SlapSegII Analysis: Matching 

Segmented Fingerprint Images

Craig Watson

Patricia Flanagan

NISTIR 7747
NISTIR 7747

SlapSegII Analysis: Matching 

Segmented Fingerprint Images

Craig Watson

Patricia Flanagan

Information Technology Laboratory

Information Access Division

November 2010

U.S. Department of Commerce

National Institute of Standards and Technology

Patrick D. Gallagher, Director
1

SlapSegII Analysis: Matching Segmented Fingerprint Images

Abstract

This report is an extension of the SlapSegII1

1. Introduction

evaluation which measured the ability of slap 

segmentation algorithms to successfully segment four-finger slaps into individual fingerprint images. 

This study compared the matching accuracy of hand-marked segmentation and automated 

segmentation algorithm results to examine the effect of automated slap fingerprint segmentation

errors on matching accuracy. A couple other issues this study examined are: 1) How detrimental was

excluding fingerprint ridge structure (over-segmentation) to matching? 2) How much did fusion of 

multiple fingers compensate for segmentation errors? The only dataset from SlapSegII with mates 

available for matching was the dataset with 2-inch high slaps, so the study looked at matching accuracy 

for single finger, two index fingers and eight fingers (no thumbs).

The SlapSegII evaluation was performed to measure the current capabilities of four-finger slap 

segmentation algorithms to correctly segment fingerprints within a certain geometric tolerance of 

hand-marked ground truth data. The tolerances were based on matching studies conducted on a data 

sample of the 2-inch tall slap fingerprint data. In those matcher studies various tolerance limits were 

studied and the SlapSegII tolerance limits were selected to have a minimal effect on the matching 

accuracy. This gave SlapSegII a more stringent success metric than SlapSeg04 which only required that 

the fingerprints be matchable using high quality matching algorithms. The advantage of SlapSegII was 

that it measured the segmentation algorithm’s ability to preserve the fingerprint ridge structure. While 

a good matcher may compensate for over-segmentation on some fingers it couldn’t match ridge detail 

that was missing, which would’ve been more significant to latent fingerprint matching.

This report evaluated the ability of fingerprint matching algorithms to match rolled images with both 

the hand-marked ground truth boxes and the results of the automated segmentation algorithms. Some 

caveats to this study were: 1) While this study used law enforcement operational quality data, results 

can vary from dataset to dataset. 2) The effect of fusing multiple fingers was examined but only for

basic sum fusion by adding matcher scores. 3) The matching used 1-to-1 matching with mates and true 

non-mates (search probes with no mate in the gallery). It was beyond the scope of this study to 

measure the effect on a complete n-staged 1-to-many Automated Fingerprint Identification System 

(AFIS) taking into account all aspects of that system.

2. Dataset

The dataset used for this study was the 2-inch tall data from SlapSegII as it was the only dataset with 

mate data (rolled prints) available for matching. This data was operational law enforcement quality 

 1 www.nist.gov/itl/iad/ig/slapseg.cfm
2

that is used in AFIS systems today. Future work may include new data samples (ie., 3-inch live-scan 

data) as mates become available for studying matching accuracy. This 2-inch dataset contained right 

hand slaps for ~22,500 subjects and left hand slaps for ~20,000 subjects. There were ~12,500 subjects 

with both right and left hand slaps in the dataset that were used in the multiple finger fusion part of 

the study. 

3. Testing Performed

The baseline matching results used the hand-marked ground-truth boxes to segment the individual 

fingerprint images that were then matched to the rolled fingerprint images. Then, the resulting images 

from each segmentation algorithm were matched against the rolled fingerprint images. Matcher 

threshold values were determined based on the baseline results (ie., threshold at False Match Rate 

(FMR) 10-4

) and then fixed when evaluating the results for each segmentation algorithm. 

The three matching algorithms were high-end performers in the Proprietary Fingerprint Template 

(PFT)2

The first set of tests compared baseline results with the segmentation algorithm results for single 

fingers, two fingers (combining left and right index fingers), and eight fingers. The fused matching 

scores were computed using the sum rule for left/right index fingers (2F) and all eight fingers (8F). 

evaluation but with different speed and template size characteristics. Matcher-I had the smallest 

feature template and was the fastest in both template extractions and matching. Matcher-II was two 

times slower than Matcher-I in template extractions, had larger templates, and was only slightly slower 

in matching speed. Matcher-III was 3-4 times slower than Matcher-I for template extraction, had the 

largest template size, and was 2-3 times as slow in matching speed.

The second set of results looked at only the hand-marked segmentation boxes for three cases. The first 

case used the ground truth (GT) boxes. The second case used the hand-marked boxes moved to the 

minimum tolerance limit (GTL) used for SlapSegII (maximum ridge structure loss allowed). The third 

case used the hand-marked boxes moved 16 pixels3

The next test examined the change in the average matcher score for each segmentation algorithm 

compared to the GT results. The average matcher score was only computed for known mates in the 

dataset. The raw matcher score couldn’t be revealed so for this test the percentage change in the 

average score was recorded.

inside the minimum tolerance limit (GTL2). The 

minimum tolerance limit in SlapSegII was -32 pixels for the left and right sides and -64 pixels for the top 

and bottom edges.

 2 http://www.nist.gov/itl/iad/ig/pft.cfm

3 All fingerprint images in this study were scanned at 500 pixels per inch (19.69 pixels per millimeter).
3

Finally, the actual number of False Non-Matches (FNM) and False Matches (FM) were recorded to 

compare the use of GT segmentation boxes to the use of segmentation algorithms boxes.

4. Results

Figures 1 and 2 show the detection error trade-off (DET) curves for Matcher-I using left and right index 

fingers. In both cases there was about a 2% increase in error rate from the hand-marked (GT) 

segmentation boxes to the highest error rate for the segmentation algorithms. Comparing this to the 

results from SlapSegII, the algorithms in SlapSegII that performed segmentation best based on the 

geometric limits produced matching results closest to the GT boxes. Appendix D has the complete set 

of DET curves for all fingers and matchers.

Figures 3 and 4 are DET curves that showed the variations of the hand-marked data. There was a 

significant increase in error going from GT to GTL to GTL2, which showed that allowing all sides to 

reach the minimum tolerance limits of SlapSegII was detrimental to matching. Based on these results,

most of the segmentation algorithms in SlapSegII were not failing on all sides of the segmentation box. 

Also, looking at Figures 1 and 2 compared to Figures 3 and 4 the GTL results were about the same as 

the worst segmentation algorithm results. The GTL2 results were significantly worse than the 

segmentation algorithm results. Appendix E has the complete set of curves for all fingers and matching 

algorithms.

Figures 5 and 6 show the two-finger fusion matching results for each segmentation algorithm and 

variations of the hand-marked boxes. Note the scale change from the previous figures. Not 

surprisingly, two-finger fusion produced better matching results over single finger matching. Twofinger fusion also reduced the range in performance across the segmentation algorithms in Figure 5 as 

well as the hand-marked ground truth variations in Figure 6. Figures 7-8 show the same results for 8-

finger fusion and again the scale was changed. More fingers further reduced the range but there

remained a difference between the lower to better performing segmentation algorithms. The end of 

Appendix E has the full set of plots for all three matching algorithms.
4

Figure 1: DET - Matcher-I – Finger 02 – Segmentation Algorithm Boxes.

Figure 2: DET - Matcher-I – Finger 07 – Segmentation Algorithm Boxes.
5

Figure 3: DET - Matcher-I – Finger 02 – GT Variation Boxes.

Figure 4: DET - Matcher-I – Finger 07 – GT Variation Boxes.
6

Figure 5: DET - Matcher-I – 2F Fusion – Segmentation Algorithm Boxes.

Figure 6: DET - Matcher-I – 2F Fusion – GT Variation Boxes.
7

Figure 7: DET - Matcher-I – 8F Fusion – Segmentation Algorithm Boxes.

Figure 8: DET - Matcher-I – 8F Fusion – GT Variation Boxes.
8

Table 1 shows the complete set of FNMR points for single finger, two-finger and eight-finger 

combinations based on the thresholds that resulted in FMR of 10-4 for the hand-marked ground truth 

results (GT). All single finger matching results for the segmentation algorithms performed worse than 

the single finger matching results for the GT boxes. These results in table 1 also reinforce that two or 

more fingers can significantly compensate for reduced segmentation accuracy. In fact, the two-finger 

fusion matching results for the most accurate segmentation algorithms performed as well as the twofinger fusion GT matching results. Just as important, all the two-finger fusion matching for the 

segmentation algorithms performed better than the single finger matching results for GT boxes. 

Appendix A has the complete set of tables for the three matching algorithms used.

02 03 04 05 07 08 09 10 2f 8f

GT 0.0549 0.0577 0.0560 0.1148 0.0431 0.0466 0.0748 0.1677 0.0119 0.0018

GTL 0.0802 0.0842 0.0839 0.1767 0.0735 0.0770 0.1175 0.2675 0.0203 0.0045

A 0.0753 0.0799 0.0808 0.1491 0.0629 0.0671 0.0983 0.2083 0.0191 0.0050

B 0.0598 0.0591 0.0559 0.1151 0.0416 0.0480 0.0742 0.1682 0.0119 0.0021

C 0.0605 0.0580 0.0538 0.1145 0.0412 0.0454 0.0729 0.1677 0.0120 0.0023

E 0.0590 0.0590 0.0557 0.1154 0.0423 0.0464 0.0730 0.1699 0.0122 0.0019

F 0.0584 0.0582 0.0555 0.1160 0.0414 0.0463 0.0710 0.1699 0.0113 0.0020

G 0.0654 0.0605 0.0551 0.1165 0.0434 0.0485 0.0769 0.1714 0.0123 0.0022

H 0.0587 0.0659 0.0576 0.1246 0.0460 0.0491 0.0765 0.1824 0.0134 0.0029

I 0.0624 0.0610 0.0597 0.1197 0.0477 0.0485 0.0778 0.1689 0.0125 0.0019

J 0.0688 0.0739 0.0708 0.1475 0.0737 0.0781 0.1021 0.2048 0.0226 0.0082

Table 1: FNMR for Matcher-I (Threshold from GT at FMR of 10-4)

Table 2 shows the percentage drop in the average confidence score compared to the hand-marked 

(GT) results for Matcher-I. Appendix B has the complete set of tables for all three matching algorithms. 

02 03 04 05 07 08 09 10 2f 8f

GTL -7.57 -7.73 -7.49 -11.81 -8.58 -7.50 -9.95 -15.89 -16.33 -9.68

A -4.12 -4.36 -5.24 -6.07 -4.66 -3.76 -4.44 -6.32 -8.90 -4.92

B -0.29 0.00 0.07 0.20 0.12 -0.16 0.23 0.01 -0.20 -0.01

C -0.33 -0.01 0.12 0.34 0.05 -0.05 0.29 0.20 -0.39 0.03

E -0.22 -0.00 -0.09 0.09 0.11 -0.01 0.30 0.02 -0.28 -0.02

F -0.23 0.09 0.06 -0.01 0.13 -0.03 0.29 0.05 -0.27 -0.00

G -1.21 -0.58 -0.28 -0.17 -0.15 -0.41 -0.14 -0.13 -1.35 -0.42

H -0.54 -1.20 -0.38 -0.89 -0.40 -0.53 -0.07 -1.72 -1.08 -0.76

I -0.52 -0.29 -0.58 -0.48 -0.49 -0.39 -0.37 -0.25 -1.05 -0.44

J -1.95 -2.67 -2.37 -4.07 -3.35 -3.69 -3.58 -5.27 -5.77 -3.47

Table 2: Percent Change in Average Confidence Score Compared to GT for Matcher-I

Tables 3-4 show the actual number of False Non Matches (FNM) and False Matches (FM) for each 

segmentation algorithm. Again the threshold was fixed based on the hand-marked results (GT) at FMR 

of 10-4

. In some cases there were several hundred matches made with the hand-marked segmentation 
9

boxes that were missed with the segmentation algorithms. The sample size should be increased to 

make any significant analysis of the false matches but there was a small increase in the number of false 

matches when comparing GT to the segmentation algorithms. Appendix C has the complete set of 

tables for all three matching algorithms.

02 03 04 05 07 08 09 10 2f 8f

GT 1239 1303 1264 2592 849 919 1476 3311 149 23

GTL 1813 1899 1898 3989 1448 1520 2322 5272 252 57

A 1699 1803 1826 3375 1239 1318 1938 4097 239 63

B 1350 1337 1261 2600 819 943 1462 3317 147 27

C 1365 1308 1212 2589 812 892 1434 3315 152 30

E 1330 1329 1257 2603 834 911 1438 3349 153 24

F 1321 1314 1255 2613 815 912 1398 3348 139 26

G 1480 1359 1248 2644 858 955 1515 3364 154 28

H 1331 1486 1310 2812 906 967 1507 3597 168 37

I 1407 1375 1347 2701 939 959 1530 3326 157 25

J 1555 1663 1597 3336 1451 1537 2016 4046 282 103

Table 3: Number of FNM for Matcher-I (Threshold from GT at FMR of 10-4)

02 03 04 05 07 08 09 10 2f 8f

GT 9 9 9 9 8 8 8 8 5 5

GTL 17 10 13 12 14 10 7 5 12 4

A 15 7 11 12 16 9 7 8 8 2

B 10 9 6 11 18 7 5 6 7 6

C 10 6 4 6 13 6 8 1 4 6

E 11 7 7 7 15 5 6 6 8 5

F 10 9 7 3 12 4 7 6 10 10

G 15 11 4 8 14 5 7 2 5 2

H 15 10 5 7 17 13 8 4 12 0

I 13 6 4 5 13 6 8 6 9 3

J 12 5 6 14 12 7 4 7 6 2

Table 4: Number of FM for Matcher-I (Threshold from GT at FMR of 10-4)

5. Summary

The results show there was a drop off in matching accuracy performance as segmentation boxes 

deviated from the hand-marked segmentation boxes. At an FMR of 10-4 the FNMR for single finger, two 

fingers (right and left index) and eight fingers increased approximately 2%, 1% and 0.5% respectively 

compared to results from the hand-marked segmentation boxes. The use of multiple fingers definitely 

improved matching performance, but for the least accurate segmentation algorithms the score 

returned by the matcher was about 5% lower than hand-marked segmentation boxes. Two other 

observations also showed that using multiple fingers improved matching of segmented fingerprint 
10

data. First, two finger matching accuracies for all the segmentation algorithms were better than the 

single finger results for the hand-marked ground truth. Second, two finger and eight finger matching 

rates for the most accurate segmentation algorithms were equal to matching rates for the handmarked segmentation boxes. 

Finally, analysis of the tolerance limits used in the SlapSegII evaluation showed that placing the 

segmentation boxes at twice the inner tolerance limit increased the FNMR rate (FMR 10-4

) by a factor 

of 3.

There were three issues not addressed in this report. First, what would the effect of these 

segmentation errors be on an “end-to-end” AFIS? Second, how will segmentation boxes that are larger 

than the hand marked segmentation boxes affect matching? Finally, how would the loss of fingerprint 

information due to over segmentation affect matching against latent fingerprints? Certainly, the later 

issue alone would be a good enough reason to preserve as much of the fingerprint information as 

possible during slap fingerprint segmentation.
A-1

Appendix A

These tables show the False Non-Match Rates (FNMR) at a False Match Rate of 10-4 for the hand 

marked segmentation (GT) boxes using three different matching algorithms. The threshold from the GT 

results remained fixed for the other rows in the table. GTL was the hand marked segmentation (GT) 

adjusted to the minimum tolerance allowed in SlapSegII which was -32 for the left and right sides and -

64 for the top and bottom.

The three matching algorithms were high-end performers in the Proprietary Fingerprint Template (PFT)1

evaluation but with different speed and template size characteristics. Matcher-I had the smallest feature 

template and was the fastest in both template extractions and matching. Matcher-II was two times 

slower than Matcher-I in template extractions, had larger templates, and was only slightly slower in 

matching speed. Matcher-III was 3-4 times slower than Matcher-I for template extraction, had the 

largest template size, and was 2-3 times as slow in matching speed.

 1 http://www.nist.gov/itl/iad/ig/pft.cfm
A-2

02 03 04 05 07 08 09 10 2f 8f

GT 0.0549 0.0577 0.0560 0.1148 0.0431 0.0466 0.0748 0.1677 0.0119 0.0018

GTL 0.0802 0.0842 0.0839 0.1767 0.0735 0.0770 0.1175 0.2675 0.0203 0.0045

A 0.0753 0.0799 0.0808 0.1491 0.0629 0.0671 0.0983 0.2083 0.0191 0.0050

B 0.0598 0.0591 0.0559 0.1151 0.0416 0.0480 0.0742 0.1682 0.0119 0.0021

C 0.0605 0.0580 0.0538 0.1145 0.0412 0.0454 0.0729 0.1677 0.0120 0.0023

E 0.0590 0.0590 0.0557 0.1154 0.0423 0.0464 0.0730 0.1699 0.0122 0.0019

F 0.0584 0.0582 0.0555 0.1160 0.0414 0.0463 0.0710 0.1699 0.0113 0.0020

G 0.0654 0.0605 0.0551 0.1165 0.0434 0.0485 0.0769 0.1714 0.0123 0.0022

H 0.0587 0.0659 0.0576 0.1246 0.0460 0.0491 0.0765 0.1824 0.0134 0.0029

I 0.0624 0.0610 0.0597 0.1197 0.0477 0.0485 0.0778 0.1689 0.0125 0.0019

J 0.0688 0.0739 0.0708 0.1475 0.0737 0.0781 0.1021 0.2048 0.0226 0.0082

Table A.1: FNMR for Matcher I (Threshold from GT at FMR of 10-4)

02 03 04 05 07 08 09 10 2f 8f

GT 0.0148 0.0118 0.0135 0.0294 0.0099 0.0124 0.0183 0.0388 0.0021 0.0005

GTL 0.0198 0.0159 0.0163 0.0407 0.0143 0.0180 0.0256 0.0648 0.0025 0.0005

A 0.0275 0.0226 0.0262 0.0451 0.0219 0.0265 0.0326 0.0646 0.0030 0.0007

B 0.0203 0.0133 0.0148 0.0319 0.0110 0.0149 0.0200 0.0439 0.0017 0.0005

C 0.0200 0.0128 0.0140 0.0299 0.0104 0.0143 0.0183 0.0414 0.0017 0.0006

E 0.0197 0.0125 0.0142 0.0298 0.0096 0.0137 0.0190 0.0425 0.0018 0.0005

F 0.0202 0.0125 0.0144 0.0307 0.0094 0.0136 0.0191 0.0421 0.0017 0.0006

G 0.0256 0.0139 0.0149 0.0295 0.0127 0.0171 0.0214 0.0434 0.0031 0.0006

H 0.0184 0.0202 0.0158 0.0409 0.0144 0.0184 0.0234 0.0593 0.0027 0.0009

I 0.0229 0.0147 0.0188 0.0350 0.0168 0.0166 0.0239 0.0443 0.0026 0.0006

J 0.0293 0.0282 0.0305 0.0614 0.0435 0.0479 0.0520 0.0725 0.0089 0.0039

Table A.2: FNMR for Matcher II (Threshold from GT at FMR of 10-4)

02 03 04 05 07 08 09 10 2f 8f

GT 0.0233 0.0180 0.0172 0.0420 0.0191 0.0225 0.0289 0.0602 0.0063 0.0020

GTL 0.0319 0.0245 0.0239 0.0618 0.0284 0.0316 0.0414 0.0972 0.0088 0.0026

A 0.0379 0.0311 0.0320 0.0628 0.0338 0.0363 0.0457 0.0876 0.0105 0.0028

B 0.0286 0.0185 0.0181 0.0449 0.0201 0.0221 0.0305 0.0642 0.0062 0.0014

C 0.0297 0.0191 0.0169 0.0425 0.0196 0.0226 0.0311 0.0616 0.0062 0.0016

E 0.0282 0.0187 0.0176 0.0436 0.0192 0.0223 0.0300 0.0607 0.0063 0.0017

F 0.0278 0.0194 0.0172 0.0430 0.0186 0.0230 0.0304 0.0616 0.0062 0.0014

G 0.0334 0.0190 0.0182 0.0443 0.0206 0.0000 0.0326 0.0613 0.0067 0.0013

H 0.0266 0.0250 0.0192 0.0511 0.0230 0.0256 0.0317 0.0743 0.0070 0.0014

I 0.0310 0.0199 0.0219 0.0466 0.0253 0.0250 0.0344 0.0623 0.0071 0.0014

J 0.0328 0.0269 0.0289 0.0713 0.0487 0.0485 0.0559 0.0890 0.0119 0.0048

Table A.3: FNMR for Matcher III (Threshold from GT at FMR of 10-4)
B-1

Appendix B

These tables show the percent change in the matcher confidence score compared to the hand marked 

segmentation boxes (GT) for single finger, two finger and eight finger combinations and three different 

matchers. GTL was the hand marked segmentation (GT) adjusted to the minimum tolerance allowed in 

SlapSegII which was -32 for the left and right sides and -64 for the top and bottom. The threshold was 

chosen based on the GT data at False Match Rate (FMR) 10-4 and fixed for all other rows in the table.

The three matching algorithms were high-end performers in the Proprietary Fingerprint Template (PFT)1

 1 http://www.nist.gov/itl/iad/ig/pft.cfm

evaluation but with different speed and template size characteristics. Matcher-I had the smallest feature 

template and was the fastest in both template extractions and matching. Matcher-II was two times 

slower than Matcher-I in template extractions, had larger templates, and was only slightly slower in 

matching speed. Matcher-III was 3-4 times slower than Matcher-I for template extraction, had the 

largest template size, and was 2-3 times as slow in matching speed.
B-2

02 03 04 05 07 08 09 10 2f 8f

GTL -7.57 -7.73 -7.49 -11.81 -8.58 -7.50 -9.95 -15.89 -16.33 -9.68

A -4.12 -4.36 -5.24 -6.07 -4.66 -3.76 -4.44 -6.32 -8.90 -4.92

B -0.29 0.00 0.07 0.20 0.12 -0.16 0.23 0.01 -0.20 -0.01

C -0.33 -0.01 0.12 0.34 0.05 -0.05 0.29 0.20 -0.39 0.03

E -0.22 -0.00 -0.09 0.09 0.11 -0.01 0.30 0.02 -0.28 -0.02

F -0.23 0.09 0.06 -0.01 0.13 -0.03 0.29 0.05 -0.27 -0.00

G -1.21 -0.58 -0.28 -0.17 -0.15 -0.41 -0.14 -0.13 -1.35 -0.42

H -0.54 -1.20 -0.38 -0.89 -0.40 -0.53 -0.07 -1.72 -1.08 -0.76

I -0.52 -0.29 -0.58 -0.48 -0.49 -0.39 -0.37 -0.25 -1.05 -0.44

J -1.95 -2.67 -2.37 -4.07 -3.35 -3.69 -3.58 -5.27 -5.77 -3.47

Table B.1: Percent Change in Average Matcher Score Compared to GT for Matcher I

02 03 04 05 07 08 09 10 2f 8f

GTL -11.34 -10.69 -11.03 -15.51 -13.97 -12.54 -13.51 -20.35 -26.45 -13.24

A -5.95 -6.16 -7.96 -8.25 -7.99 -6.24 -6.46 -7.84 -14.68 -7.00

B -1.13 -0.57 -0.48 -0.50 -0.79 -0.85 -0.56 -0.29 -1.92 -0.65

C -1.08 -0.69 -0.32 -0.21 -0.68 -0.71 -0.49 -0.10 -1.82 -0.53

E -0.47 -0.28 -0.33 -0.41 -0.22 -0.40 -0.20 -0.21 -0.81 -0.31

F -0.54 -0.25 -0.21 -0.44 -0.26 -0.43 -0.12 -0.11 -0.90 -0.28

G -1.40 -0.30 -0.15 0.02 -0.48 -0.55 -0.25 0.07 -1.86 -0.42

H -0.56 -1.25 -0.44 -1.21 -0.89 -1.00 -0.47 -1.58 -1.44 -0.90

I -0.93 -0.66 -1.18 -1.24 -1.09 -0.93 -0.93 -0.82 -2.16 -0.95

J -2.14 -3.09 -2.05 -4.18 -3.39 -3.89 -2.88 -6.09 -5.94 -3.39

Table B.2: Percent Change in Average Matcher Score Compared to GT for Matcher II

02 03 04 05 07 08 09 10 2f 8f

GTL -18.11 -17.37 -19.34 -22.84 -23.87 -20.86 -21.16 -28.01 -46.88 -21.13

A -9.73 -9.22 -12.48 -12.25 -12.61 -9.50 -9.49 -11.39 -25.15 -10.72

B -0.95 -0.67 0.00 0.26 -0.73 -0.81 -0.12 -0.16 -1.92 -0.45

C -1.18 -0.62 -0.12 0.64 -0.60 -0.69 -0.19 0.04 -2.20 -0.46

E -0.01 -0.19 -0.07 0.23 0.19 -0.17 0.29 0.30 0.06 -0.01

F -0.12 -0.05 -0.05 -0.21 0.06 -0.16 0.14 0.09 -0.44 -0.10

G -1.77 -0.75 -0.21 0.01 -1.31 -1.00 0.05 -0.39 -3.63 -0.79

H -0.38 -1.64 -0.48 -0.89 -0.06 -0.98 -0.32 -1.66 -0.81 -0.80

I -0.53 -0.54 -1.19 -0.79 -0.73 -0.89 -0.85 -1.08 -1.71 -0.86

J 0.08 1.19 1.54 -3.39 -0.14 0.43 0.45 -7.32 0.16 -0.48

Table B.3: Percent Change in Average Matcher Score Compared to GT for Matcher III
C-1

Appendix C

The six tables in this appendix show the actual number of False Non-Matches (FNM) and False Matches 

(FM) for single finger, two-finger, and eight finger combinations and three different matching 

algorithms. GT was the results for the hand marked segmentation boxes, GTL was the results for GT 

adjusted to the minimum tolerance allowed in SlapSegII, which was -32 for left and right sides, -64 for 

the top and bottom. The matcher threshold was chosen based on the GT data (first row in the table) at 

FMR of 10-4 and fixed for all other rows in each table.

The three matching algorithms were high-end performers in the Proprietary Fingerprint Template (PFT)1

evaluation but with different speed and template size characteristics. Matcher-I had the smallest feature 

template and was the fastest in both template extractions and matching. Matcher-II was two times 

slower than Matcher-I in template extractions, had larger templates, and was only slightly slower in 

matching speed. Matcher-III was 3-4 times slower than Matcher-I for template extraction, had the 

largest template size, and was 2-3 times as slow in matching speed.

 1 http://www.nist.gov/itl/iad/ig/pft.cfm
C-2

02 03 04 05 07 08 09 10 2f 8f

GT 1239 1303 1264 2592 849 919 1476 3311 149 23

GTL 1813 1899 1898 3989 1448 1520 2322 5272 252 57

A 1699 1803 1826 3375 1239 1318 1938 4097 239 63

B 1350 1337 1261 2600 819 943 1462 3317 147 27

C 1365 1308 1212 2589 812 892 1434 3315 152 30

E 1330 1329 1257 2603 834 911 1438 3349 153 24

F 1321 1314 1255 2613 815 912 1398 3348 139 26

G 1480 1359 1248 2644 858 955 1515 3364 154 28

H 1331 1486 1310 2812 906 967 1507 3597 168 37

I 1407 1375 1347 2701 939 959 1530 3326 157 25

J 1555 1663 1597 3336 1451 1537 2016 4046 282 103

Table C.1: Number of FNM for Matcher I (Threshold from GT at FMR of 10-4)

02 03 04 05 07 08 09 10 2f 8f

GT 9 9 9 9 8 8 8 8 5 5

GTL 17 10 13 12 14 10 7 5 12 4

A 15 7 11 12 16 9 7 8 8 2

B 10 9 6 11 18 7 5 6 7 6

C 10 6 4 6 13 6 8 1 4 6

E 11 7 7 7 15 5 6 6 8 5

F 10 9 7 3 12 4 7 6 10 10

G 15 11 4 8 14 5 7 2 5 2

H 15 10 5 7 17 13 8 4 12 0

I 13 6 4 5 13 6 8 6 9 3

J 12 5 6 14 12 7 4 7 6 2

Table C.2: Number of FM for Matcher I (Threshold from GT at FMR of 10-4)

02 03 04 05 07 08 09 10 2f 8f

GT 334 267 304 664 197 245 363 767 26 7

GTL 448 357 369 919 285 354 505 1279 32 7

A 621 511 592 1018 436 522 646 1276 37 9

B 458 298 335 720 219 293 394 869 22 6

C 452 288 317 673 206 281 361 819 21 7

E 446 281 322 670 188 271 374 837 23 6

F 458 284 325 699 185 269 377 832 22 7

G 578 315 335 665 250 337 421 856 39 7

H 413 454 355 924 285 363 462 1167 34 12

I 516 331 424 787 331 326 472 877 34 8

J 663 636 691 1387 857 941 1023 1426 112 50

Table C.3: Number of FNM for Matcher II (Threshold from GT at FMR of 10-4)
C-3

02 03 04 05 07 08 09 10 2f 8f

GT 9 8 8 9 7 8 9 9 5 4

GTL 24 34 24 57 29 25 40 87 24 22

A 11 14 6 27 19 11 24 25 10 5

B 6 8 8 12 5 6 11 8 5 5

C 9 6 6 13 5 5 9 13 4 5

E 7 5 7 13 6 7 12 16 6 2

F 5 6 4 14 3 6 13 16 4 2

G 6 9 9 9 12 7 8 15 4 1

H 4 10 11 12 8 6 8 11 6 6

I 5 10 8 8 7 8 7 14 4 4

J 11 15 8 16 11 9 14 18 6 6

Table C.4: Number of FM for Matcher II (Threshold from GT at FMR of 10-4)

02 03 04 05 07 08 09 10 2f 8f

GT 530 408 389 948 375 444 571 1185 79 26

GTL 720 554 540 1396 561 621 815 1912 111 33

A 859 705 724 1416 665 715 899 1723 129 35

B 648 420 410 1016 393 473 600 1265 79 19

C 670 431 384 957 387 448 613 1212 78 20

E 639 421 399 985 380 461 596 1196 79 22

F 631 437 390 972 363 456 603 1211 78 17

G 754 429 411 999 405 474 641 1208 85 16

H 603 567 434 1153 453 503 634 1462 88 18

I 700 451 494 1054 501 494 675 1228 90 18

J 741 608 652 1613 957 952 1098 1754 150 60

Table C.5: Number of FNM for Matcher III (Threshold from GT at FMR of 10-4)

02 03 04 05 07 08 09 10 2f 8f

GT 10 10 10 9 8 8 8 8 5 7

GTL 30 18 25 15 9 11 12 20 3 11

A 19 9 15 11 7 6 8 8 2 3

B 9 8 12 5 3 1 11 6 3 6

C 11 7 11 11 7 3 11 11 3 11

E 13 8 18 6 4 1 4 4 6 9

F 9 14 26 7 7 3 8 6 3 4

G 12 11 20 6 6 0 6 5 1 6

H 18 12 13 7 9 4 4 4 2 4

I 16 9 13 10 7 4 9 9 0 7

J 25 12 21 8 5 2 2 12 1 2

Table C.6: Number of FM for Matcher III (Threshold from GT at FMR of 10-4)
D-1

Appendix D

This appendix has plots of False Non-Match Rate (FNMR) vs False Match Rate (FMR) for single finger, 

two finger, and eight finger combinations using three different matching algorithms. The segmentation 

boxes used were from the segmentation algorithms in SlapSegII. Note that the y-axis (FNMR) scale 

changes between the single finger, two finger and eight finger plots.

The three matching algorithms were high-end performers in the Proprietary Fingerprint Template (PFT)1

evaluation but with different speed and template size characteristics. Matcher-I had the smallest feature 

template and was the fastest in both template extractions and matching. Matcher-II was two times 

slower than Matcher-I in template extractions, had larger templates, and was only slightly slower in 

matching speed. Matcher-III was 3-4 times slower than Matcher-I for template extraction, had the 

largest template size, and was 2-3 times as slow in matching speed.

 1 http://www.nist.gov/itl/iad/ig/pft.cfm
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Appendix E

This appendix has plots of False Non-Match Rate (FNMR) vs False Match Rate (FMR) for single finger, 

two finger, and eight finger combinations using three different matching algorithms. The segmentation 

boxes were the hand marked boxes (GT), the minimum tolerance limits allowed in SlapSegII (GTL), 

and 16 pixels inside the minimum tolerance limits (GTL2). The minimum tolerance allowed in 

SlapSegII was -32 pixels for the left and right sides, -64 pixels for the top, and -64 pixels for the bottom.

Note that the y-axis (FNMR) scale changes between the single finger, two finger and eight finger plots.

The three matching algorithms were high-end performers in the Proprietary Fingerprint Template (PFT)1

evaluation but with different speed and template size characteristics. Matcher-I had the smallest feature 

template and was the fastest in both template extractions and matching. Matcher-II was two times 

slower than Matcher-I in template extractions, had larger templates, and was only slightly slower in 

matching speed. Matcher-III was 3-4 times slower than Matcher-I for template extraction, had the 

largest template size, and was 2-3 times as slow in matching speed.

 1 http://www.nist.gov/itl/iad/ig/pft.cfm
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