NISTIR 7950

Examination of the Impact of Fingerprint Spatial Area 

Loss on Matcher Performance in Various Mobile 

Identification Scenarios

Shahram Orandi

Kenneth Ko

Stephen S. Wood

John D. Grantham

Michael D. Garris

http://dx.doi.org/10.6028/NIST.IR.7950
NISTIR 7950

Examination of the Impact of Fingerprint Spatial Area 

Loss on Matcher Performance in Various Mobile 

Identification Scenarios

Shahram Orandi

Kenneth Ko

Stephen S. Wood

Michael D. Garris

Information Access Division

Information Technology Laboratory

John D. Grantham

Systems Plus, Inc.

Rockville, MD

March 2014

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director
Table of Contents

1. INTRODUCTION ..................................................................................................................................................................1

2. STUDY OBJECTIVES..........................................................................................................................................................2

3. MATERIALS AND METHOD ...........................................................................................................................................3

3.1. SEARCH PROBE GENERATION ........................................................................................................................................................3

3.2. FINGER NUMBERING METHODOLOGY.........................................................................................................................................5

3.3. GALLERY SELECTION .......................................................................................................................................................................6

3.4. MATCHER CONFIGURATION ...........................................................................................................................................................6

3.5. TEST CASES .........................................................................................................................................................................................7

3.6. SCORING METHODOLOGY ...............................................................................................................................................................8

3.7. SYSTEM SCORE THRESHOLDS OF INTEREST..............................................................................................................................8

3.8. DISTANCE BASED COMPARISON AND RANKING......................................................................................................................9

3.9. SYSTEM THROUGHPUT CALCULATION METHODOLOGY ....................................................................................................10

4. RESULTS...............................................................................................................................................................................11

4.1. OBJECTIVE 1: BASELINE IDFP SYSTEM PERFORMANCE MEASUREMENT ...................................................................12

4.1.1. Data for Objective 1 ................................................................................................................................................................12

4.1.2. Results for Objective 1...........................................................................................................................................................12

4.2. OBJECTIVE 2: TWO FINGER RISC SCENARIO COMPARISON ............................................................................................13

4.2.1. Data for Objective 2 ................................................................................................................................................................13

4.2.2. Results for Objective 2...........................................................................................................................................................14

4.3. OBJECTIVE 3: IMPACT OF VARIOUS PROBE FINGER COMBINATIONS ............................................................................15

4.3.1. Data for Objective 3 ................................................................................................................................................................15

4.3.2. Results for Objective 3...........................................................................................................................................................17

4.4. OBJECTIVE 4: IMPACT OF GALLERY CONFIGURATION ON TWO FINGER FAPS..........................................................19

4.4.1. Data for Objective 4 ................................................................................................................................................................19

4.4.2. Results for Objective 4...........................................................................................................................................................22

4.5. OBJECTIVE 5: SYSTEM THROUGHPUT BEHAVIOR..................................................................................................................23

4.5.1. Data for Objective 5 ................................................................................................................................................................23

4.5.2. Results for Objective 5...........................................................................................................................................................25

4.6. OBJECTIVE 6: IDENTIFYING AN OPTIMAL STRATEGY.........................................................................................................26

4.6.1. Data for Objective 6 ................................................................................................................................................................27

4.6.2. Results for Objective 6...........................................................................................................................................................29

4.7. OBJECTIVE 7: EXPLORATORY EXAMINATION OF EMERGING SMALL-PLATEN CAPTURE DEVICES.....................30

4.7.1. Data for Objective 7 ................................................................................................................................................................30

4.7.2. Results for Objective 7...........................................................................................................................................................32

5. DISCUSSION........................................................................................................................................................................33

6. CONCLUSIONS ..................................................................................................................................................................35

7. REFERENCES .....................................................................................................................................................................36

ii
List of Tables

Table 1 - Acquisition Profiles Examined in this Study..........................................................................................................................................3

Table 2 - Simulated Mobile ID Data Generation Process ....................................................................................................................................4

Table 3 - Probe Impression Combinations....................................................................................................................................................................5

Table 4 - Test Gallery Composition.....................................................................................................................................................................................6

Table 5 - Mobile ID Test Cases for This Study.............................................................................................................................................................7

Table 6 - Scoring Threshold Values ....................................................................................................................................................................................9

Table 7 - Baseline IDFP System Performance..........................................................................................................................................................12

Table 8 - Matcher Performance by Mobile ID FAP, Two Finger Probe Scenario (2, 7).............................................................13

Table 9 - System Performance by Various Probe Finger Combinations ..............................................................................................15

Table 10 - System Performance by Various Probe Gallery Combinations, Two Finger Probe Scenario (2, 7)........19

Table 11 - Median System Matching Times (milliseconds) for Selected FAPs vs. FD-249 Gallery...................................23

Table 12 - Median Matching Times (milliseconds) for Image-Only Searches vs. Finger Combination.........................24

Table 13 - Strictly Increasing FAP Median Match Times (FAP10 < FAP20 < FAP30) for Various Scenarios ...........25

Table 14 - Performance Data Ranked by Distance-to-Optimal for Error Trade-off ....................................................................27

Table 15 - Comparison of Experimental 1/4" FAP Versus Traditional Mobile ID FAPs...........................................................31

Table 16 - Summary Matching Performance Data by Probe and Gallery Type...............................................................................38

Table 17 - Summary Matching Throughput by Probe and Gallery Type (median match time, milliseconds).........39

List of Figures

Figure 1 - Finger Numbering Methodology ..................................................................................................................................................................6

Figure 2 - Matcher Performance by Mobile ID FAP, Two Finger Probe Scenario (2, 7)...........................................................13

Figure 3 - Error Tradeoff for Various Mobile ID FAPs in Two Finger (2, 7) Probes vs. FD-249 Gallery ......................14

Figure 4 - False Positive Identification Rates by Probe Finger Combination...................................................................................16

Figure 5 - False Negative Identification Rates by Probe Finger Combination.................................................................................16

Figure 6 - Error Tradeoff for Various Mobile ID FAPs and Finger Combinations vs. FD-249 Gallery............................16

Figure 7 - Error Tradeoff for Individual Mobile ID FAPs and Finger Combinations vs. FD-249 Gallery......................17

Figure 8 - Matcher Performance by Gallery Configuration, Two Finger Probe Scenario (2, 7)..........................................20

Figure 9 - Error Tradeoff for All Two Finger Mobile ID FAPs (2, 7) vs. Various Gallery Configurations.....................21

Figure 10 - Error Tradeoff for Each Two Finger Mobile ID FAP (2, 7) vs. Various Gallery Configurations ...............22

Figure 11 - Median Matching Times (milliseconds) for Selected FAPs vs. FD-249 Gallery ...................................................23

Figure 12 - Median Matching Times (milliseconds) for Image-Only Searches vs. Finger Combination.......................24

Figure 13 - Error Tradeoff for all Test Cases ............................................................................................................................................................28

Figure 14 - Example of an Embedded Small Biometric Verification Sensor Such as the iPhone 5S................................30

Figure 15 - Error Tradeoff Comparison of Experimental 1⁄4” FAP Versus Traditional Mobile ID FAPs ........................31

iii
Executive Summary

NIST conducted an independent study to examine the impact of various mobile device Fingerprint Acquisition 

Profiles (FAPs) on the accuracy and throughput of the Next Generation Identification (NGI) Repository for 

Individuals of Special Concern (RISC) subsystem. RISC is intended for use primarily by personnel in the field 

operating portable or mobile devices that collect either fewer fingerprints than are traditionally captured in a 

fingerprint booking environment, and/or smaller fingerprint impressions than traditionally collected.

In conducting this study, NIST developed a database and a methodology which accommodates multiple 

enrollment gallery configurations as well as various spatial geometries for the probe images simulating the 

various fingerprint acquisition profiles according to the Mobile ID Best Practices Recommendation [MOBID] and 

the ANSI/NIST Standard [AN2k11] against various background (gallery) data scenarios. 

For the typical field use-case of capturing two finger images and matching against a gallery of full FD-249 

records, fingerprint acquisition profile 10 (FAP10) is at a disadvantage compared to other FAPs that define larger 

capture areas. In terms of false non-identification rate (FNIR), FAP10 submissions fail to identify their target 

more than twice as often as FAP20 (1.164 % vs. 0.506 %) at the system low confidence threshold (Yellow). For 

the high confidence threshold (Red), FNIR for FAP10 is almost twice that of FAP20 (3.270 % vs. 1.844 %). FAP30 

on the other hand appears to be best/optimal and its performance is on a par with (if not slightly better than) the 

uncropped control case (d=0.0162 for FAP30, d=0.0164 for control).

Comparing various probe finger combinations shows ten and four finger probe sets typically outperforming two

finger probe sets in matching. While the FAP10 two finger operational case is not the worst performing among 

those tested, significant matching performance benefits can be realized by either increasing the count of fingers 

captured in FAP10, or utilizing FAP30 in two finger capture operations. Capturing more fingers using the smaller 

single-finger mobile capture devices may increase the risk of sequencing errors during the capture process so 

this mitigation strategy may not be optimal.

Examining system throughput performance shows that system match times appear closely linked to image 

spatial geometry where median search times for all image search FAPs consistently increases from FAP10 to 

FAP30 (with certain isolated exceptions). The two finger case of (2,7)1 appears to provide the best throughput 

performance of all cases examined, while the two finger case of (1,6) incurs an unusually large throughput 

penalty.

Given the data in this study, the operationally typical case of FAP10 two finger (2,7) suffers from a significant 

performance penalty in terms of FNIR, showing a miss-rate almost twice as high as FAP20 and FAP30. It is 

possible to reduce FNIR significantly by adopting either FAP20 or FAP30. In cases where investment in legacy 

equipment must be preserved, a possible mitigation strategy for this performance gap is to utilize four finger 

capture (i.e., 2,3,7,8) with the smaller FAP10, while taking steps to avoid an increase in sequencing errors.

Finally, the two thumbs (1,6) combination appears to be at a significant disadvantage ranking below most of the 

other cases examined in terms of FNIR. This disadvantage is marked and is not substantially mitigated by using 

various gallery strategies or FAP levels, with the two thumbs (1,6) FAP10 test cases alone occupying 6 of the 8 

lowest ranked performance cases out of the 60 cases examined in total.

1 For more information on the finger numbering system used in this study please refer to section 3.2.

iv

 
Abstract

Background: NIST conducted a study of the FBI Repository for Individuals of Special Concern (RISC) system 

using various gallery and Mobile ID [MOBID] acquisition profile combinations to examine performance 

characteristics of the various profiles in terms of matching effectiveness and throughput. 

Results: The predominant RISC operational case of Mobile ID FAP10 (fingerprint acquisition profile 10) using 

the left and right index fingers is at a marked disadvantage in terms of matcher performance compared to the 

larger FAP20 and FAP30 cases using the same fingers. In terms of false non-identification rate (FNIR), FAP10 

submissions fail to identify their target approximately twice as often as FAP20 or FAP30. FAP30 appears to be 

best/optimal and its performance is on a par with (if not slightly better) than the uncropped control case. False 

positive identification rates remain relatively flat across all the cases examined.

Conclusion: System false non-identification rates suffer a significant performance penalty in the typical 

operational case of FAP10 two index finger (2,7) capture. FNIR performance can be markedly improved by 

either adopting FAP20 or FAP30 capture. To preserve legacy FAP10 equipment, it may be possible to utilize four 

finger capture (i.e., 2,3,7,8) with the smaller FAP10 to help mitigate some of the large performance penalty 

incurred with FAP10 in two finger operation, but this may increase the risk of sequencing errors from additional 

fingers having to be captured. Utilizing additional fingers or larger FAPs can also help mitigate performance 

penalties when a database of only-rolled or only-flat fingerprint impressions is used rather than one populated 

with complete FD-249 records.

Keywords

Mobile ID; Capture Spatial Area; NGI RISC; Biometrics; FAP10; SAP10; FAP20; SAP20; FAP30; SAP30

v
1. Introduction

In 2013, the National Institute of Standards and Technology (NIST)—in partnership with the Federal Bureau of 

Investigation (FBI)—conducted an independent study to examine the impact of various mobile device 

Fingerprint Acquisition Profiles (FAPs) on the accuracy and throughput of the Next Generation Identification 

(NGI) Repository for Individuals of Special Concern (RISC) subsystem. The results of this effort provide insight 

into current system performance and a baseline for future NGI incremental improvements. The RISC subsystem 

is geared to providing an enhanced capability to identify persons of heightened investigative interest or who 

present special risks to the public or to law enforcement personnel. RISC is intended for use primarily by 

personnel in the field operating portable or mobile devices that collect either fewer fingerprints than are 

traditionally captured in a fingerprint booking environment, and/or smaller fingerprint impressions than 

traditionally collected.

In conducting this study, NIST developed a database and a methodology which accommodates multiple 

enrollment gallery configurations as well as various spatial geometries for the probe images simulating the 

various RISC capture scenarios according to the Mobile ID Best Practices Recommendation [MOBID] and the 

ANSI/NIST Standard [AN2k11]. 

The background data used in this study consisted of operational data provided by the FBI. Moreover, the search 

data was originally selected through biographical means without the use of an AFIS one-to-many search protocol, 

but later verified through automated biometric verification. This mitigates the possibility of bias in data 

selection caused by automated one-to-many biometric searching as the primary selection process.

1
2. Study Objectives

NIST’s objectives for this study examine the effectiveness of various RISC scenarios and the impact of those 

scenarios on matcher system performance2. The specific goals and objectives are as follows:

1. Establish a baseline of matcher performance in standard ten-print mode of operation, using 

identification-flat probe data as the base case.

2. Examine differences in RISC matcher performance given two finger Mobile ID acquisition profiles.

3. Examine differences in RISC matcher performance given various Mobile ID finger combinations.

4. Examine the impact of gallery impression type on the effectiveness of Mobile ID matching.

5. Measure the impact of various Mobile ID scenarios on system throughput performance.

6. Propose an optimization strategy for the system given the examined test cases.

7. Conduct a cursory evaluation of emerging small platen devices and the impact of such devices on 

matching performance.

2 Any hardware and software products identified in this report were used in order to perform the evaluations of 

experimental objectives described in this document. In no case does any 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.

2

 
3. Materials and Method

In order to create the dataset of fingerprint images to be used in this experiment, selections were made from the 

FBI-provided Platinum Data Repository (PDR). The PDR contains 4 225 062 mated biometric records including a 

mix of full rolled FD-2493 capture sets and identification-flat4 capture sets. Of the 4 225 062 records, a total of 

390 268 represent matched pairs of records meaning that each pair consists of two distinct biometric records 

taken from the same individual.

3.1. Search Probe Generation

A total of 100 000 image sets were randomly selected from the PDR to serve as probe (search) biometric record 

sets. Of these probes, 50 000 samples were selected that had exactly one corresponding matched/mated record 

available in the gallery and 50 000 were selected that did not have any corresponding match/mate record 

available.

The probe sets were all selected to be identification-flat image sets which had already been segmented by an 

automated process to identify the location of the individual finger images in the larger multi-finger slap image. 

No effort was made to conduct further ground-truth evaluation and verification of the segmentation process.

The 100 000 mated and non-mated probe image sets were then processed to produce image sets intended to 

simulate the various Subject Acquisition Profiles or SAPs as defined in [MOBID]. It should be noted that this 

terminology has now been superseded in [AN2k11] where these profiles are now referred to as Finger 

Acquisition Profiles or FAPs. The FAPs of interest that were examined in the scope of this study are described in 

Table 1 below.

Table 1 - Acquisition Profiles Examined in this Study

Dimensions FAP(SAP) Profile Name

FAP10 FAP20 FAP30 Control

Image Width 0.5” (12.70mm)5 0.6” (15.24mm) 0.8” (20.32mm) Full image width (1.6” or 40.64mm max)

Image Height 0.65” (16.51mm) 0.8” (20.32mm) 1.0” (25.4mm) Full image height (1.5” or 38.10mm max)

Sample

To create the simulated Mobile ID FAP images, a subsection of the finger images were cropped according to 

dimensions of interest for a given FAP (see Table 1). The cut sub-section was positioned at the geometric center 

of the finger as identified by using the segmentation coordinates of each digit as provided in the PDR data set 

(see Table 2).

3 The FD-249 is a sample collection card typically used in U.S. criminal booking scenarios. It allows for the capture of all 10 rolled fingerprint 

images, and the corresponding left-slap (left-slap-4), right-slap (right slap-4), left thumb (plain) and right thumb (plain) impressions.

4 The identification-flat (or id flat) image set consists of captured left-slap, right-slap, and a slap capture of both thumbs captured together or 

thumbs captured individually. 5 Resolution values and scanner platen dimensions are specified in inches and pixels per inch (ppi) respectively throughout this document. 

This is based on widely used specification guidelines for such devices and imagery, and is accepted as common nomenclature within the 

industry. SI units for these will be presented only once.

3

 
Table 2 - Simulated Mobile ID Data Generation Process

Stage 1 – Extract Segmentation Coordinates From Biometric 

Record Set

Stage 2 - Establish Geometric Center for Each Segmentation 

Box

Stage 3 - Cut Image of Desired Size From Each Finger Image (Cut Location Centered by Segmentation Coordinates)

For FAP10, FAP20 and FAP30 when the segmentation box is smaller than the desired FAP dimensions, the cut 

subsection image is padded with white space (gray-scale index value of 255). The control image is an exception 

where no attempt is made to pad the image.

Given this data generation methodology, there is certain consistency in the way the simulated FAP10/20/30 

images are cut and created for testing which can eliminate any bias from the variance in placement of the finger 

at each FAP capture. The variance in finger placement as they are presented naturally at capture time may have 

a measurable impact on matcher behavior as well that may be masked by the image cutting methodology in this 

study. Measuring the impact of finger placement however is outside of the scope of this study and may be 

explored in a separate study. 

4
In addition to various FAPs, probe sets were also configured for various finger impression combinations. These 

finger combinations are listed in Table 3.

Table 3 - Probe Impression Combinations

Impression Case Number Finger Numbers6 in Probe Set Example

1 1 through 10 (full set)

2 2, 3, 7, 8

3 1, 2, 6 and 7

4 2 and 7

5 1 and 6

3.2. Finger Numbering Methodology

The finger numbering system used in this study is that of the Henry Classification System which is the accepted 

norm for the industry. In the Henry Classification system fingers on the right hand are numbered from 1 to 5 

starting with the thumb, and moving outward to the small finger (see Figure 1). Fingers on the left hand are 

numbered from 6 to 10 starting with the thumb, and moving outward.

6 The finger numbering system used in this study is that of the Henry Classification System. For more information on this system please refer 

to section 3.2.

5

 
Figure 1 - Finger Numbering Methodology

3.3. Gallery Selection

A total of 1 615 228 FD-249 sets were selected from the PDR to serve as gallery image sets. Of these 1 615 228

image sets, 50 000 sets had exactly one corresponding matched record set in the probe set and 1 565 228 were 

randomly selected so that they did not have any corresponding matching pair in the probe sets.

The 1 615 228 FD-249 records were then used to generate three discrete test gallery configurations sets (see 

Table 4). The first test gallery was populated to contain the original FD-249 full set with no changes or 

modifications. The second test gallery was populated by using only the rolled images from the FD-249 set (and 

omitting the flat/slap images). The third test gallery was populated by using only the flat/slap images (and 

omitting the rolled images) from the FD-249 base record.

During each test scenario execution, only one gallery configuration was active. The system was purged and 

repopulated with each test gallery to test new scenarios.

Table 4 - Test Gallery Composition

Gallery Number Gallery Composition Gallery Size Total

1 Original FD-249 set (Rolled and Flat Images) 1 615 228

2 Rolled Ten-Print Images Only 1 615 228

3 Slap (Flat) Ten-Print Images Only 1 615 228

The test gallery was generated with the intent to provide a robust test environment to yield measurement of 

matching errors with sufficient resolution for this study. The performance data obtained in this study therefore 

is dependent on the gallery selected for this study and may not be indicative of actual performance of the system 

using the contemporary FBI NGI gallery of over 70 million biometric records.

3.4. Matcher Configuration

The matcher used in this study is an FBI supplied modern state-of-industry commercial fingerprint matcher. The 

matcher supports both rolled fingerprints as well as flat fingerprint impression matching. The matcher also 

supports specific optimizations for data typical of Mobile ID in RISC field scenarios. 

6
3.5. Test Cases

Given the various combination of gallery and probe sets, a total of 60 test cases of interest were identified and 

executed for this study (see Table 5) and candidate lists with match scores were retrieved for each test case for 

scoring.

Table 5 - Mobile ID Test Cases for This Study

Test Case Number Gallery Configuration Probe Type Probe Impressions

1

Full FD-249

FAP10

1 - 10

2 2, 3, 7, 8

3 1, 2, 6, 7

4 2, 7

5 1, 6

6

FAP20

1 - 10

7 2, 3, 7, 8

8 1, 2, 6, 7

9 2, 7

10 1, 6

11

FAP30

1 - 10

12 2, 3, 7, 8

13 1, 2, 6, 7

14 2, 7

15 1, 6

16

Control

1 - 10

17 2, 3, 7, 8

18 1, 2, 6, 7

19 2, 7

20 1, 6

21

Rolled Only

FAP10

1 - 10

22 2, 3, 7, 8

23 1, 2, 6, 7

24 2, 7

25 1, 6

26

FAP20

1 - 10

27 2, 3, 7, 8

28 1, 2, 6, 7

29 2, 7

30 1, 6

31

FAP30

1 - 10

32 2, 3, 7, 8

33 1, 2, 6, 7

34 2, 7

35 1, 6

36

Control

1 - 10

37 2, 3, 7, 8

38 1, 2, 6, 7

39 2, 7

40 1, 6

41

Flat Only

FAP10

1 - 10

42 2, 3, 7, 8

43 1, 2, 6, 7

44 2, 7

45 1, 6

46

FAP20

1 - 10

47 2, 3, 7, 8

48 1, 2, 6, 7

49 2, 7

50 1, 6

51

FAP30

1 - 10

52 2, 3, 7, 8

53 1, 2, 6, 7

54 2, 7

55 1, 6

56

Control

1 - 10

57 2, 3, 7, 8

58 1, 2, 6, 7

59 2, 7

60 1, 6

7
3.6. Scoring Methodology

The test cases in this study were executed as an open-set 1:N (1 to N, also known as one to many) matching 

scenario where a preset portion of subjects in the 1:N search actually have an enrolled mate in the gallery, and 

the probe set contains both mated and non-mated searches.

Mated searches are defined as probes for a given subject that is already enrolled in the gallery, and are expected 

to return a candidate list. Expected matches not returned, or returned with a score below the match score 

threshold are considered to be false negatives and are used in calculating the False Negative Identification Rate 

(FNIR). Given N enrolled subjects, with L candidates in the candidate list returned by the system using a 

threshold of T and rank of R (in this paper, R is always equal to 1, and L is always equal to 20), FNIR is defined 

as:

FNIR (N, R, T, L) =

Number of mate candidate searches where

candidate′s mate is below threshold T or below rank R

Number of mate candidate lists, SM

Non-mated searches are defined as probes for a given subject that has no corresponding mated record enrolled 

in the gallery, and searches using these non-mate probes should not return a candidate list with any score at or 

above the match score threshold T. Candidate lists returned with a score at or above the match threshold are 

considered to be false positives and are used in calculating the False Positive Identification Rate of this system 

(FPIR). Given N enrolled subjects, a candidate list of length L, and using a threshold of T, FPIR is defined as:

FPIR (N, T, L) =

Number of non − mate candidate lists with one or more

candidates at or above threshold T

Number of non − mate candidate lists, SNM

3.7. System Score Thresholds of Interest

The system is designed and configured to support multiple decisional thresholds. These thresholds are designed 

to classify results of searches into categories that dictate a match success or failure, as well as candidates that 

would warrant further adjudication by additional processing steps such as human examiner review.

8
For the purpose of this study, the RISC subsystem was measured according to the RISC performance thresholds 

listed below in Table 6.

Table 6 - Scoring Threshold Values

Threshold Value Threshold Name Notes

2000 RISC Low Confidence Hit A candidate match with a score at or above the low 

confidence threshold, but below the high confidence 

threshold, is considered a possible match, also referred to 

as a “Yellow” hit ().

A candidate match with a score below the low confidence 

threshold is considered a non-match, also referred to as a 

“Green” non-hit ().

5000 RISC High Confidence Hit Typically, a candidate match with a score at or above a high 

confidence threshold is considered a match with no further 

adjudication or review necessary as part of the initial match 

process. This is also referred to as a “Red” hit ().

12 000 IDFP7 Low Confidence Hit A score at or above the low confidence threshold on the 

ten-print matching system indicates candidacy; however 

some adjudication or review may be required.

25 000 IDFP High Confidence Hit A score at or above the high confidence threshold on the 

ten-print matching system indicates a fully lights-out 

candidacy decision with no adjudication, special processing 

or reviews required.

3.8. Distance Based Comparison and Ranking

In addition to the individual performance measures of FNIR and FPIR, this study also utilizes a distance-based 

strategy for the comparison or ranking of performance by calculating a composite measure from FNIR and FPIR. 

This distance-based measure is defined as

d = �FNIR2 + FPIR2

This formula is based on the calculation of distances in Euclidean space between the FPIR and FNIR value for a 

given test case to the Euclidian origin of (0,0) which for error tradeoff corresponds to the optimal condition of 

0 % FPIR and 0 % FNIR and represents the theoretical best-possible case. Therefore the lower the calculated d

is, the closer the test case in question is to the theoretical best-possible case, and the better the performance for 

that given case.

It should be noted that like data analysis methods involving Equal Error Rates (EER), the distance based method 

defined here treats both error types equally and does not apply any weights favoring either FPIR or FNIR.

7 The acronym IDFP or IdFP is commonly used to denote the FBI ten-print fingerprint identification system and 

associated processes. Although the IDFP system and the RISC system have basic design commonalities, they 

are quite distinct in terms of behavior, and have different characteristics.

9

 
3.9. System Throughput Calculation Methodology

Due to the size of the candidate list diagnostic log files and the amount of computational resources required to 

capture, process and extract throughput data for all candidate lists (approximately 400 terabytes of data for all 

test cases in this study), it was not possible to generate and process diagnostic log files for all 100 000 probes for 

each of the 60 test cases in the timeframe for this study. Instead, a subset of 6000 probes was randomly selected 

(3000 mates and 3000 non-mates) for each of the 60 cases examined, and timing data was calculated from this

randomly selected subset.

The following caveats and conditions apply to the throughput portion of this study:

• Timing data was calculated as reported by the system for each candidate list returned in a diagnostic log 

file.

• System timing data was returned at a reported resolution of 1 milliseconds (ms) and inclusive of any/all 

processing overhead for each candidate search. 

• No attempt was made to measure or remove I/O overhead resulting from normal system processing.

• The computational environment used for this study was isolated physically and logically from the 

general computing environment.

• No general processes were executed on the machines involved in this study while the throughput test 

was being conducted.

• System initialization and initial start-up times were not factored in measuring matcher performance. 

The test was commenced with the system fully initialized and at idle.

• The machines utilized for this study were time synchronized to a single machine within the 

environment.

10
4. Results

The system gallery was initialized and probe sets were injected into the system for matching according to each of 

the 60 test case outlined in Table 5.

The resulting candidate lists for each test case were then scored according to the methodology described in 

section 3.6 and aggregate scores calculated where for each of the 60 test cases in Table 5, a set of FPIR and FNIR

measurements were calculated for each of the scoring thresholds of interest described in Table 6.

A summary of all aggregate scores is provided in Table 16 of Appendix A.

11
4.1. Objective 1: Baseline IDFP System Performance Measurement

The first objective of this study set out to establish how the system performs using identification-flat probes that 

have not been cropped in any way, and are matched against a standard full-set FD-249 gallery. This establishes

baseline matcher performance inclusive of any bias that may be present in the data selected for this study.

The thresholds used for this examined case are specific to IDFP operation and differ from the RISC configuration; 

these IDFP thresholds are found in the third and fourth entries in Table 6. 

All matcher-based RISC optimizations were disabled for this case to establish a baseline.

4.1.1. Data for Objective 1

Table 7 - Baseline IDFP System Performance

Test Parameter Test Value

Case Description Control Case (IDFP, No RISC optimization)

Gallery Configuration Rolled and Flat (FD-249)

Gallery Size (Identities) 1 615 228 

Probe Set Total (Identities, total count) 100 000 

Probes (Mated count) 50 000 

Probes (Non-Mated count) 50 000 

Probe Impression Type ID Flat (Control, Not Cropped)

Low Confidence Threshold (Yellow) High Confidence Threshold (Red)

False Negative Identification Rate (FNIR) 1.260 % 14.814 %

False Positive Identification Rate (FPIR) 0.000 % 0.000 %

Median Image Search Time (ms) Median Template Search Time (ms)

Processing Time8 (Mated Probes) 3186 1739

Processing Time (Non-Mated Probes) 3096 1918

4.1.2. Results for Objective 1

The system achieved an FNIR of 1.260 % at the low confidence threshold (yellow) and 14.814 % at the high 

confidence/lights-out threshold (red) set in this study. FPIR was at 0 % for both thresholds (yellow and red)

examined.

8 Throughput measurement testing was conducted using a subset of 6000 probes randomly selected from the 

100 000 probe set. See section 3.9 for more information.

12

 
4.2. Objective 2: Two Finger RISC Scenario Comparison

The next objective in this study was to measure the effectiveness of the system matching performance when 

configured in RISC mode and operating on probe data meeting various Mobile ID finger acquisition profiles

against a gallery of complete FD-249 image sets. The specific Mobile ID case examined for this objective is that of 

the two finger capture, typically from the left and right index fingers (2, 7) and is the predominant case for RISC

in the field.

4.2.1. Data for Objective 2

Table 8 provides the data for this study objective and ranks the values from 1 (best) to 4 (worst) according to 

the distance-from-origin the methodology described in section 3.8 for each of the performance metrics (FPIR and 

FNIR), at both system confidence thresholds (yellow and red). 

Table 8 - Matcher Performance by Mobile ID FAP, Two Finger Probe Scenario (2, 7)

Gallery TypeProbe Finger ConfigurationFAP ConfigurationFNIRLow Confidence (

)

FNIRHigh Confidence ()FPIRLow Confidence (

)

FPIRHigh Confidence ()Distance to OptimalLow Confidence (

) 

Distance to OptimalHigh Confidence ()Distance Rank Low Confidence (

) 

Distance Rank High Confidence ()

FD-249 2,7 FAP30 0.400 % 1.616 % 0.356 % 0.000 % 0.0054 0.0162 1 1

FD-249 2,7 control 0.472 % 1.640 % 0.368 % 0.000 % 0.0060 0.0164 2 2

FD-249 2,7 FAP20 0.506 % 1.844 % 0.422 % 0.000 % 0.0066 0.0184 3 3

FD-249 2,7 FAP10 1.164 % 3.272 % 0.340 % 0.000 % 0.0121 0.0327 4 4

Figure 2 shows summary data from Table 8 for the two print case of (2, 7) using a full FD-249 gallery with the

matcher and with RISC optimizations enabled.

Figure 2 - Matcher Performance by Mobile ID FAP, Two Finger Probe Scenario (2, 7)

13
Figure 3 - Error Tradeoff for Various Mobile ID FAPs in Two Finger (2, 7) Probes vs. FD-249 Gallery

4.2.2. Results for Objective 2

For the two finger Mobile ID cases using the left and right index fingers (2, 7), FAP10 appears to be at a 

disadvantage when compared to other FAPs in terms of FNIR where FAP10 submissions fail to identify their 

mated target almost twice as often as FAP20 (1.164 % vs. 0.506 %) at the Low Confidence system threshold

(yellow hit). The same behavior holds in the high confidence system threshold (red hit) scenario where FNIR for 

FAP10 is almost twice as great as that of FAP20 (3.270 % vs. 1.844 %). Error tradeoff analysis using the 

distance-from-origin methodology places the FAP10 case as last-ranked and worst for both low and high 

confidence FNIR, while FAP30 is best.

FPIR rates for all cases is flat at 0 % at the high confidence threshold (red hit), and none of the Mobile ID FAPs 

show any disadvantage over the others in the high confidence scenario. For the low confidence system threshold 

scenario (yellow hit), the difference between matcher performance is very small with FAP10 showing a very 

slight advantage over FAP30 (0.340 % vs. 0.356 %).

Using the distance-to-origin methodology to examine the error tradeoff between FPIR and FNIR shows that 

FAP30 excels over all other Mobile ID two finger (2,7) FAPs. 

What was surprising here was that FAP30 outperformed the control case, though by a very small margin. While 

this difference in performance is small (i.e., an FNIR of 1.616 % vs 1.640 % for the two finger (2,7) case at the 

high confidence threshold) this difference is statistically significant (p<0.0001 level, using Wilcoxon signed-rank 

test) and this pattern was observed in several other case clusters where FAP30 outperformed the control case. 

14
4.3. Objective 3: Impact of Various Probe Finger Combinations

The next objective in this study was to measure system matching performance when configured in RISC mode 

and using various combinations of fingers for Mobile ID searches against a gallery of complete FD-249 image 

sets. The finger combinations examined for this objective are described in Table 3.

4.3.1. Data for Objective 3

Table 9 summarizes the data from this objective utilizing all finger combinations described in Table 3 using a 

full FD-249 gallery with the matcher in RISC optimization mode, and ranks the values from 1 (best) to 20 (worst) 

using distance-to-optimal calculations (section 3.8) for each of the performance metrics (FPIR and FNIR) at both 

system confidence thresholds (High/Red and Low/Yellow). Figure 4 and Figure 5 show summary data from 

Table 9, for FPIR and FNIR respectively.

Table 9 - System Performance by Various Probe Finger Combinations

Gallery TypeProbe Finger ConfigurationFAP ConfigurationFNIRLow Confidence ()FNIRHigh Confidence ()FPIRLow Confidence ()FPIRHigh Confidence ()Distance to OptimalLow Confidence ()Distance to OptimalHigh Confidence ()Distance Rank Low Confidence ()Distance Rank High Confidence ()

FD-249 1-10 FAP30 0.098 % 0.206 % 0.054 % 0.000 % 0.0011 0.0021 1 1

FD-249 1-10 control 0.134 % 0.218 % 0.052 % 0.000 % 0.0014 0.0022 2 2

FD-249 1-10 FAP20 0.190 % 0.306 % 0.060 % 0.000 % 0.0020 0.0031 3 3

FD-249 2,3,7,8 FAP30 0.160 % 0.338 % 0.548 % 0.000 % 0.0057 0.0034 8 4

FD-249 2,3,7,8 control 0.192 % 0.374 % 0.546 % 0.000 % 0.0058 0.0037 9 5

FD-249 1,2,6,7 FAP30 0.216 % 0.390 % 0.122 % 0.000 % 0.0025 0.0039 4 6

FD-249 1,2,6,7 control 0.226 % 0.410 % 0.158 % 0.000 % 0.0028 0.0041 5 7

FD-249 2,3,7,8 FAP20 0.236 % 0.442 % 0.650 % 0.000 % 0.0069 0.0044 12 8

FD-249 1,2,6,7 FAP20 0.464 % 0.720 % 0.128 % 0.000 % 0.0048 0.0072 6 9

FD-249 2,7 FAP30 0.400 % 1.616 % 0.356 % 0.000 % 0.0054 0.0162 7 10

FD-249 2,7 control 0.472 % 1.640 % 0.368 % 0.000 % 0.0060 0.0164 10 11

FD-249 2,3,7,8 FAP10 1.372 % 1.714 % 0.622 % 0.000 % 0.0151 0.0171 14 12

FD-249 2,7 FAP20 0.506 % 1.844 % 0.422 % 0.000 % 0.0066 0.0184 11 13

FD-249 2,7 FAP10 1.164 % 3.272 % 0.340 % 0.000 % 0.0121 0.0327 13 14

FD-249 1,6 control 1.890 % 3.560 % 0.046 % 0.000 % 0.0189 0.0356 15 15

FD-249 1,6 FAP30 1.964 % 3.598 % 0.090 % 0.000 % 0.0197 0.0360 16 16

FD-249 1-10 FAP10 3.740 % 3.940 % 0.034 % 0.000 % 0.0374 0.0394 19 17

FD-249 1,2,6,7 FAP10 3.658 % 4.040 % 0.146 % 0.000 % 0.0366 0.0404 18 18

FD-249 1,6 FAP20 2.620 % 5.130 % 0.106 % 0.000 % 0.0262 0.0513 17 19

FD-249 1,6 FAP10 5.938 % 10.402 % 0.208 % 0.000 % 0.0594 0.1040 20 20

15
Figure 4 - False Positive Identification Rates by Probe Finger Combination

Figure 5 - False Negative Identification Rates by Probe Finger Combination

Figure 6 - Error Tradeoff for Various Mobile ID FAPs and Finger Combinations vs. FD-249 Gallery

16
Figure 7 - Error Tradeoff for Individual Mobile ID FAPs and Finger Combinations vs. FD-249 Gallery

4.3.2. Results for Objective 3

The data from this study objective shows that FAP30 (1-10) finger case excels, followed by the Control (1-10) 

finger case, and the FAP20 (1-10) finger case when compared to a full FD-249 gallery, for both the high and low 

confidence thresholds. 

On the other end of the scale, FAP10 (1,6) two finger case ranks last in performance for both the high and low 

confidence thresholds.

At the high confidence system threshold FPIR appears flat at 0 % for all cases and is not impacted by the various 

finger combination treatments. At the low confidence system threshold, FPIR does show some fluctuation but 

the fluctuation is generally small with all values below 1 %.

17
FNIR performance appears widely impacted by the number of fingers and favors cases where the search probe 

had more finger impressions. FNIR at the high confidence system threshold ranges from 0.206 % for the FAP30 

(1-10) finger combination treatment, to 10.402 % for the FAP10 (1,6) finger combination treatment. To further 

emphasize the positive impact on the system’s FNIR when additional fingers are used, it can be seen in Table 9

that 9 out of the 10 top-ranked cases were those that had four or ten finger probes.

FNIR performance at the low confidence threshold nearly mimics the high confidence performance, with the 

FNIR rates better than halved (i.e., 0.098 % vs. 0.206 %) for the (1-10) FAP30 case which emerged as the best.

18
4.4. Objective 4: Impact of Gallery Configuration on Two Finger FAPs

The next objective in this study was to measure system matching performance when configured in RISC mode 

and using various gallery combinations listed in Table 4 for the two finger (2,7) combination case.

4.4.1. Data for Objective 4

Table 10 below provides data from tests utilizing the (2,7) finger combination for all Mobile ID FAPs described 

in Table 1 versus each gallery described in Table 4 with the matcher in RISC optimization mode. The data in 

Table 10 has been sorted according to rank values ranging from 1 (best) to 12 (worst) for each of the 

performance metrics (FPIR and FNIR) and both system confidence thresholds (High and Low). A summary graph 

is provided as Figure 8 as well as error trade-off charts Figure 9 (all FAPs in a single graph), and Figure 10

where error tradeoffs are provided separately for each FAP.

Table 10 - System Performance by Various Probe Gallery Combinations, Two Finger Probe Scenario (2, 7) Gallery Type FAP Configuration FNIR Low Confidence (

)

FNIRHigh Confidence ()FPIRLow Confidence (

)

FPIRHigh Confidence ()Distance to OptimalLow Confidence (

) 

Distance to OptimalHigh Confidence ()Distance Rank Low Confidence (

) 

Distance Rank High Confidence ()

FD-249 Gallery FAP30 0.400 % 1.616 % 0.356 % 0.000 % 0.0054 0.0162 1 1

FD-249 Gallery Control 0.472 % 1.640 % 0.368 % 0.000 % 0.0060 0.0164 2 2

FD-249 Gallery FAP20 0.506 % 1.844 % 0.422 % 0.000 % 0.0066 0.0184 3 3

FD-249 Gallery FAP10 1.164 % 3.272 % 0.340 % 0.000 % 0.0121 0.0327 4 4

Rolled-Only Gallery FAP30 1.318 % 3.862 % 0.222 % 0.000 % 0.0134 0.0386 5 5

Flat-Only Gallery FAP30 1.570 % 4.032 % 0.396 % 0.000 % 0.0162 0.0403 7 6

Rolled-Only Gallery Control 1.588 % 4.078 % 0.284 % 0.000 % 0.0161 0.0408 6 7

Flat-Only Gallery Control 1.726 % 4.138 % 0.406% 0.000 % 0.0177 0.0414 8 8

Flat-Only Gallery FAP20 1.766 % 4.474 % 0.462 % 0.000 % 0.0183 0.0447 10 9

Rolled-Only Gallery FAP20 1.806 % 4.494 % 0.236 % 0.000 % 0.0182 0.0449 9 10

Flat-Only Gallery FAP10 2.806 % 6.694 % 0.374 % 0.000 % 0.0283 0.0669 11 11

Rolled-Only Gallery FAP10 4.024 % 8.048 % 0.294 % 0.000 % 0.0403 0.0805 12 12

19
Figure 8 - Matcher Performance by Gallery Configuration, Two Finger Probe Scenario (2, 7)

20
Figure 9 - Error Tradeoff for All Two Finger Mobile ID FAPs (2, 7) vs. Various Gallery Configurations

21
Figure 10 - Error Tradeoff for Each Two Finger Mobile ID FAP (2, 7) vs. Various Gallery Configurations

4.4.2. Results for Objective 4

For the two finger probe field scenario (2,7) examined in this study objective, a full FD-249 set gallery emerges as 

the best gallery where all four FD-249 gallery test scenarios emerged at the top of the performance rankings (see 

Table 10), with FAP30 leading the pack at both low and high confidence thresholds (FNIR of 0.400 % and 

1.616 % respectively). A rolled-only gallery favors optimization of FPIR for all Mobile ID FAPs at the low 

confidence threshold with the four lowest FPIR cases being the result of a rolled-only gallery configuration, and 

FAP30 again leading the pack with the lowest FPIR rate. At the high confidence system threshold, FPIR is flat at 

0 % for all cases. 

22
4.5. Objective 5: System throughput behavior

The next objective in this study was to measure the throughput of the system using various Mobile ID FAP 

scenarios using the full set FD-249 gallery. In addition to the various image-based scenarios, template-only 

based scenarios were also tested to provide a basis of throughput without the image processing overhead 

involved. The templates generated by the system were proprietary to the system and generated on the system 

environment prior to conducting the test. Finally, for the purpose of comparison, ten-print submissions using 

the system in both RISC and IDFP configuration were made and the throughput measured.

4.5.1. Data for Objective 5

Table 11 and Figure 11 provide median match times for the system processing of various Mobile ID FAP 

scenarios (image-only as well as template-only probe sets) using the full set FD-249 gallery, while Table 12 and 

Figure 12 provide median match times for system processing of various image-only Mobile ID FAP scenarios and 

finger combinations against the full set FD-249 gallery.

Table 11 - Median System Matching Times (milliseconds) for Selected FAPs vs. FD-249 Gallery

Probe Type FAP10 (2,7) FAP20 (2,7) FAP30 (2,7) Control (2,7) Control (1-10), RISC Control (1-10), IDFP

Image-only Probe Non-mated 1966 2370 3059 2690 4336 3096

Mated 1841 2251 2964 2568 3362 3186

Template-only Probe Non-mated 763 807 936 851 2099 1918

Mated 663 707 870 756 1293 1739

Figure 11 - Median Matching Times (milliseconds) for Selected FAPs vs. FD-249 Gallery

23
Table 12 - Median Matching Times (milliseconds) for Image-Only Searches vs. Finger Combination

Finger Combination

Probe Type 1, 6 2, 7 1, 2, 6, 7 2, 3, 7, 8 1 - 10

FAP10 Non-mated 3068 1966 2155 2205 3377

Mated 2942 1841 1945 1996 2050

FAP20 Non-mated 3973 2370 2675 2664 3671

Mated 3928 2251 2505 2483 2537

FAP30 Non-mated 5323 3059 3713 3518 4400

Mated 5345 2964 3530 3327 3426

Control Non-mated 5445 2690 3675 3119 4336

Mated 5467 2568 3481 2935 3362

Figure 12 - Median Matching Times (milliseconds) for Image-Only Searches vs. Finger Combination

24
Table 13 - Strictly Increasing FAP Median Match Times (FAP10 < FAP20 < FAP30) for Various Scenarios

Finger Combination Where Median Match Time for FAP10 < FAP20 < FAP30

Probe Contents Probe Matching Gallery 1, 6 2, 7 1, 2, 6, 7 2, 3, 7, 8 1 - 10

Image Non-mated FD249 TRUE TRUE TRUE TRUE TRUE

Image Non-mated Flat TRUE TRUE TRUE TRUE TRUE

Image Non-mated Rolled TRUE TRUE TRUE TRUE TRUE

Image Mated FD249 TRUE TRUE TRUE TRUE TRUE

Image Mated Flat TRUE TRUE TRUE TRUE TRUE

Image Mated Rolled TRUE TRUE TRUE TRUE TRUE

Template Non-mated FD249 TRUE TRUE TRUE TRUE FALSE

Template Non-mated Flat TRUE TRUE TRUE TRUE FALSE

Template Non-mated Rolled TRUE TRUE TRUE TRUE FALSE

Template Mated Flat TRUE TRUE TRUE TRUE FALSE

Template Mated FD249 TRUE TRUE TRUE TRUE FALSE

Template Mated Rolled TRUE TRUE TRUE TRUE FALSE

4.5.2. Results for Objective 5

System match times appear to be linked to image spatial geometry where median search time for all image 

searches FAPs strictly increases from FAP10 to FAP30. This strictly increasing pattern is also present in all 

template searches except for the ten-print template search cases where the FAP10 cases appear to incur a larger 

penalty than FAP20. The strictly increasing behavior is not consistent however between FAP30 and the Control 

case where FAP30 incurs a slight penalty versus the uncropped control case.

As noted before, the two thumbs (1,6) case again appears to exhibit a significant penalty, with searches taking 

nearly twice as long as other finger combinations for a given FAP.

25
4.6. Objective 6: Identifying an Optimal Strategy

The next objective in this study was to organize and rank all test cases together where data from all Mobile ID 

FAPs, matched against all gallery configurations and finger combinations was tabulated into a single data table 

(Table 14). This data was then assigned a rank based on the methodology described in section 3.8. This data 

allows for comparison of heterogeneous configurations (e.g., Mobile ID FAP10 using an FD-249 gallery vs. Mobile 

ID FAP30 using a flat-only gallery) at a glance. 

Performance data (FPIR and FNIR) for each of the experimental treatments were each assigned a rank ranging 

from 1 (best) to 60 (worst) for each of FPIR and FNIR, at both low and high confidence threshold. The composite

table of ranks and performance data is presented in Table 14 and an error trade-off graph is presented in Figure 

13.

26
4.6.1. Data for Objective 6

Table 14 - Performance Data Ranked by Distance-to-Optimal for Error Trade-off

Gallery TypeProbe Finger ConfigurationFAP ConfigurationFNIRLow Confidence (

)

FNIRHigh Confidence ()FPIRLow Confidence (

)

FPIRHigh Confidence ()Distance to OptimalLow Confidence (

) 

Distance to OptimalHigh Confidence ()Distance Rank Low Confidence (

) 

Distance Rank High Confidence ()FNIRLow Confidence (

)

FNIRHigh Confidence ()

FD-249 1-10 FAP30 0.098 % 0.206 % 0.054 % 0.000 % 0.0011 0.0021 1 1 L1 H1

FD-249 1-10 Control 0.134 % 0.218 % 0.052 % 0.000 % 0.0014 0.0022 2 2 L2 H2

FD-249 1-10 FAP20 0.190 % 0.306 % 0.060 % 0.000 % 0.0020 0.0031 3 3 L3 H3

FD-249 2,3,7,8 FAP30 0.160 % 0.338 % 0.548 % 0.000 % 0.0057 0.0034 10 4 L10 H4

FD-249 2,3,7,8 Control 0.192 % 0.374 % 0.546 % 0.000 % 0.0058 0.0037 11 5 L11 H5

FD-249 1,2,6,7 FAP30 0.216 % 0.390 % 0.122 % 0.000 % 0.0025 0.0039 4 6 L4 H6

FD-249 1,2,6,7 Control 0.226 % 0.410 % 0.158 % 0.000 % 0.0028 0.0041 5 7 L5 H7

FD-249 2,3,7,8 FAP20 0.236 % 0.442 % 0.650 % 0.000 % 0.0069 0.0044 16 8 L16 H8

Flat 1-10 FAP30 0.538 % 0.662 % 0.060 % 0.000 % 0.0054 0.0066 8 9 L8 H9

FD-249 1,2,6,7 FAP20 0.464 % 0.720 % 0.128 % 0.000 % 0.0048 0.0072 6 10 L6 H10

Rolled 1-10 FAP30 0.564 % 0.768 % 0.062 % 0.000 % 0.0057 0.0077 9 11 L9 H11

Flat 1-10 Control 0.686 % 0.786 % 0.066 % 0.000 % 0.0069 0.0079 15 12 L15 H12

Rolled 1-10 Control 0.688 % 0.868 % 0.036 % 0.000 % 0.0069 0.0087 14 13 L14 H13

Rolled 2,3,7,8 FAP30 0.652 % 1.010 % 0.258 % 0.000 % 0.0070 0.0101 17 14 L17 H14

Flat 1-10 FAP20 0.946 % 1.084 % 0.052 % 0.000 % 0.0095 0.0108 21 15 L21 H15

Flat 1,2,6,7 FAP30 0.796 % 1.094 % 0.138 % 0.000 % 0.0081 0.0109 18 16 L18 H16

Flat 2,3,7,8 FAP30 0.714 % 1.124 % 0.714 % 0.000 % 0.0101 0.0112 23 17 L23 H17

Flat 1,2,6,7 Control 0.888 % 1.188 % 0.158 % 0.000 % 0.0090 0.0119 19 18 L19 H18

Rolled 2,3,7,8 Control 0.892 % 1.194 % 0.344 % 0.000 % 0.0096 0.0119 22 19 L22 H19

Flat 2,3,7,8 Control 0.848 % 1.214 % 0.644 % 0.000 % 0.0106 0.0121 25 20 L25 H20

Rolled 1-10 FAP20 1.030 % 1.230 % 0.028 % 0.000 % 0.0103 0.0123 24 21 L24 H21

Rolled 1,2,6,7 FAP30 0.904 % 1.234 % 0.098 % 0.000 % 0.0091 0.0123 20 22 L20 H22

Flat 2,3,7,8 FAP20 0.936 % 1.342 % 0.664 % 0.000 % 0.0115 0.0134 28 23 L28 H23

Rolled 2,3,7,8 FAP20 1.016 % 1.390 % 0.356 % 0.000 % 0.0108 0.0139 26 24 L26 H24

Rolled 1,2,6,7 Control 1.114 % 1.420 % 0.108 % 0.000 % 0.0112 0.0142 27 25 L27 H25

FD-249 2,7 FAP30 0.400 % 1.616 % 0.356 % 0.000 % 0.0054 0.0162 7 26 L7 H26

FD-249 2,7 Control 0.472 % 1.640 % 0.368 % 0.000 % 0.0060 0.0164 12 27 L12 H27

FD-249 2,3,7,8 FAP10 1.372 % 1.714 % 0.622 % 0.000 % 0.0151 0.0171 32 28 L32 H28

Flat 1,2,6,7 FAP20 1.364 % 1.734 % 0.166 % 0.000 % 0.0137 0.0173 31 29 L31 H29

FD-249 2,7 FAP20 0.506 % 1.844 % 0.422 % 0.000 % 0.0066 0.0184 13 30 L13 H30

Rolled 1,2,6,7 FAP20 1.838 % 2.238 % 0.104 % 0.000 % 0.0184 0.0224 38 31 L38 H31

Flat 2,3,7,8 FAP10 2.660 % 3.260 % 0.704 % 0.000 % 0.0275 0.0326 42 32 L42 H32

FD-249 2,7 FAP10 1.164 % 3.272 % 0.340 % 0.000 % 0.0121 0.0327 29 33 L29 H33

FD-249 1,6 Control 1.890 % 3.560 % 0.046 % 0.000 % 0.0189 0.0356 39 34 L39 H34

FD-249 1,6 FAP30 1.964 % 3.598 % 0.090 % 0.000 % 0.0197 0.0360 40 35 L40 H35

Rolled 2,7 FAP30 1.318 % 3.862 % 0.222 % 0.000 % 0.0134 0.0386 30 36 L30 H36

FD-249 1-10 FAP10 3.740 % 3.940 % 0.034 % 0.000 % 0.0374 0.0394 45 37 L45 H37

Flat 2,7 FAP30 1.570 % 4.032 % 0.396 % 0.000 % 0.0162 0.0403 34 38 L34 H38

FD-249 1,2,6,7 FAP10 3.658 % 4.040 % 0.146 % 0.000 % 0.0366 0.0404 44 39 L44 H39

Rolled 2,7 Control 1.588 % 4.078 % 0.284 % 0.000 % 0.0161 0.0408 33 40 L33 H40

Flat 2,7 Control 1.726 % 4.138 % 0.406 % 0.000 % 0.0177 0.0414 35 41 L35 H41

Flat 2,7 FAP20 1.766 % 4.474 % 0.462 % 0.000 % 0.0183 0.0447 37 42 L37 H42

Rolled 2,7 FAP20 1.806 % 4.494 % 0.236 % 0.000 % 0.0182 0.0449 36 43 L36 H43

Rolled 2,3,7,8 FAP10 4.212 % 4.768 % 0.462 % 0.000 % 0.0424 0.0477 49 44 L49 H44

FD-249 1,6 FAP20 2.620 % 5.130 % 0.106 % 0.000 % 0.0262 0.0513 41 45 L41 H45

Flat 1,6 Control 3.846 % 6.032 % 0.064 % 0.000 % 0.0385 0.0603 46 46 L46 H46

Flat 1,6 FAP30 3.930 % 6.124 % 0.082 % 0.000 % 0.0393 0.0612 47 47 L47 H47

Flat 1,2,6,7 FAP10 5.786 % 6.334 % 0.182 % 0.000 % 0.0579 0.0633 53 48 L53 H48

Flat 2,7 FAP10 2.806 % 6.694 % 0.374 % 0.000 % 0.0283 0.0669 43 49 L43 H49

Flat 1-10 FAP10 6.438 % 6.706 % 0.036 % 0.000 % 0.0644 0.0671 56 50 L56 H50

Rolled 1,6 Control 4.298 % 7.560 % 0.054 % 0.000 % 0.0430 0.0756 50 51 L50 H51

Rolled 1,6 FAP30 4.380 % 7.630 % 0.076 % 0.000 % 0.0438 0.0763 51 52 L51 H52

Rolled 1-10 FAP10 7.539 % 7.899 % 0.064 % 0.000 % 0.0754 0.0790 57 53 L57 H53

Rolled 2,7 FAP10 4.024 % 8.048 % 0.294 % 0.000 % 0.0403 0.0805 48 54 L48 H54

Rolled 1,2,6,7 FAP10 7.704 % 8.372 % 0.164 % 0.000 % 0.0771 0.0837 58 55 L58 H55

Flat 1,6 FAP20 5.268 % 8.504 % 0.132 % 0.000 % 0.0527 0.0850 52 56 L52 H56

FD-249 1,6 FAP10 5.938 % 10.402 % 0.208 % 0.000 % 0.0594 0.1040 55 57 L55 H57

Rolled 1,6 FAP20 5.860 % 10.850 % 0.080 % 0.000 % 0.0586 0.1085 54 58 L54 H58

Flat 1,6 FAP10 11.122 % 16.350 % 0.218 % 0.000 % 0.1112 0.1635 60 59 L60 H59

Rolled 1,6 FAP10 10.610 % 18.566 % 0.176 % 0.000 % 0.1061 0.1857 59 60 L59 H60

27
Figure 13 - Error Tradeoff for all Test Cases9

9 See Table 14 for legend key

28

 
4.6.2. Results for Objective 6

Using the data in Table 14/Figure 13 spanning all 60 cases examined in this study, the case of FAP30 two finger 

(2,7) using the FD-249 full set gallery emerges as the best two finger FAP for the high confidence system 

threshold in terms of error trade-off with a distance-to-optimal of 0.0162. The highest ranking FAP10 case when 

using an FD-249 database is that of four finger (2,3,7,8) FAP10 with a distance-to-origin of 0.0171 ranking it 

higher than FAP20 two finger (2,7) but lower than both two finger (2,7) FAP30 and two finger (2,7) control 

cases. Given this information, in cases where legacy FAP10 hardware needs to be used, FAP10 (2,3,7,8) with 

d=0.0171 may be a competitive alternative in terms of error trade-off with the top ranking case of FAP30 two 

finger (2,7) with d=0.0162.

Looking from the bottom end of the performance rankings, the two finger (1,6) occupies the lowest rankings 

with all gallery configurations, with the two finger (1,6) FAP10 case coming in dead last among all 60 cases 

examined for the high confidence system threshold. 

29
4.7. Objective 7: Exploratory Examination of Emerging Small-Platen Capture Devices

As mobile/handheld information devices mature and evolve, the market has seen a gradual proliferation of 

biometric technology in these devices primarily for access control either as a replacement for or as an 

augmentation of traditional password and/or gesture based security measures. These biometric technologies 

include a new generation of very-small platen devices such as the fingerprint sensor embedded in the iPhone10

5s (see Figure 14). While such sensors are intended for verification purposes (also referred to as one to one, or 

1:1) and the usage of these very-small sensors for one-to-many searching (1:N) has not been endorsed by any 

vendors, this study included an exploratory examination on the use of such a small platen device in a one-tomany biometric-search scenario (1:N). This was done by creating a 1⁄4” (6.35mm) square fingerprint image and 

testing that image using the same protocols that other Mobile ID FAPs were tested. The gallery used for this test 

was populated with full FD-249 image sets and the system was configured with RISC system optimization.

Figure 14 - Example of an Embedded Small Biometric Verification Sensor Such as the iPhone10 5S

4.7.1. Data for Objective 7

Figure 15 below shows summary data from tests utilizing various finger combination for all Mobile ID FAPs 

described in Table 1 plus the experimental 1/4" FAP data. Table 14 summarizes the data in Figure 15, and 

ranks the values from 1 (best) to 25 (worst) for each of the performance metrics (FPIR and FNIR) and both 

system confidence thresholds (High and Low) using the distance-to-origin methodology described in section 3.8.

10 iPhone is a registered trademark of Apple Inc., registered in the U.S. and other countries.

30

 
Table 15 - Comparison of Experimental 1/4" FAP Versus Traditional Mobile ID FAPs

Gallery TypeProbe Finger ConfigurationFAP ConfigurationFNIRLow Confidence (

)

FNIRHigh Confidence ()FPIRLow Confidence (

)

FPIRHigh Confidence ()Distance to OptimalLow Confidence (

) 

Distance to OptimalHigh Confidence ()Distance Rank Low Confidence (

) 

Distance Rank High Confidence ()

FD-249 1-10 FAP30 0.098 % 0.206 % 0.054 % 0.000 % 0.0011 0.0021 1 1

FD-249 1-10 Control 0.134 % 0.218 % 0.052 % 0.000 % 0.0014 0.0022 2 2

FD-249 1-10 FAP20 0.190 % 0.306 % 0.060 % 0.000 % 0.0020 0.0031 3 3

FD-249 2,3,7,8 FAP30 0.160 % 0.338 % 0.548 % 0.000 % 0.0057 0.0034 8 4

FD-249 2,3,7,8 Control 0.192 % 0.374 % 0.546 % 0.000 % 0.0058 0.0037 9 5

FD-249 1,2,6,7 FAP30 0.216 % 0.390 % 0.122 % 0.000 % 0.0025 0.0039 4 6

FD-249 1,2,6,7 Control 0.226 % 0.410 % 0.158 % 0.000 % 0.0028 0.0041 5 7

FD-249 2,3,7,8 FAP20 0.236 % 0.442 % 0.650 % 0.000 % 0.0069 0.0044 12 8

FD-249 1,2,6,7 FAP20 0.464 % 0.720 % 0.128 % 0.000 % 0.0048 0.0072 6 9

FD-249 2,7 FAP30 0.400 % 1.616 % 0.356 % 0.000 % 0.0054 0.0162 7 10

FD-249 2,7 Control 0.472 % 1.640 % 0.368 % 0.000 % 0.0060 0.0164 10 11

FD-249 2,3,7,8 FAP10 1.372 % 1.714 % 0.622 % 0.000 % 0.0151 0.0171 14 12

FD-249 2,7 FAP20 0.506 % 1.844 % 0.422 % 0.000 % 0.0066 0.0184 11 13

FD-249 2,7 FAP10 1.164 % 3.272 % 0.340 % 0.000 % 0.0121 0.0327 13 14

FD-249 1,6 Control 1.890 % 3.560 % 0.046 % 0.000 % 0.0189 0.0356 15 15

FD-249 1,6 FAP30 1.964 % 3.598 % 0.090 % 0.000 % 0.0197 0.0360 16 16

FD-249 1-10 FAP10 3.740 % 3.940 % 0.034 % 0.000 % 0.0374 0.0394 19 17

FD-249 1,2,6,7 FAP10 3.658 % 4.040 % 0.146 % 0.000 % 0.0366 0.0404 18 18

FD-249 1,6 FAP20 2.620 % 5.130 % 0.106 % 0.000 % 0.0262 0.0513 17 19

FD-249 1,6 FAP10 5.938 % 10.402 % 0.208 % 0.000 % 0.0594 0.1040 20 20

FD-249 1-10 1⁄4" FAP 20.122 % 47.432 % 0.000 % 0.000 % 0.2012 0.4743 21 21

FD-249 2,3,7,8 1⁄4" FAP 31.558 % 64.524 % 0.000 % 0.000 % 0.3156 0.6452 22 22

FD-249 1,2,6,7 1⁄4" FAP 33.214 % 69.905 % 0.000 % 0.000 % 0.3321 0.6991 23 23

FD-249 2,7 1⁄4" FAP 54.894 % 94.440 % 0.002 % 0.000 % 0.5489 0.9444 24 24

FD-249 1,6 1⁄4" FAP 66.687 % 97.380 % 0.002 % 0.000 % 0.6669 0.9738 25 25

Figure 15 - Error Tradeoff Comparison of Experimental 1⁄4” FAP Versus Traditional Mobile ID FAPs

31
4.7.2. Results for Objective 7

The smaller platen sensors present a significant advance in the area of authentication and verification; their 

application in one-to-many (1:N) searching may be premature given the results from this exploratory 

investigation. While the FPIR rates remain reasonable and on a par with traditional Mobile ID FAPs examined in 

this study, FNIR takes a significant performance hit when using these images. In the worst case scenario, at the 

high confidence system threshold, FNIR peaks at 97.380 % for the case of the two finger (1,6) combination using 

the experimental 1/4" FAP. In the best case scenario where all ten fingers can be captured, the experimental 

1/4" FAP yields an FNIR of 47.432 %, with an FPIR of 0.0 %.

32
5. Discussion

Examining various two finger (2,7) Mobile ID FAP scenarios with a gallery of full FD-249 fingerprint sets (section 

4.2.1) shows that FAP10 is at a disadvantage when compared to other FAPs. In terms of FNIR, FAP10 

submissions fail to identify their target twice as often as FAP20 (1.164 % vs. 0.506 %) at the system low 

confidence threshold. For the high confidence threshold, FNIR for FAP10 is almost twice that of FAP20 (3.270 %

vs. 1.844 %). Error tradeoff analysis using distance-to-optimal calculations (section 3.8) places the FAP10 case 

as last-ranked and worst for both low and high confidence FNIR, while FAP30 is best for both thresholds. A

surprise finding was that the FAP30 case outperformed the control case, though by a very small margin. While 

this performance difference is small (e.g., an FNIR of 1.616 % vs 1.640 % for the two finger (2,7) case at the high 

confidence threshold) this difference is statistically significant (p<0.0001 level, using Wilcoxon signed-rank test) 

and this pattern was observed in several other case clusters where FAP30 outperformed the control case ever so 

slightly. 

For the two finger (2,7) Mobile ID FAP scenarios with a full FD-249 gallery shows that FPIR rates for all cases is 

flat at 0 % at the high confidence threshold, and none of the Mobile ID FAPs show any disadvantage over the 

others in the high confidence scenario. For the low confidence system threshold scenario, the difference in FPIR

is very small with FAP10 showing a very negligible advantage over FAP30 (0.340 % vs. 0.356 %).

Examining cases of various finger combinations versus a full FD-249 gallery (section 4.3.1) shows that the FAP30 

(1-10) finger case excels all others, followed by the Control (1-10) finger case, and then the FAP20 (1-10) finger 

case. This ranking is true for both the high and low confidence thresholds. On the other end of the scale, the 

FAP10 (1,6) two finger case ranks last in performance for both the high and low confidence thresholds.

As with the two finger (2,7) cases, all finger combinations at the high confidence system threshold yielded FPIR 

performance that was flat at 0 % for all cases and appeared not to be impacted by the various finger combination 

treatments. At the low confidence system threshold, FPIR does show some fluctuation but the fluctuation is 

generally small with all values below 1 %.

FNIR performance is widely impacted by the number of fingers in the search probe and FNIR performance favors 

cases where the search probe had more finger impressions (e.g., 10 finger cases). FNIR at the high confidence 

system threshold ranges from 0.206 % for the FAP30 (1-10) finger combination treatment, to 10.402 % for the 

FAP10 (1,6) finger combination treatment. This effect is so marked in that FNIR for the high confidence 

threshold 9 out of 10 cases in the top half of the ranked performance were either 4 or ten finger cases. FNIR 

performance at the low confidence threshold nearly mimics the high confidence performance, with the FNIR 

rates better than halved (i.e., 0.098 % vs. 0.206 %) for the (1-10) FAP30 case which emerged as the best.

In examining the impact of gallery configuration on system performance (section 4.4.1), various Mobile ID FAPs 

in two finger (2,7) configuration were tested against several possible gallery configurations (Table 4). For all 

Mobile ID scenarios examined, a full FD-249 set gallery emerged as the best gallery where all four FD-249 test 

scenarios were at the top of the performance rankings (see Table 10) using the distance based performance 

methodology (section 3.8), with FAP30 leading the pack at both low and high confidence thresholds (FNIR of 

0.400 % and 1.616 % respectively). A rolled-only gallery favors optimization of FPIR for all Mobile ID FAPs at the 

low confidence threshold with the four lowest FPIR cases being the result of a rolled-only gallery configuration, 

and FAP30 again leading the pack with the lowest FPIR rate. At the high confidence system threshold, FPIR is flat 

at 0 % for all cases. 

Comparing the various Mobile ID FAP scenarios with a two finger (2,7) treatment and a gallery consisting of 

various configurations of the system for impact on system throughput (section 4.5.1) shows that the system 

match times appear closely linked to image spatial geometry where median search times for all image search 

FAPs consistently increases from FAP10 to FAP30. This consistently increasing pattern is also present in all 

template searches and therefore not purely linked to image processing times. An exception to this increasing 

33
pattern is the ten-print template search cases where the FAP10 cases appear to incur a larger penalty than 

FAP20, and between FAP30 and the Control case where FAP30 incurs a slight penalty versus the uncropped 

control case. It should be emphasized however that the template-only matching scenarios do not suggest the 

elimination of the template generation processes and were included in this study to demonstrate the effect of the 

smaller Mobile ID templates on system throughput behavior without the overhead of image processing. In 

normal operation, template generation processes must ultimately occur somewhere in the processing workflow 

in order to conduct a template-only match.

Utilizing a rank based strategy based on distance-from optimal/origin (section 4.6.1), the absolute best ranked

performance case is that of Mobile ID FAP30 (1-10) when matched against a full FD-249 gallery for both the low 

and high confidence thresholds. The case of FAP30 two finger (2, 7) using the FD-249 full set gallery emerges as 

the best two finger FAP for the high confidence system threshold in terms of error trade-off with a distance-tooptimal of 0.0162. The highest ranking FAP10 case using an FD-249 database is that of four finger (2,3,7,8) with 

a distance of 0.0171 ranking it higher than FAP20 two finger (2,7) but lower than both two finger FAP30 and two 

finger control cases. Given this information, in cases where legacy FAP10 hardware needs to be used, FAP10 four 

finger (2,3,7,8) may be a competitive alternative in terms of error trade-off with the top ranking case of FAP30. 

Utilizing the rank based strategy, the two thumbs case (1,6) appears to be at a significant disadvantage in terms 

of system matching performance ranking below most of the other cases examined. This disadvantage is marked 

(i.e., FAP10 two finger (1,6) FNIR = 18.566 %) and is not substantially mitigated by using various gallery 

strategies or FAP levels.

Finally, the emerging smaller platen sensors (1/4" square) may present a significant advance in the area of 

authentication and verification, but their application in one-to-many (1:N) searching for which they are not 

designed is ill-advised. While the 1/4" square images perform reasonably well in terms of FPIR rates on the 

system tested, FNIR performance takes a significant hit where in the worst case scenario the system may fail to 

match the target identity at a rate of 97.380 % when using two thumbs (1,6). In the best case scenario where all 

ten fingers can be captured, the experimental 1/4" FAP yields an FNIR performance of 47.432 %, with an FPIR of 

0.0 %.

34
6. Conclusions

For the typical field case of two finger captured images being matched against a gallery of identities comprised of 

full FD-249 sets, FAP10 is at a disadvantage compared to other FAPs. In terms of FNIR, FAP10 submissions fail to 

identify their target almost nearly twice as often as FAP20 (1.164 % vs. 0.506 %) at the system low confidence 

threshold. For the high confidence threshold, FNIR for FAP10 is again almost twice that of FAP20 (3.270 % vs. 

1.844 %). FAP30 on the other hand appears to be best/optimal and its performance is on a par with (if not 

slightly better than) the uncropped control case (d=0.0162 for FAP30, d=0.0164 for control).

Comparing various probe finger combinations and Mobile ID FAPs to a gallery of full FD-249 shows stratification 

of ten and four finger probe sets at the top of the ranking scale (section 4.3.1) and the two finger probe sets 

occupying most of the lower half of the list. While the FAP10 two finger (2,7) case is not the worst performing 

among those tested (ranked 14 out of 20), significant matching performance benefits can be realized by either 

increasing the count of fingers captured in FAP10, or utilizing FAP30 for two finger capture cases. 

In examining the impact of gallery configuration on system performance (section 4.4.1), a complete FD-249 

gallery emerges as the optimal gallery configuration, with FAP30 ranking as best case tested among the various 

FAPs on this gallery. Rankings show that performance degradation observed by rolled-only or flat-only gallery 

configurations can be mitigated with larger FAPs or more fingers. The case of FAP10 ranks last among the 

Mobile ID FAPs when used on a rolled-only or flat-only gallery.

Throughout all FAPs, finger combinations and database combinations studied, system FPIR rates remained flat at 

0 % at the high confidence threshold. For the low confidence threshold, FPIR rates fluctuated slightly across the 

various test cases examined but generally remained low ranging from 0.028 % to 0.714 %.

Comparing the various Mobile ID FAP scenarios (section 4.5.1) for throughput performance shows that system 

match times appear closely linked to image spatial geometry where median search times for all image search 

FAPs consistently increases from FAP10 to FAP30 (with certain isolated exceptions). The two finger case of (2,7) 

appears to provide the best throughput performance out of all cases examined, while the two finger case of (1,6) 

incurs an unusually large throughput penalty.

Given the data in this study, the typical field case of FAP10 two finger (2,7) suffers from a significant 

performance penalty in terms of FNIR, showing a miss-rate almost twice as high as FAP20 and FAP30. It is 

possible to reduce FNIR significantly by adopting a larger capture profile such as FAP20 or FAP30. In cases 

where investment in legacy equipment must be preserved, a possibility mitigation strategy for this performance 

gap is to utilize four finger capture (i.e., 2,3,7,8) with the smaller FAP10. It should be noted however that simply 

capturing more fingers using the smaller single-finger mobile capture devices may introduce risk in terms of 

sequencing errors during the capture process so this mitigation strategy may introduce risk in terms of potential 

sequence errors.

35
7. References 

AN2k11 NIST Special Publication 500-290 Update 2013: “American National Standard for Information Systems — Data 

Format for the Interchange of Fingerprint, Facial & Other Biometric Information (ANSI/NIST ITL 1-2011)”. 

Approved December 2013. http://biometrics.nist.gov/cs_links/standard/ansi_2012/UpdateFinal_Approved_Version.pdf. Retrieved 01/31/2014.

MOBID Orandi, S., McCabe, R. M., “Mobile ID Device Best Practice Recommendation Version 1.0”, NIST SP 500-280, 

National Institute of Standards and Technology, Gaithersburg, MD. 

http://www.nist.gov/customcf/get_pdf.cfm?pub_id=903169. Retrieved 03/11/2013.

36
APPENDIX A. AGGREGATE TEST DATA

37
Table 16 - Summary Matching Performance Data by Probe and Gallery Type

A B C D E F G H I J K L M N O P Q R S T

RISC High Confidence System Hit (Threshold=5000)

FPIR FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control

1 FD249 Gallery 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%

2 Flat-only Gallery 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%

3 Rolled-only Gallery 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%

FNIR FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control

4 FD249 Gallery 10.402% 5.130% 3.598% 3.560% 3.272% 1.844% 1.616% 1.640% 4.040% 0.720% 0.390% 0.410% 1.714% 0.442% 0.338% 0.374% 3.940% 0.306% 0.206% 0.218%

5 Flat-only Gallery 16.350% 8.504% 6.124% 6.032% 6.694% 4.474% 4.032% 4.138% 6.334% 1.734% 1.094% 1.188% 3.260% 1.342% 1.124% 1.214% 6.706% 1.084% 0.662% 0.786%

6 Rolled-only Gallery 18.566% 10.850% 7.630% 7.560% 8.048% 4.494% 3.862% 4.078% 8.372% 2.238% 1.234% 1.420% 4.768% 1.390% 1.010% 1.194% 7.899% 1.230% 0.768% 0.868%

RISC Low Confidence System Hit (Threshold=2000)

FPIR FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control

7 FD249 Gallery 0.208% 0.106% 0.090% 0.046% 0.340% 0.422% 0.356% 0.368% 0.146% 0.128% 0.122% 0.158% 0.622% 0.650% 0.548% 0.546% 0.034% 0.060% 0.054% 0.052%

8 Flat-only Gallery 0.218% 0.132% 0.082% 0.064% 0.374% 0.462% 0.396% 0.406% 0.182% 0.166% 0.138% 0.158% 0.704% 0.664% 0.714% 0.644% 0.036% 0.052% 0.060% 0.066%

9 Rolled-only Gallery 0.176% 0.080% 0.076% 0.054% 0.294% 0.236% 0.222% 0.284% 0.164% 0.104% 0.098% 0.108% 0.462% 0.356% 0.258% 0.344% 0.064% 0.028% 0.062% 0.036%

FNIR FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control

10 FD249 Gallery 5.938% 2.620% 1.964% 1.890% 1.164% 0.506% 0.400% 0.472% 3.658% 0.464% 0.216% 0.226% 1.372% 0.236% 0.160% 0.192% 3.740% 0.190% 0.098% 0.134%

11 Flat-only Gallery 11.122% 5.268% 3.930% 3.846% 2.806% 1.766% 1.570% 1.726% 5.786% 1.364% 0.796% 0.888% 2.660% 0.936% 0.714% 0.848% 6.438% 0.946% 0.538% 0.686%

12 Rolled-only Gallery 10.610% 5.860% 4.380% 4.298% 4.024% 1.806% 1.318% 1.588% 7.704% 1.838% 0.904% 1.114% 4.212% 1.016% 0.652% 0.892% 7.539% 1.030% 0.564% 0.688%

IDFP High Confidence System Hit (Threshold=25000)

FPIR Control

13 FD249 Gallery - - - - - - - - - - - - - - - - - - - 0.000%

FNIR Control

14 FD249 Gallery - - - - - - - - - - - - - - - - - - - 14.802%

IDFP Low Confidence System Hit (Threshold=12000)

FPIR Control

15 FD249 Gallery - - - - - - - - - - - - - - - - - - - 0.000%

FNIR Control

16 FD249 Gallery - - - - - - - - - - - - - - - - - - - 1.260%

Probe Configurations

38
Table 17 - Summary Matching Throughput by Probe and Gallery Type (median match time, milliseconds)

RISC Matcher Configuration A B C D E F G H I J K L M N O P Q R S T

Probe Contents Probe Pairing Gallery FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control FAP10 FAP20 FAP30 Control

1 Image Non-mated FD249 3068 3973 5323 5445 1966 2370 3059 2690 2155 2675 3713 3675 2205 2664 3518 3119 3377 3671 4400 4336

2 Image Mated FD249 2942 3928 5345 5467 1841 2251 2964 2568 1945 2505 3530 3481 1996 2483 3327 2935 2050 2537 3426 3362

3 Image Non-mated Flat 2110 2765 3716 3845 1696 2068 2604 2338 1883 2364 3213 3252 1892 2296 2950 2731 2093 2713 3573 3197

4 Image Mated Flat 2141 2823 3813 3901 1697 2074 2610 2344 1908 2398 3276 3299 1849 2290 2993 2711 2177 2806 3569 3389

5 Image Non-mated Rolled 2327 3117 4181 4291 1681 2061 2665 2350 1782 2269 3140 3168 1807 2239 2931 2640 2085 2543 3331 3340

6 Image Mated Rolled 2565 3307 4424 4545 1772 2159 2764 2439 2044 2504 3450 3379 1945 2357 3082 2841 3073 3334 4107 4043

7 Template Non-mated FD249 1652 2074 2534 2595 763 807 936 851 827 879 1037 979 853 898 1090 977 1816 1506 1536 2099

8 Template Mated FD249 1566 2035 2587 2625 663 707 870 756 687 770 969 881 745 914 1125 842 1258 1243 993 1293

9 Template Non-mated Flat 827 994 1192 1211 551 564 616 605 580 623 706 700 614 644 732 698 1088 900 842 1888

10 Template Mated Flat 854 1035 1246 1281 547 564 612 599 592 640 740 722 614 650 741 704 904 791 834 1155

11 Template Non-mated Rolled 1183 1400 1698 1740 639 645 745 701 725 747 838 817 740 747 851 799 1295 1088 1181 1338

12 Template Mated Rolled 1209 1447 1755 1798 625 638 739 682 724 752 855 828 722 728 849 804 1158 1074 1193 1594

IDFP Matcher Configuration

13 Image Non-mated FD249 - - - - - - - - - - - - - - - - - - - 3096

14 Image Mated FD249 - - - - - - - - - - - - - - - - - - - 3186

15 Template Non-mated FD249 - - - - - - - - - - - - - - - - - - - 1918

16 Template Mated FD249 - - - - - - - - - - - - - - - - - - - 1739

Probe Configurations

39