M = Mated (genuine) subjects: 64,867 additional subjects had two face/fingerprint sets each (two 

encounters designated as M1 and M2). Of these, 4,015 subjects had three fingerprint sets each; these 

are the multi-sample subjects used in the analysis in Part X. 

These sample sizes were limited by data availability and computing resources. Given these constraints, 

the samples (especially of mated subjects) were as large as possible to allow precise measurements at very 

high TAR, as anticipated from the use of high quality matchers and N-way fusion. 

The association of mates was not biased through the use of matchers in selection: the mating information 

supplied with the dataset was determined through a combination of name-based (non-biometric) 

matching, and matching of the rolled fingerprints. The slap fingerprints and face images used in these 

studies were not used to identify the mates. 

29 June 2006 5/20 
Studies of Biometric Fusion 

3.1.3 Matchers 

Three fingerprint and three face matchers were used: 

• The fingerprint matchers were three of the more accurate fingerprint matchers identified in the NIST 

SDK single finger and two-finger tests [SDK; SDK2]: matcher H, matcher I, and matcher Q. 

• The face matchers were three recent (c. 2004-5) commercially available face recognition systems, 

which are required to remain anonymous. 

3.1.4 Derivation of genuine and imposter matcher scores 

As mentioned above (under “Number of subjects”), the sets of biometric inputs were: 

• M1: 1 set of 10 slap fingerprints and 1 face image from each of the 64,867 mated subjects (M1=Mate 

set 1) 

• M2: a second set of 10 slap fingerprints and 1 face image from each of the 64,867 mated subjects in M1 

(M2=Mate set 2) 

• NM: 1 set of 10 slap fingerprints and 1 face image from each of the 122,000 non-mated subjects (not in 

M1 or M2) (NM=Non-Mated set)

The similarity scores in the NBDF06 dataset follow the design developed for [SDK2]. Each set of scores 

consists of 

• 64,867 genuine scores, corresponding to the diagonal elements of the M1 x M2 similarity matrix 

shown in Figure 1; and 

• 122,000 imposter scores, generated by comparing each of the NM samples against one of the M1 

samples, corresponding to the 2 “diagonals” of the NM x M2 similarity matrix shown in Figure 2. 

Thirty-three sets of scores were produced by running face matchers A, B, C (one score set each) and finger 

matchers I, H, Q (one score set per finger). These are summarized in Table 1. 

Figure 1: Derivation of genuine scores in the NBDF06 dataset. Diagonal elements of the M1 x M2 

matrices provide the “true mates” (genuines) for construction of the ROC curves; off-diagonal 

comparisons were not performed. 

Left Little by Q 

Probes (M1) 

Face by A

64,867 Mate Scores (True Accepts) Gallery (M2) 

M1 = 64,867 subjects 

M2 = 64,867 subjects (mates) 

29 June 2006 6/20 
Appendix A Terminology, Experimental Design and Data Description 

122,000 Non-Mate Scores (True Rejects)

Figure 2: Derivation of imposter scores in the NBDF06 dataset. Diagonal elements of the NM x M2 

matrices provide the “true non-mates” (imposters) for construction of the ROC curves. The diagonals 

represent the 122,000 imposter comparisons that were performed. 

A B C H I Q

Face (frontal) x x x 

Right Thumb x x x 

Right Index x x x 

Right Middle x x x 

Right Ring x x x 

Right Little x x x 

Left Thumb x x x 

Left Index x x x 

Left Middle x x x 

Left Ring x x x 

Left Little x x x 

Table 1: Thirty-three sets of 64,867 genuine and 122,000 imposter scores were generated from 186,867 

distinct subjects, 251,734 distinct collection events (face/fingerprint sets), using 3 face matchers 

(A,B,C), and 3 fingerprint matchers (H,I,Q). 

3.1.5 Fingerprint image quality 

Figure 3 and Figure 4 show the distribution of fingerprint quality for the NBDF06 fingerprint data, using 

the NFIQ metric [NFIQ; NFIS]. 1.3% of all fingerprints were NFIQ 5; 4.8% were NFIQ 4 or 5. In 0.01% of 

the ten-finger sets, all fingerprints were NFIQ 5; in 0.22% of the ten-finger sets, all fingerprints were NFIQ 

4 or 5. 

Left Little by Q 

Probes (NM) 

Face by A

Gallery (M2) 

NM = 122,000 subjects (non-mates) 

M2 = 64,867 subjects (mates) 

29 June 2006 7/20 
Studies of Biometric Fusion 

NFIQs for all NBDF06 fingerprints

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

RT LT RI LI RM LM RR LR RL LL Worst

(all 10)

Best 

(all 10)

Best 

(RSlap)

Best 

(LSlap)

5 (worst)

4

3

2

1 (best)

Figure 3: NFIQ results for all genuine and imposter fingerprints, including the best and worst 

fingerprint from each subject, and the best fingerprint from each slap from each subject. 

NFIQs for all NBDF06 fingerprints (Detail)

90%

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%

RT LT RI LI RM LM RR LR RL LL Worst

(all 10)

Best 

(all 10)

Best 

(RSlap)

Best 

(LSlap)

5 (worst)

4

3

2

1 (best)

Figure 4: Detail of Figure 3 (NFIQ results for all genuine and imposter fingerprints) 

Figure 5 and Figure 6 show the distribution of NFIQ fingerprint quality for the poorer quality fingerprint 

in all mated pairs (genuines). 

29 June 2006 8/20 
Appendix A Terminology, Experimental Design and Data Description 

Worst quality fingerprint for all mated pairs

Max(NFIQprobe, NFIQgallery) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

RT LT RI LI RM LM RR LR RL LL Worst

(all 10)

Best 

(all 10)

Best 

(RSlap)

Best 

(LSlap)

5 (worst)

4

3

2

1 (best)

Figure 5: NFIQ results for the worst NFIQ value for every mated (genuine) comparison, including the 

best and worst finger position from each subject, and best finger position for each slap from each 

subject. 

Worst quality fingerprint for all mated pairs (Detail)

Max(NFIQprobe, NFIQgallery) 

90%

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%

RT LT RI LI RM LM RR LR RL LL Worst

(all 10)

Best 

(all 10)

Best 

(RSlap)

Best 

(LSlap)

5 (worst)

4

3

2

1 (best)

Figure 6: Detail of Figure 5 (NFIQ results for the worst NFIQ value for every mated comparison) 

29 June 2006 9/20 
Studies of Biometric Fusion 

3.1.6 Data integrity issues in NBDF06 data: detection and results 

Precise measurement of very small error rates on large datasets requires a detailed analysis of the data for 

potential data integrity issues; this process is often known as “groundtruthing”. The specific issues that 

were considered in this study include: 

• Unconsolidated records: A data integrity error (metadata error) in which the same person has records 

under different identifiers 

• Misidentified records (misidentifications): A data integrity error in which records from different 

people are listed under the same identifier 

• Swapped fingers: An error in which fingerprint images for one subject are incorrectly identified (as 

when the left and right thumb images are positionally reversed) 

• Repeated fingers: An error in which one fingerprint is captured twice in a fingerprint set (as when the 

left thumb field of a record contains a second impression of the right thumb) 

Such issues are often due to collection problems or administrative errors; see [DataQuality] for a full 

discussion. Note that fusion can be used to detect or overcome some data integrity issues. 

The reason that detection of data integrity issues is so critical is that they set a limit on error rates. Given 

an accurate matcher: 

• FRR cannot be lower than the rate of misidentifications 

• FAR cannot be lower that the rate of unconsolidated records 

3.1.6.1 Detecting data integrity problems (Groundtruthing) 

Data integrity problems can be difficult to detect in large datasets. Redundant data (faces and multiple 

fingers) and multiple accurate matchers provide a basis for locating problems which can then be 

reviewed manually. To automate detection, metrics were developed based on two methods: score-level 

fusion and Mahalanobis analysis (which identifies outliers in the score data). Both methods were effective 

at identifying subject pairs that were misidentified as genuines. Mahalanobis analysis readily revealed 

partial errors, such as swapped thumbs of subjects whose fused scores were high because the face or 

other fingerprints matched. 

Thorough human review was not possible. Cases were reviewed if metrics flagged all fingerprints, and a 

sample of cases was reviewed in which metrics flagged only some of the fingerprints. In practice, this 

means that we believe that we detected 

• most or all cases in which subject pairs were incorrectly identified as genuines (misidentifications), 

i.e., did not belong in M1 and M2; 

• most or all cases in which the thumbs were swapped; 

• most or all cases in which the slaps were swapped; 

• some of the cases in which only one thumb or slap was invalid (such as the right thumb repeated in 

both thumb images); 

• some of the cases in which the faces were misidentified. 

Some data integrity issues were almost certainly undetected, particularly if they only affected one source 

image (face, one thumb, or one slap). These cases are often difficult to separate from image quality 

problems. 

3.1.6.2 Results 

Among 64,867 subjects, we found 33 data integrity problems (0.051%): 

• 24 had face and fingerprints misidentified (0.037%) 

29 June 2006 10/20 
Appendix A Terminology, Experimental Design and Data Description 

• 9 had some but not all fingerprints misidentified (0.014%) 

This means that FRR smaller than 0.037% should not be possible at low FAR1, and that FRR smaller than 

0.051% is possible only when fusion tolerates these errors due to using additional fingerprints or face 

images that were not misidentified. 

Among all 186,867 subjects, no unconsolidated records were detected in our analysis of this dataset. This 

is not surprising, as each non-mate (impostor) was compared to only one subject in the gallery, i.e., a full 

similarity matrix was not produced. 

Among the very low-scoring genuines manually reviewed, an additional 46 subjects (0.071%) had some 

or all fingerprints noted as egregiously poor quality. 

3.2 FpVTE MST Data 

The Fingerprint Vendor Technology Evaluation (FpVTE) Medium-Scale Test (MST) dataset and matchers 

are fully described in [FpVTE]. The following is a summary. The FpVTE test was not rerun: the results in 

the “Multi-Algorithm Fusion” study are based on further processing of the FpVTE MST data from the fall 

of 2003. 

3.2.1 Types of biometric samples 

The MST dataset contained right index fingerprints from flat (single finger) or slap (four finger 

simultaneous) livescan devices. The fingerprints were collected from a range of governmental sources. 

3.2.2 Number of subjects 

There were 5,600 subjects, and a total of 10,000 fingerprints (all right index fingerprints). Among these, 

there were at least two fingerprints for 3240 subjects. The full similarity matrix for these subjects 

(3240x3240) was used in this study. 

3.2.3 Matchers 

The fusion analysis used fingerprint matching scores from fourteen of the fingerprint systems that 

participated in the FpVTE MST evaluation in 2003. Of the matchers from the original FpVTE MST, four 

were not included in the fusion analysis, due to redundancy: 

• SAGEM and Ultrascan had multiple entries, of which only the more accurate was used in this study. 

• Golden Finger and Raytheon returned equivalent similarity matrices on MST (differing in only 2 out 

of 100 million scores), so only Golden Finger was used in this analysis. 

3.2.4 Derivation of genuine and imposter matcher scores 

Each system in the MST evaluation generated a complete 10,000 x 10,000 similarity matrix, by comparing 

all pairwise combinations of fingerprint images. For these analyses, a subset of this matrix was used, with 

M=3,240 and NM=M (3,240 x 3,240), resulting in 3,240 genuine scores and about 10 million imposter 

scores. 

 

1 A coincidence of matching error and integrity error could result in a lower FRR. 

29 June 2006 11/20 
Studies of Biometric Fusion 

3.3 NIST Biometric Scores Set, Release 1 (BSSR1) 

Some of the experiments conducted used the NIST Biometric Scores Set, Release 1, which is described in 

[BSSR1]. The BSSR1 Dataset has the advantage of being freely available, and therefore is useful for 

replicating results. While the size of this dataset is sufficient for an exploratory study, the results from the 

NBDF06 dataset generally supersede BSSR1 results. BSSR1 contains scores based on both faces and 

fingerprints. The sources of these faces and fingerprints have not been made public. 

3.3.1 Types and numbers of biometric samples 

BSSR1 includes 3 datasets: 

• Set 1 is based on face and fingerprint data from 517 subjects, with scores from two face matchers and 

one fingerprint matcher. 

• Set 2 is based on fingerprint data from 6000 subjects. 

• Set 3 (Face data from 3000 subjects) was not used in this study. 

Elapsed time between a single individual’s samples ranges from 1 day to 29 months with an average of 7 

months. 80% of the elapsed times are less than 12 months. 

3.3.2 Matchers 

BSSR1 includes scores from two anonymous c. 2002 face matchers (C and G). No further information has 

been made public about these matchers. 

BSSR1 includes scores from one fingerprint matcher: V, the NIST VTB Bozorth matcher, developed in 

1993-95. A description of the VTB and analysis of its performance is included in [VTB], and in Appendix 

B of [FpVTE]. In the [FpVTE] and [SDK] evaluations, the performance of the VTB was shown to be about 

average in comparison to the performance of the commercially available matchers that participated in 

those evaluations. The source code for the Bozorth matcher is publicly available in [NFIS]. 

3.3.3 Derivation of genuine and imposter matcher scores 

BSSR1 Set 1 is based on face and fingerprint data from 517 subjects, with scores from two anonymous c. 

2002 face matchers (C and G), and one fingerprint matcher (V). Set 1 contains four 517x517 similarity 

matrices: right index fingerprints scored by matcher V, left index fingerprints scored by matcher V, 

frontal face images scored by matcher C, and frontal face images scored by matcher G. Each similarity 

matrix contains 517 genuine scores and 266,772 (517 * 516) imposter scores. 

BSSR1 Set 2 is comprised of fingerprint scores from matcher V run on the fingerprints of 6000 subjects. 

There is one 6000 x 6000 similarity matrix for the left index fingerprints, and another for the right index 

fingerprints. Each matrix contains 6000 genuine scores and 35,994,000 (6000 * 5999) imposter scores. The 

similarity matrices are depicted in Figure 7. 

29 June 2006 12/20 
Appendix A Terminology, Experimental Design and Data Description 

Probes 

Gallery 

Set 1: 517 x 517

Face by C 

Face by G 

Right Index by V

Left Index by V

Probes 

Gallery 

Set 2: 6000 x 6000 

Right Index by V 

Left Index by V 

Figure 7: Structure of BSSR1 Set 1 and Set 2. Diagonal elements represent the “true mates” (genuines) 

and off-diagonal elements represent the “true non-mates” (imposters) for construction of the ROC 

curves. 

4 Pre-fusion matcher performance 

This section shows the accuracy of the matchers prior to fusion. It is provided as a baseline for 

comparison with fused results in the other studies in this collection. 

29 June 2006 13/20 
Studies of Biometric Fusion 

4.1 Baseline performance for NBDF06 data 

Figure 8: Comparison of face matchers A, B, and C on the NBDF06 dataset 

Figure 9: Fingerprint matcher H on the NBDF06 dataset, showing separate results for each finger 

29 June 2006 14/20 
Appendix A Terminology, Experimental Design and Data Description 

Figure 10: Fingerprint matcher I on the NBDF06 dataset, showing separate results for each finger 

Figure 11: Fingerprint matcher Q on the NBDF06 dataset, showing separate results for each finger 

29 June 2006 15/20 
Studies of Biometric Fusion 

Figure 12: Confidence intervals at selected TAR/FRR and FAR points for the NBDF06 dataset 

4.2 Baseline performance for BSSR1 data 

Figure 13: Face and fingerprint matchers on BSSR1 Set 1 

29 June 2006 16/20 
Appendix A Terminology, Experimental Design and Data Description 

Figure 14: Fingerprint matcher on BSSR2 Set 2 

BSSR1 Set 1 95% Confidence Intervals

0.75

0.8

0.85

0.9

0.95

1

0.00001 0.0001 0.001 0.01 0.1 1

Figure 15: Confidence intervals at selected TAR/FRR and FAR points for the BSSR1 dataset 

29 June 2006 17/20 
Studies of Biometric Fusion 

4.3 Baseline performance for FpVTE MST data 

The baseline performance of the FpVTE MST matchers can be found in detail in Appendix C of [FpVTE]. 

Figure 16: Baseline performance of 18 matchers on FpVTE MST dataset [FpVTE] 

29 June 2006 18/20 
Appendix A Terminology, Experimental Design and Data Description 

FpVTE MST 95% Confidence Intervals

0.75

0.8

0.85

0.9

0.95

1

0.00001 0.0001 0.001 0.01 0.1 1

Figure 17: Confidence intervals at selected TAR/FRR and FAR points for the FpVTE MST dataset 

5 References 

[BSSR1] NIST; Biometric Scores Set –- Release 1 (BSSR1); September 2004. 

http://www.nist.gov/biometricscores/ 

[DataQuality] Hicklin, Khanna; “The Role of Data Quality in Biometric Systems”; February 2006; 

http://www.mitretek.org/Role_of_Data_Quality_Final.pdf 

[EFTS-7.1] Federal Bureau of Investigation, Criminal Justice Information Services (CJIS); 

Electronic Fingerprint Transmission Specification (EFTS); IAFIS-DOC-01078-7.1; May 2, 

2005. http://www.fbi.gov/hq/cjisd/iafis/efts71/cover.htm 

[FBICert] Products Certified For Compliance with the FBI's Integrated Automated Fingerprint 

Identification System Image Quality Specifications; 

http://www.fbi.gov/hq/cjisd/iafis/cert.htm 

[FpVTE] C. Wilson, A. Hicklin, H. Korves, B. Ulery, M. Zoepfl, M. Bone, P. Grother, R. 

Micheals, S. Otto, C. Watson; "Fingerprint Vendor Technology Evaluation 2003"; 

NIST Interagency Report 7123. June 2004. http://fpvte.nist.gov/ 

[FRVT2002] Phillips, Grother, Micheals, Blackburn, Tabassi, Bone; Face Recognition Vendor Test 

2002; March 2003. http://www.frvt.org 

[MugshotBP] “Best Practice Recommendation for the Capture OF Mugshots”; Version 2.0; 

September 23, 1997. (http://www.itl.nist.gov/iad/894.03/face/bpr_mug3.html ) Note: 

this is identical to “Best Practice Application Level 30” in the forthcoming 

ANSI/NIST ITL-1 2006 standard. 

29 June 2006 19/20 
Studies of Biometric Fusion 

 [NFIQ] Tabassi, E. et al., “Fingerprint Image Quality”; NIST Interagency Report 7151, August 

2004. 

 [NFIS] C. Watson, et al; NIST Fingerprint Image Software. 

http://www.itl.nist.gov/iad/894.03/databases/defs/nist_nfis.html 

[SDK] Craig Watson, Charles Wilson, Karen Marshall, Mike Indovina, & Rob Snelick; 

“Studies of One-to-One Fingerprint Matching with Vendor SDK Matchers”; NIST 

Interagency Report 7221; April 2005. http://fingerprint.nist.gov/SDK/ir_7221.pdf 

[SDK2] C. Watson, C. Wilson, M. Indovina, B. Cochran; “Two Finger Matching With Vendor 

SDK Matchers”; NIST Interagency Report 7249; July 2005. 

http://fingerprint.nist.gov/SDK/ir_7249.pdf 

[SlapSeg] Ulery, Hicklin, Watson, Kwong; Slap Segmentation Evaluation 2004, NIST 

Interagency Report 7209. March 2005. 

http://fingerprint.nist.gov/SlapSeg04/ir_7209.pdf

[Snelick-03] R. Snelick, M. Indovina, J. Yen, and A. Mink, “Multimodal Biometrics: Issues in 

Design and Testing”, in Proceedings of Fifth International Conference on Multimodal 

Interfaces, (Vancouver, Canada), November 2003. 

http://w3.antd.nist.gov/pubs/ICMI_submit_4_23_03.pdf 

[VTB] C. Wilson, C. Watson, M. Garris, A. Hicklin; "Studies of Fingerprint Matching Using 

the NIST Verification Test Bed (VTB)"; NIST Interagency Report 7020, July 2003. 

ftp://sequoyah.ncsl.nist.gov/pub/nist_internal_reports/ir_7020.pdf 

29 June 2006 20/20