NISTIR 7249

Two Finger Matching With Vendor 

SDK Matchers

C. Watson

C. Wilson

M. Indovina

B. Cochran
NISTIR 7249

Two Finger Matching With Vendor 

SDK Matchers

C. Watson

C. Wilson

M. Indovina

B. Cochran

Image Group

Information Technology Laboratroy

July 2005

U.S. DEPARTMENT OF COMMERCE

Carlos M. Gutierrez, Secretary

TECHNOLOGY ADMINISTRATION

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

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

William A. Jeffrey, Director
Two Finger Matching with Vendor 

SDK Matchers 

Craig Watson, Charles Wilson, Mike Indovina, Brian Cochran 

National Institute of Standards and Technology 

Abstract 

This report is an extension of the NIST “Studies of one-to-one Fingerprint Matching with 

Vendor SDK Matchers” which evaluated the accuracy of SDK (Software Development 

Kit) based COTS (Commercial Off-The-Shelf) fingerprint matching systems for one-toone verification applications [1]. Fingerprint matching systems from twelve vendors were 

evaluated. The two finger matching evaluation is an extension of that testing used to 

evaluate the accuracy that can be achieved by combining the index finger scores to 

achieve a match. These results are based on the SDK matchers provided for the original 

single finger SDK testing. More details will be available from the Minutiae Exchange 

Test 2004 (MINEX04) http://fingerprint.nist.gov/MINEX04. The more accurate 

matchers in the two finger SDK scoring were able to achieve true accept rates (TAR) in 

the range of .985 - .998 at a false accept rate (FAR) of 0.0001. A copy of this report and 

appendices is available at http://fingerprint.nist.gov/SDK. 
2

Introduction 

The original SDK one-to-one fingerprint matching tests were performed to measure 

the accuracy of one-to-one fingerprint verification systems[1]. This evaluation is an 

extension of the one-to-one fingerprint matching tests and is designed to evaluate the 

matching accuracy that can be achieved by combining scores for the right and left index 

fingers. These results will support work being done by NIST, Personal Identity 

Verification (PIV) [3], for Homeland Security Presidential Directive 12 (HSPD12) [2]. 

This report along with the original single finger SDK testing report will show fingerprint 

matching accuracies that are obtainable using either single finger or two finger matching 

strategies. The current two finger testing evaluates fingerprint data from live-scan capture 

devices that capture each index finger one at a time. Segmented four finger slaps are not 

evaluated. 

This report provides preliminary estimates of the propietary templates tests being 

done in MINEX04 [4]. Part of the MINEX04 testing will evaluate the use of each 

vendor’s propietary minutiae template for fingerprint matching. The propietary template 

testing in MINEX04 uses the same format as this SDK testing but will include more 

vendors. 

Testing Strategy 

The results in this report are based on a very simple strategy of summing the matcher 

scores obtained for the right and left index fingers into a single score. This score is used 

to evaluate the matching accuracy for two fingers. This test did not search for the best 

strategy for combining the index finger scores. The intent of this report is to show what 

can be achieved from a simple two finger matching strategy. 

Previous SDK testing used sample sizes of 6,000 subjects and computed a complete 

score matrix using the first capture as the probe and the second as the gallery. This 

produced 6,000 mate scores and 35,994,000 non-mate scores. This match distribution 

sample size works for single finger matchers but was not large enough when using two 

finger matching. The non-match distribution size is hundreds of times larger than these 

tests require. 

For the two finger matcher test, the sample size used for each dataset tested was 

approximately 60,000 mates and 120,000 true non-mates, these sizes varied slightly as 

consolidations were made. The tests were setup so that the gallery images were the 

second instances of the 60,000 mates. The non-mate scores were obtained by matching 

the first 60,000 non-mates probes against the 60,000 mate gallery and then repeating this 

for the next 60,000 non-mates probes. 

The larger sample size created problems with the previous test strategy of extracting 

all the templates into memory and matching from memory. There was not enough 

memory on the machines to store all 240,000 templates. The NIST software that accessed 

the SDK libraries was redesigned to do matching in two steps. First, the templates were 
3

extracted for each image written to disk. The templates are randomly read in pairs and 

scores computed for each pair of templates. The resulting scores are then stored on disk. 

Evaluation Data 

Since the current scope of this report is supporting NIST work for HSPD-11 and 

HSPD-12 the only datasets being used for the current testing is from single finger livescan capture devices. The data is from several sources: US VISIT Port of Entry (POE) 

and Bio-Visa Application (BVA), Department of State (DOS), and Department of 

Homeland Security (DHS2). At this time only the DOS data sample has been completely 

consolidated by human examiners. 

The data used for testing (60,000 mates and 120,000 non-mates) was randomly 

selected from the available data for each dataset. 

DHS 2-Finger Images (DHS2) 

Description 

DHS recidivist cases, the majority of which are border crossing cases with 

Mexico. 

Environment: border patrol field operations 

Number of Subjects 

 ~600,000 (of ~632,000) 

Instances per Subject 

Minimum of 2 cases per person, 

where each case contains one right 

index impression and one left index 

impression. 

Impression Type 

Live-scan Plain 

Finger Positions Captured 

Right and Left Index 

Capture Device(s) 

Identix DFR-901

Availability 

Government use only 

Data Preparation 

The sample of data used for this two finger SDK testing has not been

consolidated by human examiners. 

 

1

 Specific hardware and software products identified in this report do not imply recommendation or 

endorsement by the National Institute of Standards and Technology. 
4

Department of State Mexican Visa Database (DOS) 

Description 

DOS Mexican Visa cases 

Environment: Mexican Consulates offices 

Number of Subjects 

~274,000 (of 288,00) 

~6 million background 

Instances per Subject 

Minimum of 2 cases per person, 

where each case contains one right 

index impression and one left index 

impression. 

Impression Type 

Live-scan Plain 

Finger Positions Captured 

Right and Left Index 

Capture Device(s) 

Identix DFR-90 

Availability 

Government use only 

Data Preparation 

The sample of data used for this two finger SDK testing has been consolidated by 

human examiners. 
5

U.S. VISIT Point of Entry Data (POE) 

and Bio-Visa Application (BVA) 

Description 

Data from U.S. VISIT captured from persons entering the U.S. at airport points 

of entry (POE) and at Consulates when applying for a U.S. VISA (BVA). 

Number of Subjects 

 ~3.5 Million with one POE instance 

~715K with 2 or more POE instances 

~290K with POE and BVA instances 

Instances per Subject 

One to many cases per person, with 

each case containing one right index 

finger impression and one left index 

finger impression. 

Impression Type 

Live-scan Plain 

Finger Positions Captured 

Right and Left Index 

Capture Device(s) 

Cross Match 300A (POE) 

Smiths-Heimann ACC01394 (BVA) 

Availability 

Government use only 

Data Preparation 

The sample of data used for this two finger SDK testing has not been

consolidated by human examiners. 
6

Results 

 The 60,000 match scores were used to generate match distributions for each matcher 

and dataset combination. The 120,000 nonmatch scores were used to generate nonmatch 

distributions for each of the corresponding matcher and dataset combinations. These two 

distributions were then used to generate sets of ROC (Receiver Operator Curve) and DET 

(Detection Error Tradeoff) curves. These sample sizes were selected to provide accurate 

FAR calculations down to 0.0001. 

The number of ROC and DET curves was too large to put in the body of this report so 

they are included in appendices. Appendix A contains the ROC/DET curves for each 

SDK tested. Appendix B contains the ROC/DET curves for each dataset. 

Tables 1 and 2 show TARs achieved at FARs of 0.0001 and 0.01 for each SDK in 

alphabetical order. Tables 3 and 4 show the same information using FRR instead of TAR. 

The highest TAR value for each dataset at FAR of 0.0001 is 0.9881 (DHS2, D), 0.9983 

(DOS, R), 0.9986 (POE, H,I&R), and 0.9987 (POEBVA, H,I&R). At FAR 0.01 the 

highest TAR value is 0.9982 (DHS2, D), 0.9996 (DOS, H,I&R), 0.999 (POE, H,I,O&R), 

and 0.9995 (POEBVA, H,I&R). These are significantly better than the same points for 

single fingers shown in table 5. Clearly using two fingers helps all vendors achieve 

higher TARs at a fixed FAR point. Two fingers can make a lower scoring single finger 

vendor (K for DHS2 RI-0.871 and LI-0.8769) more competitive with other vendors (K 

for DHS2 0.9661). 

Tables 6 and 7 show the FAR values at a set TAR of 0.995 and 0.998. These points 

show some separation in the different vendor SDKs when achieving a high level of TAR 

performance. The lowest FAR value for each dataset at a TAR of 0.998 is 0.0081 (DHS2, 

D), 0.0001 (DOS, R), <0.00002 (POE, H,I,O&R), and <0.00002 (POEBVA, H,I&R). 

Although two fingers helped improve K’s TAR (0.9661) performance at FAR 0.0001 at 

higher TAR (0.998) values the FAR increases significantly (0.4084). 

Table 8 shows the equal error rate (EER) points for all the SDKs. This is another 

point to help separate the performance of each SDK. The lower the EER value is the 

better overall performance you will get from the matcher. The best equal error rate is 

0.0008 for marcher H&R on POEBVA and R on DOS matches. It is interesting to note 

that for this matcher the equal error rate for DHS2 data is 0.0087 an order of magnitude 

higher. The best equal error rate for DHS2 data is 0.0034 for matcher D. Most of the 

equal error rates for the other three data sets are in the 0.001 to 0.002 range. 

Table 9 gives the vendor names that go with each SDK letter, showing that some 

vendors submitted multiple SDKs for testing. 
7

DHS2 DOS POE POEBVA 

D 0.9881 0.9845 0.9946 0.9905 

F 0.9851 0.9944 0.9971 0.9969 

H 0.9833 0.9978 0.9986 0.9987 

I 0.9865 0.9978 0.9985 0.9986 

J 0.9909 0.9965 0.9957 

K 0.9661 0.9808 0.9911 0.9909 

O 0.9820 0.9969 0.9984 0.9981 

P 0.9779 0.9948 0.9978 0.9971 

Q 0.9834 0.9960 0.9968 0.9955 

R 0.9983 0.9987 0.9987 

Table 1: TAR at a FAR of 0.0001 

DHS2 DOS POE POEBVA 

D 0.9982 0.9950 0.9981 0.9982 

F 0.9949 0.9985 0.9988 0.9992 

H 0.9916 0.9996 0.9990 0.9995 

I 0.9976 0.9996 0.9990 0.9994 

J 0.9966 0.9983 0.9981 

K 0.9885 0.9933 0.9969 0.9966 

O 0.9892 0.9989 0.9989 0.9990 

P 0.9837 0.9973 0.9984 0.9980 

Q NA NA NA NA 

R 0.9997 0.9990 0.9996 

Table 2: TAR at a FAR of 0.01 

DHS2 DOS POE POEBVA 

D 0.0119 0.0155 0.0054 0.0095 

F 0.0149 0.0056 0.0029 0.0031 

H 0.0167 0.0022 0.0014 0.0013 

I 0.0135 0.0022 0.0015 0.0014 

J 0.0091 0.0035 0.0043 

K 0.0339 0.0192 0.0089 0.0091 

O 0.0180 0.0031 0.0016 0.0019 

P 0.0221 0.0052 0.0022 0.0029 

Q 0.0166 0.0040 0.0032 0.0045 

R 0.0017 0.0013 0.0013 

Table 3: FRR at a FAR of 0.0001 
8

DHS2 DOS POE POEBVA 

D 0.0018 0.0050 0.0019 0.0018 

F 0.0051 0.0015 0.0012 0.0008 

H 0.0084 0.0004 0.0010 0.0005 

I 0.0024 0.0004 0.0010 0.0006 

J 0.0034 0.0017 0.0019 

K 0.0115 0.0067 0.0031 0.0034 

O 0.0108 0.0011 0.0011 0.0010 

P 0.0163 0.0027 0.0016 0.0020 

Q NA NA NA NA 

R 0.0003 0.0010 0.0004 

Table 4: FRR at a FAR of 0.01 

DHS2 DOS POE POEBVA 

RI LI RI LI RI LI RI LI 

D 0.9407 0.9468 0.9626 0.9320 0.9727 0.9588 0.9715 0.9486 

F 0.9570 0.9578 0.9763 0.9567 0.9805 0.9675 0.9804 0.9626 

H 0.9555 0.9554 0.9915 0.9759 0.9896 0.9854 0.9918 0.9815 

I 0.9645 0.9676 0.9888 0.9762 0.9867 0.9814 0.9887 0.9774 

J 0.9664 0.9405 0.9711 0.9592 0.9740 0.9548 

K 0.8710 0.8769 0.9390 0.8909 0.9490 0.9282 0.9464 0.9174 

O 0.9540 0.9530 0.9879 0.9713 0.9873 0.9830 0.9894 0.9764 

P 0.9410 0.9387 0.9807 0.9587 0.9815 0.9735 0.9838 0.9675 

Q 0.9471 0.9555 0.9845 0.9740 0.9760 0.9707 0.9823 0.9650 

R 0.9916 0.9783 0.9898 0.9860 0.9910 0.9821 

Table 5: Single finger TAR at a FAR of 0.0001 

DHS2 DOS POE POEBVA 

RI LI RI LI RI LI RI LI 

D 0.0593 0.0532 0.0374 0.0680 0.0273 0.0412 0.0285 0.0514 

F 0.0430 0.0422 0.0237 0.0433 0.0195 0.0325 0.0196 0.0374 

H 0.0445 0.0446 0.0085 0.0241 0.0104 0.0146 0.0082 0.0185 

I 0.0355 0.0324 0.0112 0.0238 0.0133 0.0186 0.0113 0.0226 

J 0.0336 0.0595 0.0289 0.0408 0.0260 0.0452 

K 0.1290 0.1231 0.0610 0.1091 0.0510 0.0718 0.0536 0.0826 

O 0.0460 0.0470 0.0121 0.0287 0.0127 0.0170 0.0106 0.0236 

P 0.0590 0.0613 0.0193 0.0413 0.0185 0.0265 0.0162 0.0325 

Q 0.0529 0.0445 0.0155 0.0260 0.0240 0.0293 0.0177 0.0350 

R 0.0084 0.0217 0.0102 0.0140 0.0090 0.0179 

Table 6: Single finger FRR at a FAR of 0.0001 
9

DHS2 DOS POE POEBVA 

D 0.0015 0.0099 0.0002 0.0004 

F 0.0104 0.0001 < 0.00002 < 0.00002 

H 0.0524 < 0.00002 < 0.00002 < 0.00002 

I 0.0040 < 0.00002 < 0.00002 < 0.00002 

J 0.0020 < 0.00002 < 0.00002 

K 0.0860 0.0260 0.0012 0.0023 

O NA < 0.00002 < 0.00002 < 0.00002 

P NA 0.0001 < 0.00002 < 0.00002 

Q NA < 0.00002 < 0.00002 0.0001 

R < 0.00002 < 0.00002 < 0.00002 

Table 7: FAR at a TAR of 0.995 

DHS2 DOS POE POEBVA 

D 0.0081 0.0967 0.0080 0.0075 

F 0.0295 0.0057 0.0004 0.0007 

H NA 0.0002 < 0.00002 < 0.00002 

I 0.0127 0.0002 < 0.00002 < 0.00002 

J 0.0623 0.0040 0.0079 

K 0.4084 0.2144 0.0588 0.0621 

O NA 0.0009 < 0.00002 0.0001 

P NA NA 0.0004 0.009 

Q NA NA 0.0014 NA 

R 0.0001 < 0.00002 < 0.00002 

Table 8: FAR at a TAR of 0.998 

DHS2 DOS POE POEBVA 

D 0.0034 0.0058 0.0026 0.0029 

F 0.0067 0.0026 0.0016 0.0016 

H 0.0087 0.0011 0.0011 0.0008 

I 0.0046 0.0010 0.0012 0.0009 

J 0.0042 0.0022 0.0024 

K 0.0111 0.0074 0.0038 0.0042 

O 0.0107 0.0018 0.0013 0.0013 

P 0.0159 0.0031 0.0018 0.0023 

Q 0.0101 0.0023 0.0019 0.0023 

R 0.0008 0.0011 0.0008 

Table 9: Equal Error Rates 
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SDK Vendor Name 

D, F, I, R Cogent Systems, Inc. 

H, O, P NEC 

J SAGEM Morpho, Inc. 

K Neurotechnologija 

Q ID Solutions, Inc. 

Table 10: Vendor Names. 

Conclusions 

Two finger matching is better than single finger (Tables 1 and 5). For a FAR of 

0.0001 the best (RI-POEBVA) TAR is 0.9918 and for two fingers the equivalent TAR is 

0.9987. This effect is even more pronounced for the less accurate matcher K. On DHS2 

data the single RI TAR is 0.8710 and the two finger result is 0.9661. In both cases the 

FRR is reduced by more than a factor of four. 

A lower scoring single finger matcher can make significant improvements by using 

two fingers and be competitive with the better single finger matchers. On good quality 

POEBVA matches the K matcher has a TAR of 0.9909 at a FAR of 0.0001. Only one 

matcher on POEBVA data has a higher single finger score, H with 0.9919. 

In general, it appears that the higher performing single finger matchers will be better 

two finger matchers at extreme performance points (ie. TAR of 0.998). The performance 

patterns also show that as TARs approach 1.0 the use of additional fingers, even for the 

most accurate matchers, produces less improvement. 
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References 

[1] C. I. Watson, C. L. Wilson, K. Marshall, M. Indovina, R. Snelick, “Studies of One-toOne Fingerprint Matchers with Vendor SDK Matchers”, NIST IR 7221, April 2005. 

http://fingerprint.nist.gov/SDK 

[2] http://www.whitehouse.gov/news/releases/2004/08/20040827-8.html

[3] http://csrc.nist.gov/piv-project 

[4] Minutiae Exchange Test 2004. http://fingerprint.nist.gov/minex04 

[5] “Summary of NIST Standards for Biometric Accuracy, Tamper Resistance, and 

Interoperability,” PDF document at http://www.itl.nist.gov/iaui/894.03/pact/pact.html

(“NIST Appendix A” link), November 2002. 

[6] Public Law 107-56 (USA PATRIOT ACT); 107th United States Congress, 

Washington, D.C.; 26 October 2001 

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

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

[8] C L Wilson, C I Watson, M D Garris, A Hicklin; “Studies of Fingerprint Matching 

Using the NIST Verification Test Bed (VTB)” NIST IR 7020; National Institute of 

Standards and Technology; Gaithersburg Maryland , 07 July 2003 at 

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

[9] Charles Wilson, R. Austin Hicklin, Harold Korves, Bradford Ulery, Melissa Zoepfl, 

Mike Bone, Patrick Grother, Ross Micheals, Steve Otto, and Craig Watson, “Fingerprint 

Vendor Technology Evaluation 2003,” DRAFT NIST IR, June 2004. 

[10] Stephen S. Wood and Charles L. Wilson, “Studies of Plain-to-Rolled Fingerprint 

Matching Using the NIST Algorithmic Test Bed (ATB),” NIST IR 7112; National 

Institute of Standards & Technology, Gaithersburg Maryland at 

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

[11] C. L. Wilson, M. D. Garris, and C. A. Watson ,“Matching Performance for the 

US-VISIT IDENT System Using Flat Fingerprints,” NIST IR 7110; National Institute of 

Standards & Technology, Gaithersburg Maryland, May 2004, at 

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

[12] P. J. Phillips, P. Grother, R. J. Micheals, D. M. Blackburn, E. Tabassi, and M. 

Bone, “Face recognition vendor test 2002,” NIST IR 6965, National Institute of 

Standards & Technology, Gaithersburg Maryland, March 2003, results at 

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

[13] Other related NIST fingerprint work may be viewed at http://fingerprint.nist.gov. 
Appendix A: ROC/DET plots for each SDK. 

A-1
A-2
A-3
A-4
A-5
A-6
A-7
A-8
A-9
A-10
Appendix B: ROC/DET plots for each Dataset. 

B-1
B-2
B-3
B-4