Slap Fingerprint Segmentation Evaluation 2004 

Analysis Report 

Executive Summary 

Bradford Ulery1, Austin Hicklin1, Craig Watson2, Michael Indovina2, Kayee Kwong1

1

 Mitretek Systems 

2

 National Institute of Standards and Technology 

8 March 2005 

Abstract 

The Slap Fingerprint Segmentation Evaluation 2004 (SlapSeg04) was conducted to assess the 

accuracy of algorithms used to segment slap fingerprint images into individual fingerprint images. 

Thirteen slap segmentation applications from ten different organizations were evaluated using data 

from seven government sources. The source of data, the segmentation software used, and the 

scoring criteria used were each found to have a significant impact on accuracy. The most accurate 

segmenters produced at least three highly matchable fingers and correctly identified finger positions 

in from 93% to over 99% of the slap images, depending on the data source. The data source had a 

much greater effect on success rate than whether the images were collected using livescan devices or 

paper. Most segmenters achieved comparable accuracies on the better quality data, but there were 

significant differences among segmenters when processing poor quality data. Some segmenters are 

capable of identifying many, but not all, problem slaps: failure rates could be cut substantially by 

allowing some of the slaps to be recaptured or rejected. 

 

Disclaimer 

These tests were performed for the Department of Justice in accordance with section 303 of the 

Border Security Act, codified as 8 U.S.C. 1732. 

Specific hardware and software products identified in this report were used in order to perform the 

evaluations described in this document. In no case does such identification imply recommendation 

or endorsement by the National Institute of Standards and Technology, nor does it imply that the 

products and equipment identified are necessarily the best available for the purpose. 
SLAPSEG04 ANALYSIS REPORT EXECUTIVE SUMMARY

1 Introduction 

The Slap Fingerprint Segmentation Evaluation 2004 (SlapSeg04) was conducted to assess the 

accuracy of algorithms used to segment slap fingerprint images into individual fingerprint images. 

• Slap fingerprints (slaps) are taken by simultaneously pressing the four fingers of one hand 

onto a scanner or fingerprint card. Slaps are also known as four-finger simultaneous plain 

impressions. 

• Slap segmentation is the process by which a slap image (a four-finger simultaneous plain 

impression) is divided into four images of the individual fingers. 

SlapSeg04 was conducted by the National Institute of Standards and Technology (NIST) on behalf 

of the Department of Justice (DOJ) Justice Management Division (JMD), IDENT/IAFIS 

Integration Project. Additional partners include the US-VISIT Program Office of the U.S. 

Department of Homeland Security (DHS), and the Federal Bureau of Investigation (FBI). 

The use of slap fingerprints for background checks is being considered in a variety of U.S. 

Government fingerprint systems (including US-VISIT and IAFIS). Automated segmentation of slap 

fingerprints is known to have an associated error rate, but no rigorous evaluation of current slap 

segmentation algorithms had ever been conducted before SlapSeg04. Knowing whether existing 

segmentation software is of sufficient accuracy for operational use will be of practical interest and 

value to policymakers. 

2 Purpose 

This evaluation was conducted to determine the accuracy of existing slap segmentation algorithms on 

a variety of operational-quality slap fingerprints. The study incorporates several subtly different 

objectives: 

• Measurement of the accuracy of state-of-the-art slap segmentation software 

• Assessment of the practicality of segmenting operational quality slap fingerprints 

• Determination of the factors that cause slap segmentation and matching to fail 

• Assessment of the ability of segmentation algorithms to detect when segmentation is 

successful 

The sponsors of this study want to determine the practicality of these operational scenarios: 

• Batch segmentation of large databases of livescan, paper, or mixed livescan/paper slap 

fingerprints 

• Real-time segmentation of livescan slap fingerprints at the time of capture 

3 Evaluation Methodology 

Thirteen segmenters (segmentation applications) from ten organizations were evaluated in SlapSeg04. 

Participants 

• 123ID 

• Aware (two entries) 

• Cogent (two entries) 

• FBI IAFIS 

• NEC 

• NIST 

• SAGEM Morpho (two entries) 

• Smiths Heimann Biometrics (SHB) 

• Sonda 

• UltraScan 

Table 1: Participating organizations 

Segmenters were evaluated based on their abilities to 1) produce highly matchable images, 2) identify 

finger positions, and 3) detect segmentation failures. They were evaluated using a variety of data from 

8 March 2005 Page 2 of 8 
SLAPSEG04 ANALYSIS REPORT EXECUTIVE SUMMARY

seven different sources. The fingerprints were acquired from inked paper cards (subsequently 

scanned), or by livescan devices. The images had a wide range of operational and non-operational 

quality. The evaluation datasets are summarized in Table 2. 

Dataset Type Slaps Comments 

Ohio (OhioI) Livescan 1,850 Fingerprints collected from Ohio prisoners, under carefully 

controlled conditions. The only non-operational dataset. 

FBI 12k Search (12kL) Livescan 5,000 Criminal and civil livescan fingerprints from IAFIS. 

Benefits (BEN) Livescan 5,000 Fingerprints from DHS/BICE (formerly INS) Benefits data 

DoD BAT (BAT) Livescan 2,634 Fingerprints collected by DoD. 

IDENT/IAFIS (II) Livescan 5,000 Fingerprints collected in secondary processing for IDENT/IAFIS.

FBI 12k File (12kP) Paper 5,000 Criminal paper fingerprints from IAFIS. 

Texas (TX) Paper 5,000 Criminal fingerprints from Texas Department of Public Safety. 

Total 29,484 

Table 2: Sources of slap fingerprint data 

Each of the thirteen segmentation applications was tested on these 29,484 slap images. Each slap 

image contained four fingerprints (with a few exceptions) and was associated with a set of four rolled 

fingerprints, so the evaluation data included a total of nearly 240,000 individual fingerprints. 

The segmented images produced by a segmentation application from a slap image were validated by 

matching them against the rolled fingerprints from the same hand. Three of the more accurate 

fingerprint matchers from the NIST SDK tests (see fingerprint.nist.gov/SDK/) were used to validate 

the segmentation, with manual review of problem cases. 

High matcher score thresholds were set to identify near-definitive matches, or “highly matchable” 

fingerprints. Low thresholds were set to identify “marginally matchable” fingerprints. If a segmented 

slap fingerprint and the corresponding rolled fingerprint scored above the high threshold (on at least 

one matcher), the images were considered matches. Fingerprints that scored below the low threshold 

(on all matchers) were considered non-matches. 

An operational system may use different matchers and, therefore, different score thresholds, and 

would presumably fuse the results for all segmented fingers to improve overall accuracy. Matching 

accuracy as measured in this report will differ from any operational system. 

4 Results 

Three factors were identified that strongly influence the accuracy reported for slap segmentation: 

• The data being evaluated 

• The segmentation software used 

• The definition of successful segmentation 

Figure 1 gives an overview of the proportion of each dataset that could be successfully segmented 

and matched. Note that the dataset and segmentation software used both have an effect on accuracy, 

as well as how accuracy is defined. 

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SLAPSEG04 ANALYSIS REPORT EXECUTIVE SUMMARY

Segmentation and Matching Accuracy across Datasets

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

OhioI

(Live)

12kL

(Live)

BEN

(Live)

BAT

(Live)

II

(Live)

12kP

(Paper)

TX

(Paper)

0 Fingers (Any segmenter)

1 Finger (Any segmenter)

2 Fingers (Any segmenter)

3 Fingers (Any segmenter)

4 Fingers (Any segmenter)

4 Fingers (Most segmenters)

4 Fingers (All segmenters)

Figure 1: Levels of segmentation accuracy achieved for the various datasets 

The categories of segmentation accuracy were defined as follows: 

• 4 fingers (All segmenters) denotes slaps where every segmenter produced four highly 

matchable fingerprints – the easiest cases. 

• 4 fingers (Most segmenters) denotes slaps where the majority of segmenters produced 

four highly matchable fingerprints. 

• N fingers (Any segmenter) denotes slaps where at least one of the segmenters produced 

N highly matchable fingerprints, where N = 0 to 4. Note that requiring four fingers to match 

is much more restrictive than requiring three fingers. 

In Figure 1, the three shades of green show that four highly matchable fingers can be segmented 

from most slaps, but not all segmenters are equally successful. In every dataset, over 95% of the slaps 

contain three or more highly matchable fingers. The most serious problems are those slaps that do 

not contain three or more highly matchable fingers. 

Some of the datasets could be segmented with very high accuracy. The OhioI dataset was nonoperational data collected under controlled conditions. Two of the operational datasets (12kL and 

BEN) could be segmented at rates approaching the OhioI data. 

Conventional wisdom would have predicted that all livescan data would be easier to segment than 

any paper data. This is true for the least accurate segmenters, which successfully segmented four 

fingers on less than half of the paper images. However, the livescan BAT and II datasets have a 

higher proportion of slaps with fewer than three highly matchable fingers than any of the other 

datasets. Clearly other factors are more important in determining accuracy than whether the images 

were livescan or paper. 

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SLAPSEG04 ANALYSIS REPORT EXECUTIVE SUMMARY

Failures to match one or more fingers in a slap were typically due to some combination of poor 

image quality or database problems (such as sequence errors or missing rolled images), segmentation 

failures, and matcher false rejects. The datasets are representative of the sources from which they 

were selected: they include problems such as administrative errors, sequence errors, and poor quality 

rolls that limit segmentation and matching accuracy so that 100% accuracy is not achievable for any 

dataset. 

5 Comparison of Segmenters 

Figure 2 shows the proportion of slaps from which each segmenter produced four highly matchable 

fingers, with finger positions correctly identified. Most segmenters had similar results on the three 

easiest datasets. Accuracy on the other four datasets was distinctly lower, and differences among 

segmenters are much more apparent. Overall segmentation accuracy ranged from 61% to 98%; the 

three most accurate segmenters ranged from 77% to 98%. 

4 Highly Matchable Fingers per Slap, with Finger Positions Identified

60%

70%

80%

90%

100%

123ID

Aware1Aware2

Cogent1Cogent2

IAFIS

NEC

NIST

Sagem1Sagem2

SHB

Sonda

UltraScan

Percentage of Slaps

OhioI

12kL

BEN

BAT

II

12kP

TX

Figure 2: Proportion of each dataset with four highly matchable fingerprints (and correctly identified 

finger positions) for the 13 segmenters tested 

Figure 3 shows corresponding results when three or more highly matchable fingerprints were 

segmented from each slap. In the cases in which only three fingers were highly matchable, the fourth 

finger was present and marginally matchable about half of the time. Although this measure requires 

only three fingerprints, segmenters still must correctly identify the finger positions. It might be noted 

that when only three fingerprints can be segmented from a slap, correct identification of the finger 

positions becomes operationally more important. Segmentation accuracy ranged from 75% to over 

99%; the two most accurate segmenters ranged from 93% to over 99%. 

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SLAPSEG04 ANALYSIS REPORT EXECUTIVE SUMMARY

3 or More Highly Matchable Fingers per Slap, with Finger Positions Identified

60%

70%

80%

90%

100%

123ID

Aware1Aware2

Cogent1Cogent2

IAFIS

NEC

NIST

Sagem1Sagem2

SHB

Sonda

UltraScan

Percentage of Slaps

OhioI

12kL

BEN

BAT

II

12kP

TX

Figure 3: Proportion of each dataset with three or more highly matchable fingerprints (and correctly 

identified finger positions) for the 13 segmenters tested 

6 Ability to Detect Problems 

If missegmented slaps can be identified, it may be possible for them to be recaptured, rejected, or 

handled as special cases, depending on the operational scenario. The results above do not consider 

the effect of detecting problem cases and the possibility of recapturing or rejecting those slaps. If 

problems are detected and may be corrected, the accuracy rates would, of course, be better than 

those stated above. 

SlapSeg04 participants were invited to provide a segmentation quality score for each output segment. 

These quality scores were augmented with additional information on the number of fingerprints 

segments produced and an automated measure of exceptionally poor image quality. For each 

segmenter, these scores were used with a sliding quality threshold to compute match rates versus 

recapture/reject rates. 

At each quality score threshold: 

• a recapture/reject rate was determined based on the proportion of the slaps below that 

quality threshold 

• the match rate was determined based on the proportion of the slaps above that quality 

threshold that had four marginally matchable fingers 

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SLAPSEG04 ANALYSIS REPORT EXECUTIVE SUMMARY

Figure 4: Percentage of slaps with four highly matchable fingers in terms of recapture/reject rates (II 

data) 

Figure 4 shows the various segmenters’ ability to detect slaps that did not have four highly matchable 

fingers, using the II dataset. II is used as an example because it has more problem data than most of 

the other datasets, and therefore emphasizes the effect of the recapture/rejection rate. 

As an example, note the line for Sonda (the top-most light blue line). Sonda produced four highly 

matchable fingerprints per slap for about 81% of the II data (left edge of chart). If 5% of the slaps 

were recaptured or rejected, the accuracy on the remaining slaps would be nearly 85%. If 25% of the 

slaps were recaptured or rejected, the accuracy on the remaining slaps would only reach 90%. An 

accuracy of 95% could be achieved only by rejecting more than 60% of the slaps. Note that no 

fingerprints were recaptured in this study; this analysis addresses the detection of problem slaps, not 

the degree to which recapture improves quality. 

Figure 4 shows that segmenters differ greatly in the proportion of slaps on which they have 

problems, and therefore in the proportion of slaps that might would need to be rejected or 

recaptured. It also shows that no segmenter can identify all of the problem slaps at a low recapture or 

reject rate. 

7 Conclusions 

1. The source of data had more of an effect on segmentation and matching accuracy than any other 

factor. 

2. The relative accuracy of segmenters depends on the criteria and data used. Most of the 

segmenters achieved comparable accuracies on the better quality data, but there were significant 

differences on poor quality data. 

3. Segmentation and matching accuracy can be defined in a variety of ways, based on the number 

of fingers required for success, and matcher score thresholds. There is no single measure of 

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SLAPSEG04 ANALYSIS REPORT EXECUTIVE SUMMARY

accuracy that is appropriate for all possible uses. Reported accuracies depend greatly on which 

measure is used. 

° Two definitions of accuracy likely to be of general use are the ability of a segmenter to 

segment four highly matchable fingerprints from a slap, or the ability of a segmenter to 

segment three or more highly matchable fingerprints from a slap, with all finger positions 

correctly identified. 

° Segmentation accuracy, if defined as four highly matchable fingerprints per slap with finger 

positions correctly identified, ranged from 61% to 98% depending on the source of data. 

The three most accurate segmenters ranged from 77% to 98%. 

° Segmentation accuracy, if defined as three or more highly matchable fingerprints per slap 

with finger positions correctly identified, ranged from 75% to over 99%. The two most 

accurate segmenters ranged from 93% to over 99%. 

° Segmentation accuracy rates would be higher than those stated here if less restrictive 

matcher thresholds were used, which may be appropriate in some operational scenarios. 

4. Segmenters are capable of identifying many, but not all, cases where they fail to produce highly 

matchable segmented images. 

° Some slaps that could not be successfully segmented and matched were not identified by any 

segmenter or by image quality measures. 

° The ability to identify problem slaps varies greatly among segmenters, resulting in great 

variation in expected recapture/reject rates. 

° Some segmenters can accurately determine whether a slap came from a right or left hand, 

and therefore could identify many cases in which the slaps were swapped left for right. 

° The implications of recapture or rejection of data depend on operational requirements. 

5. Two characteristics of the fingerprints that might have been expected to have an obvious effect 

on segmentation and matching accuracy were found to have little or no such effect: 

° Livescan versus paper — Other factors (such as data quality) clearly outweighed whether 

images were from livescan or paper sources. 

° Slap orientation — The orientation (angle of rotation) of the slap images was found to have 

little or no effect on overall accuracy. 

6. The causes of segmentation and matching failure vary depending on the dataset. 

° Database errors, such as invalid slap images or out-of-sequence rolled fingerprints, were 

found in between 0.1% and 2.5% of data, depending on the dataset. 

° Partial, missing, and exceptionally poor quality fingerprints were found in between 0.4% and 

4.8% of data, depending on the dataset. 

° Database errors and quality problems limit segmentation and matching accuracy for all 

datasets. 

° Some failures to segment and match were due to marginal fingerprint quality rather than 

poor fingerprint quality per se. 

8 March 2005 Page 8 of 8