NISTIR 8210

Nail to Nail Fingerprint Challenge

Prize Analysis

Gregory Fiumara

Elham Tabassi

Patricia Flanagan

John Grantham

Kenneth Ko

Karen Marshall

Matthew Schwarz

Bryan Woodgate

Christopher Boehnen

This publication is available free of charge from:

https://doi.org/10.6028/NIST.IR.8210
NISTIR 8210

Nail to Nail Fingerprint Challenge

Prize Analysis

Gregory Fiumara

Elham Tabassi

Patricia Flanagan

Kenneth Ko

Karen Marshall

Bryan Woodgate

Information Access Division

Information Technology Laboratory

John Grantham

Systems Plus, Inc.

Matthew Schwarz

Schwarz Forensic Enterprises

Christopher Boehnen

Intelligence Advanced Research Projects Activity

This publication is available free of charge from:

https://doi.org/10.6028/NIST.IR.8210

April 2018

U.S. Department of Commerce

Wilbur L. Ross, Jr., Secretary

National Institute of Standards and Technology

Walter Copan, NIST Director and Under Secretary of Commerce for Standards and Technology
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Abstract 

In September 2017, the Intelligence Advanced Research Projects Activity held a fngerprint data collection as 

part of the Nail to Nail Fingerprint Challenge. Participating Challengers deployed devices designed to collect 

an image of the full nail to nail surface area of a fngerprint — equivalent to a rolled fngerprint — from an 

unacclimated user without assistance from a trained device operator. Images captured from these devices 

were searched against a set of traditionally-captured operator-assisted rolled fngerprints. Thousands of 

latent fngerprints were also searched against the images. 

Key words 

acquisition; biometrics; capture devices; data; fngerprints; latent; prototypes; rolled; sensors. 

Published Images 

All friction ridge images depicted within this report come from study participants of the Nail to Nail 

Fingerprint Challenge. All study participants consented to the publication and release of images of their 

friction ridges and were compensated for their participation. Collection of these images was overseen by 

both the Johns Hopkins Medicine Institutional Review Board (IRB00138363) and the National Institute of 

Standards and Technology Institutional Review Board (ITL-17-0028b, ITL-17-0028c). Publication and use 

of the images in this report was overseen by the National Institute of Standards and Technology Human 

Subjects Protection Oÿce (ITL-17-0013). Refer to Section 3.2 for more information. 

Disclaimer 

Certain commercial equipment, instruments, or materials are identifed in this document in order to specify 

the development procedure adequately. Such identifcation is not intended to imply recommendation or 

endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the 

materials or equipment identifed are necessarily the best available for the purpose. 

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Table of Contents 

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

2 Nail to Nail Fingerprint Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

3 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

4 Data — Nail to Nail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

5 Data — Latent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 

6 Challengers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

7 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 

8 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 

9 Results — Acquisitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

10 Results — Nail to Nail Identifcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 

11 Results — Latent Identifcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

A Results — Latent Identifcation — By Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

B Podium Finishers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 

C Public Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 

D Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 

List of Tables 

1 Nail to Nail Fingerprint Challenge Prizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

2 Study Participant Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

3 Auxiliary Capture Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 

4 Median Acquisition Time and Timing Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

5 Nail to Nail Identifcation — False Negative Identifcation Rate at a False Positive Identifcation Rate of 10−1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 

6 Nail to Nail Identifcation — Cumulative Match Characteristic — Rank 1 . . . . . . . . . . . 36 

7 Latent Identifcation — False Negative Identifcation Rate at a False Positive Identifcation 

Rate of 10−1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 

8 Latent Identifcation — Cumulative Match Characteristic — Ranks 1, 5, and 10 . . . . . . . 39 

9 Latent Identifcation — False Negative Identifcation Rate at a False Positive Identifcation 

Rate of 10−1 — By Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

10 Latent Identifcation — Cumulative Match Characteristic — Rank 1 — By Activity . . . . . 45 

11 Latent Identifcation — Data Breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 

12 National Institute of Standards and Technology Fingerprint Image Quality 2.0 Values . . . . 48 

List of Figures 

1 Plain and Nail to Nail Impressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

2 Defnition of Moderate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

3 Timeline of Nail to Nail Fingerprint Challenge Events . . . . . . . . . . . . . . . . . . . . . . 3 

4 Image Centering Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

5 Image Flopping Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

6 Examples of Developed Latent Activities — Black Powder . . . . . . . . . . . . . . . . . . . . 14 

7 Examples of Developed Latent Activities — Chemical . . . . . . . . . . . . . . . . . . . . . . 15 

8 Comparison of Digital Camera and FSIS Digitization . . . . . . . . . . . . . . . . . . . . . . . 17 

9 Picture of Glass from Latent Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 

10 Examples of Challenger Captures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

11 Examples of Challenger Captures with Minutia . . . . . . . . . . . . . . . . . . . . . . . . . . 23 

12 Example of Latent Print Cropped after Development with Minutia . . . . . . . . . . . . . . 28 

13 Acquisition Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 

14 Acquisition Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

15 Nail to Nail Identifcation — Detection Error Tradeo ̇ . . . . . . . . . . . . . . . . . . . . . . 35 

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16 Nail to Nail Identifcation — Cumulative Match Characteristic . . . . . . . . . . . . . . . . . 36 

17 Latent Identifcation — Detection Error Tradeo ̇ . . . . . . . . . . . . . . . . . . . . . . . . . 38 

18 Latent Identifcation — Cumulative Match Characteristic . . . . . . . . . . . . . . . . . . . . 39 

19 Latent Identifcation — Detection Error Tradeo ̇ — By Activity . . . . . . . . . . . . . . . . . 44 

20 Latent Identifcation — Cumulative Match Characteristic — By Activity . . . . . . . . . . . 46 

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Executive Summary 

Overview The National Institute of Standards and Technology (NIST) conducted data analysis on fngerprint images acquired during the fngerprint data collection portion of the Nail to Nail Fingerprint 

Challenge1, an Intelligence Advanced Research Projects Activity (IARPA) Prize Challenge. The Challenge 

was conducted to assess the feasibility of creating a fngerprint sensor that produces high-quality rolledequivalent, or nail to nail (N2N), fngerprint images, without relying on a trained device operator. IARPA 

used the analysis presented in this report to award the winners of the Challenge. 

Motivation Capturing a high-quality rolled fngerprint image is a diÿcult task that traditionally has 

always required the use of a trained device operator. The operator physically holds a subject’s fnger and 

repeatedly rolls it on a live scan platen until a high-quality image is produced. This can be an expensive 

and time consuming task, and may additionally make the subject feel uncomfortable. Because of this, many 

organizations that require fngerprinting resort to capturing plain fngerprint impressions. Compared to 

N2N impressions, plain impressions capture a limited region of the fnger, resulting in less information being 

made available for searching with automated fngerprint identifcation algorithms. This lack of searchable 

information is especially detrimental in forensic applications, because latent fngerprints found at crime 

scenes are sometimes formed from areas of the fnger not imaged by a plain impression. The development 

of an acquisition device that can quickly capture a high-quality and complete N2N representation of a fnger 

will encourage use from organizations by promoting greater identifcation accuracy and a higher respect 

for the subject. 

Challenge Eight organizations — comprised of both industry and academia — were selected as fnalists, 

and invited to participate in a fngerprint data collection to exercise their devices. Fingerprint images 

from 331 study participants captured with the Challenger’s devices during the fngerprint data collection 

were provided to NIST, along with traditionally-captured N2N exemplars. Tens of thousands of latent 

fngerprints were also developed from activities performed by the Challenge fngerprint data collection 

study participants. NIST searched Challenger fngerprint images and collected latent fngerprint images 

against an enrollment set of 29 986 091 rolled fngerprint images with an automated fngerprint identifcation 

algorithm and reported accuracy. 

Winners Four prizes were awarded based on the data analysis performed by NIST. To be eligible for 

all prizes, Challengers had to capture fngerprint images from at least 90 % of study participants within 

8 min. 

Speed Prize 

The Speed Prize was awarded to the Challenger achieving the fastest N2N acquisition time. This award 

was won by Advanced Optical Systems2. Second place was won by IDEMIA3 and third place was 

won by Touchless Biometric Systems4 (Section 9.3). 

Gallery Accuracy Prize 

The Gallery Accuracy Prize was awarded to the Challenger whose images were most accurately identifed 

during the N2N identifcation portion of the Challenge. This award was won by Green Bit5. Second 

place was won by Jenetric6 and third place was won by IDEMIA3 (Section 10). 

Latent Accuracy Prize 

The Latent Accuracy Prize was awarded to the Challenger whose images resulted in the most accurate 

identifcations during the latent identifcation portion of the Challenge. This award was won by Green 

1https://challenge.gov/challenge/nail-to-nail-n2n-fngerprint-challenge 

2https://aos-inc.com 

3https://idemia.com 

4https://tbs-biometrics.com 

5http://greenbit.com 

6https://jenetric.com 

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Bit5. Second place was won by Jenetric6 and third place was won by IDEMIA3 (Section 11). Results 

for second and third place relied on a tie-breaker (Appendix B). 

Grand Prize 

The Grand Prize was awarded to the Challenger with the best overall latent identifcation system. To 

qualify, Challengers had to capture images from 90 % of study participants in under 3 min and achieve 

accuracy within 2 % of the traditional N2N capture for both N2N and latent identifcation. Although 

Green Bit5 came the closest, their accuracy in N2N identifcation was not close enough to the traditional 

N2N capture’s accuracy to qualify, and as such, this prize was not awarded (Appendix B). 

Impact A goal of the Nail to Nail Fingerprint Challenge was to identify viable replacements for traditional 

operator-assisted rolled fngerprint capture by investigating acquisition speed and identifcation accuracy 

using fngerprint images captured on a device without an operator’s physical intervention. In terms of speed, 

nearly all Challengers created a device capable of acquiring images as fast as or faster than a skilled device 

operator. However, in terms of accurate N2N identifcations, only Green Bit performed similarly to the 

traditional capture method. The di ̇erence between frst and second place in the Gallery Accuracy Prize was 

nearly ten percentage points. Although study participants had to perform the physical fngerprint capture 

themselves, Challengers were allowed to coach study participants and provide feedback and suggestions 

if the captured fngerprint was not of a good quality. For the purposes of the Nail to Nail Fingerprint 

Challenge, this technique is referred to as moderated capture. 

Identifcation performance awarded in the Latent Accuracy Prize is inconclusive due to overall poor and 

unanticipated results (Section 11.1), including when latent prints are compared to images formed by the 

traditional operator-assisted capture method. 

Additionally, there was no e ̇ort to quantify the di ̇erence in surface area between a fngerprint image 

captured with a Challenger device and a fngerprint image rolled by a skilled operator. The goal of the Nail 

to Nail Fingerprint Challenge was instead to produce a rolled-equivalent image — an image that performs 

as well as the traditional operator-assisted rolled image. By this defnition, the only device that performed 

to the desired level was produced by Green Bit — a live scan device coupled with a “hands-o ̇” operator 

that mimicked the rolling procedure and image quality checks of the traditional method. 

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viii 
N2N Challenge Prize Analysis 1 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

1. Introduction 

In a traditional and controlled fngerprinting environment, there are primarily two representations in which 

a fngerprint impression can be captured. Using a plain or fat representation, a subject’s fnger is depressed 

straight down onto a fat surface, such as the platen of a live scan device or a fngerprint ink card, with 

the subject’s fngernail facing up. Using the rolled representation, a subject’s fnger is oriented such that 

their fngernail is perpendicular to a fat surface. Their fnger is slowly rolled on that fat surface until their 

fngernail is again perpendicular to the surface and facing the opposite direction. The rolled impression type 

is often called nail to nail (N2N) because it captures the surface area of a fnger’s distal phalanx from one edge 

of the fngernail to the other, ideally including the tip. Conversely, plain impressions can only capture the 

surface area on the palmar side of a fnger’s distal phalanx, omitting the sides and the tip. When searching 

fngerprints, the larger surface area of an N2N impression provides more information for the search than 

a plain impression. This is especially important when searching latent fngerprints, as the portion of the 

latent fngerprint being searched may be solely from an area not imaged by a plain impression. Fig. 1 shows 

the di ̇erence between a plain and N2N impression. 

Fig. 1. An example of a plain (left) and rolled or N2N (second from left) impression of the same fnger. In the middle, the plain 

impression is superimposed in red on the rolled impression. Areas of overlap in the rolled impression are highlighted in red in the 

image second from the right, and removed in the rightmost image. The N2N impression has more information available for searching. 

Although they provide more detail, N2N fngerprints are more diÿcult to capture. A skilled operator 

is typically employed when capturing N2N impressions. The operator holds and controls the subject’s 

fnger throughout the capture process. Without an operator that knows how to examine a fngerprint for 

quality, it is common to produce motion blur (rolling too fast), smudges (non-uniform pressure), distortions 

(movement on the platen), or to simply not capture the entire desired fnger surface area. Conversely, plain 

fngerprint impressions are often produced by subjects directly, achieving high quality plain representations 

without operator assistance. 

Requiring a skilled operator in N2N fngerprint collection can constrain the feasibility, fexibility, and cost 

of collection in a number of scenarios, such as high-throughput environments like a port of entry. As such, 

many fngerprint collection environments forego collecting these more detailed fngerprints and instead opt 

for a simpler and more cost-e ̇ective plain impression. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
2 N2N Challenge Prize Analysis

2. Nail to Nail Fingerprint Challenge 

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In an e ̇ort to reduce the burden of collecting N2N fngerprints and 

eliminate the fallback plan of solely collecting plain impressions, 

Intelligence Advanced Research Projects Activity (IARPA) held the 

frst Nail to Nail Fingerprint Challenge in 2017. The Challenge 

aimed to identify N2N fngerprint capture solutions that could support high-quality live N2N capture without requiring the physical 

intervention of a human operator to roll a subject’s fnger. The participating solution could be newly-engineered fngerprint capture 

hardware, specialized software with existing fngerprint capture 

hardware, or anything else imaginable. 

The Nail to Nail Fingerprint Challenge did not enforce complete autonomy for capture on the part of the 

subject. An operator could still be present to supervise, coach, and provide feedback to the subject towards 

them providing a high-quality N2N print, but could not physically touch the subject or instruct them to 

apply any sort of fngerprint matrix (e.g., lotion or natural oils from the forehead or ears). Per the defnition 

in Fig. 2, the term moderated capture is used to defne the level of capture assistance allowed. 

moderate /"mAd@reIt/ (v.): 

• to abate the excessiveness of 

• to reduce the amount of 

• to preside over 

moderated /"mAd@reItId/ (ppl., a.): 

• reasonably restricted and limited 

• rendered moderate 

The Oxford English Dictionary [1] 

Fig. 2. The defnition of moderate and moderated, according to The Oxford English Dictionary [1]. The term moderated capture is used 

to defne the N2N capture technique allowed during the Nail to Nail Fingerprint Challenge. Challengers were allowed to provide 

instructions and feedback to study participants throughout the capture process, but were not permitted to touch them. This is a lesser 

version of a traditional N2N capture, where the study participant renders full control to an operator, but is not fully uncontrolled 

The desired outcome of the Nail to Nail Fingerprint Challenge is for a collection device to be created that 

allows for better quality fngerprint data to be collected, while reducing the time and cost to operate a comparable rolled capture station. This outcome is a mutually benefcial scenario for fngerprint identifcation 

systems and organizations that require fngerprint capture. 

2.1 Participation 

Information about the Nail to Nail Fingerprint Challenge was made public in September 2016. In order to 

participate, potential Challengers needed to submit an abstract regarding their proposed N2N fngerprint 

capture solution. By July 2017, sample N2N fngerprint images captured by a Challenger’s device were 

required to be submitted to a three-person United States Government (USG) judging panel, known as the 

N2N Judging Committee, along with a video recording of the device in operation. The N2N Judging 

Committee assessed the quality of the images and the content of the video, and used that information to 

select eight fnalists. These fnalists were invited to the Washington, D.C. metro area in September 2017 to 

participate in a live fngerprint data collection with their devices. Fingerprints captured from each of the 

Challenger’s devices were compared to fngerprints captured with traditional operator-assisted methods. 

Latent fngerprints were also captured and searched against enrollment sets of Challenger-collected N2N 

images. A timeline of Nail to Nail Fingerprint Challenge events is presented in Fig. 3. 

Throughout this report, Challengers are referred to by a letter code, A through H. A mapping for these codes 

can be found at the bottom of each page. The Greek letter Omega (Ω) represents the data captured using 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E Low-quality White Envelope 4F Greeting Card and Envelope 4G Manila Envelope 5A Photo Paper 5B Glossy Magazine 

5C U.S. Currency 6A Stamp 6B Address Label 6C Clear Packing Tape 6D Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 3 

Registration and 

Proof of Concept 

Review, Finalist Gallery Accuracy 

Prize Winner 

Feasibility Review Selection Announced 

February 2017 July 2017 November 2017 

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September 2016 March 2017 September 2017 March 2018 

Challenge 

Announcement 

Challengers 

Construct Devices 

Fingerprint 

Data Collection 

All Winners 

Announced 

Fig. 3. Timeline of Nail to Nail Fingerprint Challenge events. Challengers worked on their devices for at most one year. 

traditional operator-assisted techniques. 

2.2 Prizes 

There were several monetary prizes eligible to win in the Nail to Nail Fingerprint Challenge. For most 

prizes, the fngerprints captured by the Challenger’s device had to perform comparably to the traditional 

operator-assisted fngerprints in various aspects, such as capture speed and identifcation accuracy. The 

complete list of prizes is recorded in Table 1. If there was no winner for a prize, at the discretion of the N2N 

Judging Committee, the award for that prize could be used to honor second and third place fnishers for a 

di ̇erent prize. 

For an oÿcial list of winners, please refer to the oÿcial IARPA Nail to Nail Fingerprint Challenge website, 

https://challenge.gov/challenge/nail-to-nail-n2n-fngerprint-challenge. 

Prize Criteria Constraints 

Grand Prize 

$100 000 

Gallery Accuracy Prize 

$25 000 

Latent Accuracy Prize 

$25 000 

Speed Prize 

$25 000 

Print Provider Prize (×8) 

$8 000 

Master Builder Prize (×8) 

$2 000 

Best latent 

identifcation system 

Best N2N 

identifcation 

performance 

Best latent 

identifcation 

performance 

Fastest N2N 

acquisition 

Providing data to 

share with the public 

Invited to fngerprint 

collection 

• ≥ 90 % of data captured 

• Acquisition time ≤ 120 % of Ω

(≤ 172.8 s, per Table 4) 

• N2N matching FNIR within 2 % of Ω

• Latent matching FNIR within 2 % of Ω

• ≥ 90 % of data captured 

• All data acquired in ≤ 8 min 

• ≥ 90 % of data captured 

• All data acquired in ≤ 8 min 

• ≥ 90 % of data captured 

• Latent matching FNIR within 80 % of Ω

• Captured data from the live test must 

be made public domain 

• Participation in September 2017 

fngerprint data collection 

Table 1. A description of the eligibility criteria for prizes available to be awarded by IARPA in the Nail to Nail Fingerprint Challenge. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
4 N2N Challenge Prize Analysis

3. Data Collection 

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3.1 Facility 

Johns Hopkins University Applied Physics Laboratory (JHU APL) was chosen by IARPA as the host facility 

for the Nail to Nail Fingerprint Challenge. From 18–21 September 2017, N2N test sta ̇ and Challengers 

transformed much of the Intelligent Systems Center at JHU APL into a secure area for performing a fngerprint data collection. The facility is a typical airport style environment, with climate control, high ceilings, 

and fuorescent lighting. There were no windows in the facility. All Challengers were located in the same 

room, and as such, environmental factors for all Challengers were uniform. 

3.2 Institutional Review Board 

Before any analysis of the performance of Challenger devices could be performed, fngerprint images from 

a number of human subjects needed to be collected. Such a collection of human subject data is bound 

by a rule of ethics known as the Common Rule. All data collected and distributed under the Nail to Nail 

Fingerprint Challenge has been approved separately by Institutional Review Boards (IRBs) associated with 

National Institute of Standards and Technology (NIST) and JHU APL. The analysis and public distribution 

of the coded data was approved by the NIST Human Subjects Protection Oÿce. All study participants 

consented to reproduction of images of their friction ridges captured during the Nail to Nail Fingerprint 

Challenge. 

3.3 Study Participant Pool 

study participants were recruited by a third-party recruitment company on behalf of JHU APL. study 

participants were required to have all 10 fngers imaged. Those with any amputated or bandaged fngers 

when arriving for the fngerprint data collection were excluded. Study participants were required to be 

able to speak, read, and understand the English language, and have full mobility in their fngers, arms, 

and wrists. They also needed the ability to stand for the duration of the fngerprint data collection, though 

in practice, study participants were permitted to sit down between capture stations. Additionally, many 

Challengers preferred that the study participants sit down during fngerprint acquisition. Breakdowns of 

study participant self-reported ages, genders, races, and occupations can be found in Table 2. 

3.4 Baseline Data 

study participants needed to have their fngerprints captured using the traditional operator-assisted technique in order to quantify the performance of the Challenger devices. IARPA invited members of the Federal 

Bureau of Investigation (FBI) Biometric Training Team to the fngerprint data collection to perform this task. 

Each study participant had N2N fngerprint images captured using the traditional operator-assisted rolled 

technique twice, each by a di ̇erent FBI expert. This resulted in two N2N baseline datasets, referred to as 

Baseline Data, that could be used as a comparison against the Challengers. 

To ensure the veracity of recorded N2N fnger positions for Baseline Data, as would be necessary in order 

to make accurate comparisons (Section 4.2), N2N test sta ̇ also captured plain fngerprint impressions in 

a 4-4-2 slap confguration. This capture method refers to simultaneously imaging the index, middle, ring, 

and little fngers on the right hand (4), then repeating the process on the left hand (4), and fnishing with 

the simultaneous capture of the left and right thumbs (2). This technique is a best practice to ensure fnger 

sequence order, since it is physically challenging for a study participant to change the ordering of fngers 

when imaging them simultaneously. 

Operators at both N2N Baseline Data stations and the slap station were given at most 5 min with the study 

participant, totaling 15 min of collection time dedicated to establishing a baseline. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 5 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Age Range Percentage 

Race Percentage 18 to 24 11.8 % 

25 to 29 14.2 % Gender Percentage African American 19.3 % 

30 to 39 23.0 % Asian 7.6 % Female 64.7 % 40 to 49 24.2 % Pacifc Islander 0.6 % Male 35.0 % 50 to 59 22.3 % White 65.0 % No Answer 0.3 % 60 to 69 3.6 % Other 6.0 % 

70 to 79 0.6 % No Answer 1.5 % 

80 to 89 0.3 % 

Employment Status Percentage 

Disabled 1.2 % Employment Type Percentage Full-time 54.4 % 

Homemaker 8.2 % Manual Labor 5.1 % 

Part-time 18.4 % Oÿce Work 49.2 % 

Retired 3.9 % Other 37.5 % 

Unemployed 4.8 % No Answer 8.2 % 

Other 7.6 % 

No Answer 1.5 % 

Table 2. Ages, genders, races, and occupations represented in the Nail to Nail Fingerprint Challenge study participant population, as 

reported by study participants. 

3.5 Challengers 

Each of the eight Challengers were supplied with a table, chairs, and an electrical power strip. Challenger 

tables were separated by sound-dampening panels. Challengers brought their fngerprint capture devices 

and any computer hardware and software necessary to perform fngerprint capture. All software, including 

any necessary to interact with the JHU APL facility Application Programming Interface (API) (Section 3.8), 

were written or procured by the Challenger. 

Each Challenger was given at most 5 min with a study participant, totaling 40 min of collection time dedicated 

to Challengers. Challengers were allowed to process the data they captured after the study participant had 

left their station, but were required to fnish processing all data by the time the facility closed for the 

evening. 

For each study participant, Challengers were to submit an individual image for each fnger usable with a 

commercial o ̇-the-shelf (COTS) fngerprint identifcation system. Challengers could capture more than 

one fnger at a time, but all images submitted through the JHU APL facility API had to depict a single fnger 

per image only (Section 3.8). 

All devices used in the fngerprint data collection underwent a safety review. JHU APL’s device safety 

committee reviewed each device, examining the physical construction, mechanical operation, electrical 

systems, optical components, and other aspects with the potential to cause harm. 

3.6 Latent Fingerprints 

NIST partnered with the FBI and Schwarz Forensic Enterprises (SFE) to design activity scenarios in which 

subjects would likely leave latent fngerprints on di ̇erent objects. The activities and associated objects — 

described in Section 5.1 — were chosen in order to use a number of latent print development techniques 

and simulate the types of objects often found in real law enforcement case work. 

SFE additionally conducted the latent print data collection for the Nail to Nail Fingerprint Challenge. 

Members of SFE instructed study participants to interact naturally with a variety of objects. SFE had 10 min 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
6 N2N Challenge Prize Analysis

Manufacturer Model Impression Capture 

Crossmatch 

Crossmatch 

DigitalPersona 

Futronic 

Green Bit 

MorphoTrust 

Michigan State University 

Guardian USB Plain 

L Scan 1000 Plain 

EikonTouch 710 Plain 

FS88 Plain 

MultiScan 527g Plain 

TouchPrint 5300 Plain 

RaspiReader Plain 

4-4-2 slap 

Upper, lower, and writer’s palm 

10 individual fngers 

10 individual fngers 

Upper, lower, and writer’s palm 

Upper, lower, and writer’s palm 

10 individual fngers 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Table 3. A list of auxiliary capture devices deployed during the Nail to Nail Fingerprint Challenge. The Make and Model columns 

show the make and model of the devices used. The Impression and Capture columns show what types of images were captured using 

the devices. Not all devices were deployed for all fve days of the data collection. The Crossmatch L Scan 1000, MorphoTrust TouchPrint 

5300, and Michigan State University RaspiReader were provided to IARPA without cost and returned after the Nail to Nail Fingerprint 

Challenge. All other devices were procured at market price by IARPA, JHU APL, or NIST. All auxiliary capture devices were operated 

by N2N test sta ̇. 

to interact with each study participant. Not every study participant performed every activity, but the 

activities were distributed such that each study participant performed activities with similar characteristics. 

There were three stations available for performing latent collection, with only two in use by study participants 

at any given time. The third station remained empty for 5 min while SFE sta ̇ completed black powder 

development and preparing for the next study participant. 

3.7 Other 

The facility at JHU APL was large enough to comfortably allow three latent collection stations, eight Challengers, and additional capture devices. Since the participants were already consented and paid for their 

time, additional friction ridge capture devices were deployed and operated by the N2N test sta ̇. This 

allowed for additional traditionally-captured data to be made available to the public. A list of all additional 

devices is shown in Table 3. 

An especially important auxiliary collection device provided for the capture of palm friction ridge data. 

During the latent collection activities, it was likely that palm prints would be left behind on objects. 

Capturing baseline exemplar palm data from all study participants added to the usefulness of the data 

collection. 

3.8 Application Programming Interface 

JHU APL developed an API, referred to as the JHU APL facility API, for Challengers to unify and simplify 

the process of transmitting fngerprint images from Challenger devices to the N2N backend server for 

later analysis by NIST. The JHU APL facility API allowed Challengers to send image data and associated 

metadata, including fnger position, to the N2N backend server. 

The JHU APL facility API also introduced a scheme for associating study participants with a Challenger 

device and their captured fngerprint images. Each study participant was given a wristband printed with a 

unique Quick Response (QR) code. Upon entering each station, a member of the N2N test sta ̇ would scan 

the study participant’s wristband QR code, followed by a QR code identifying the Challenger. This triggered 

a unique identifer to be sent from the N2N backend server to the Challenger’s fngerprint capture software 

and start their fve min capture timer. Fingerprint acquisition times used to award the Speed Prize were 

calculated by subtracting the time of the QR code scan from the time of the fnal submission of fngerprint 

images for a given identifer/device combination. 

Challengers were responsible for implementing the JHU APL facility API themselves, but JHU APL provided ample documentation and technical assistance. N2N test sta ̇ implemented the JHU APL facility API 

for baseline and other non-Challenger biometric devices. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 7 

3.9 Flow 

Much care went into designing the way study participants fowed through the many fngerprint data acquisition stations. In total, study participants needed to make their way around to 16 stations (8 Challengers, 2 

Baseline Data, 1 baseline slap, 1 latent, and 4 auxiliary) before they could leave. 

study participants arrived at JHU APL in groups of 17 — one more subject than there were stations, to 

account for the duration of latent collection. In a separate room, a JHU APL IRB representative guided 

study participants through the informed consent process required before providing their friction ridge 

data. After all participants in a group were consented, they were escorted into the fngerprint data collection 

room. Inside, N2N test sta ̇ members would pair with each study participant and accompany them to their 

specifed starting station. An announcement was made to begin QR code scanning, which started a fve min 

timer. After fve min, study participants had 30 s to move to the next station, where the process would 

repeat. study participants at the latent collection stations stayed in place for two consecutive rotations. 

When each 93 min round of fngerprint data collection had completed (15 fngerprint stations with 5 min 

durations, 1 latent station with a 10 min duration, and 15 transitions with 30 s durations), subjects were paid 

for their time and signed out of the facility. 

On each day, (3 to 5) groups of 17 study participants would make their rounds in the facility. Each day, 

N2N test sta ̇ reversed the direction in which a study participant would move to the adjacent station, to 

reduce the a ̇ects of habituation formed by preceding devices. Additionally, half-way through the week, 

Challengers physically changed location of their stations. Where possible, care was taken to avoid putting 

devices that operated in a similar manner adjacent to each other. 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
8 N2N Challenge Prize Analysis

4. Data — Nail to Nail 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

4.1 Image Format 

For each study participant, Challengers submitted up to 10 individual fngerprint images captured from 

their devices to the N2N backend server via the JHU APL facility API. All images were required to: 

• be encoded in the Portable Network Graphics (PNG) format, 

• contain 8 bits or 16 bits per channel, 

• contain ≥ 196.85 Pixels Per Centimeter (PPCM) (500 Pixels Per Inch or PPI), 

• not make use of ancillary PNG features that change color display, 

• be sized at least 128 pixels × 128 pixels at 196.85 PPCM (500 PPI), 

• be encoded in the grayscale colorspace, using black to represent friction ridges and white to represent 

ridge valleys, 

• be usable as-is with existing COTS template generation and template identifcation algorithms, including being approximately upright and oriented equivalent to an inked impression. 

4.2 Traditional Collection 

The FBI sta ̇ operating the traditional Baseline Data capture stations used Crossmatch L Scan 1000 live scan 

capture devices. These devices captured baseline N2N, 4-4-2 slap, and palm data at 393.7 PPCM (1 000 PPI). 

Each device platen was equipped with a Crossmatch silicone membrane. 

The Crossmatch device was chosen due to the sta ̇’s familiarity with the device, as well as its wide deployment at numerous United States Government biometric enrollment settings, such as ports of entry. It was 

operated at 393.7 PPCM (1 000 PPI) in order to capture the highest amount of detail and to enable future 

research on high-resolution fngerprint images. Crossmatch devices used for capturing Baseline Data were 

procured independently at market price by NIST and JHU APL prior to learning that Crossmatch would be 

participating in the Nail to Nail Fingerprint Challenge. 

4.2.1 Groundtruth 

Mistakes that a ̇ect the integrity of a fngerprint data collection are often inevitable. For instance, the capture 

of a right index fnger might accidentally be coded as a left index fnger, or a software alert indicating that 

an image wasn’t sent to the N2N backend server could be accidentally ignored. Such technical issues would 

impede on the integrity of this or any other fngerprint data collection. 

In the N2N identifcation portion of the Nail to Nail Fingerprint Challenge, all Challenger data was compared 

to the Baseline Data, so it was imperative that the Baseline Data be 100 % accurate. The process of ensuring 

the veracity of the data — checking that fngers were sequenced in order and associated with the correct 

study participant identifer — is known as groundtruthing. 

Each baseline N2N image was matched with segmented versions of the 4-4-2 baseline slap imagery using 

both the NIST-provided Fingerprint Identifcation Algorithm (the Matcher) and other COTS fngerprint 

identifcation algorithms. Low-scoring mated and high-scoring non-mated pairs were examined by visual 

inspection and labeling was corrected as necessary. As an added check, the Matcher was used to compare 

both sets of baseline N2N images to each other. Low-scoring mated and high-scoring non-mated pairs were 

examined by visual inspection and labeling was corrected as necessary. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 9 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

4.3 Challengers 

Challengers were required to submit images to the N2N backend server via the JHU APL facility API in the 

format specifed in Section 4.1. Challengers were ultimately responsible for fnger sequence checking with 

their own devices, and as such, there was no groundtruthing of Challenger data performed. The JHU APL 

facility API allowed for multiple images per fnger to be submitted for each study participant, but only the 

most recently submitted image was considered during analysis. 

4.3.1 Errors 

Several Challengers deviated from the image specifcations outlined in Section 4.1 for a large quantity of submitted images. NIST determined that the images from these Challengers would be unusable by the Matcher

if they remained as submitted, and would be detrimental to the Challenger’s overall results. With permission granted from the N2N Judging Committee, NIST performed the following minimal modifcations to 

Challenger images. 

Some Challengers submitted several images that were sized under 128 pixels × 128 pixels at 196.85 PPCM 

(500 PPI). To correct this situation, NIST centered the image on a white background of at least 128 pixels ×

128 pixels at 196.85 PPCM (500 PPI), as demonstrated in Fig. 4. Other Challengers incorrectly recorded the 

resolution in their submitted images. After consulting with the Challenger to determine the correct resolution, NIST updated the recorded resolution. Neither operation was destructive to the image data. 

Fig. 4. An example of correcting a fngerprint image whose dimensions were smaller than that required by the N2N image specifcations, 

outlined in Section 4.1. A larger image with a white background was created, and the smaller image was centered inside. This operation 

was not destructive to the image data. 

One Challenger’s device recorded di ̇erent horizontal and vertical capture resolutions in their submitted 

images. Although not inherently incorrect, varying horizontal and vertical capture resolutions could cause 

issues with some COTS fngerprint identifcation algorithms. With permission from the N2N Judging 

Committee and the Challenger, NIST resampled these images to the smaller resolution using the Lanczos-2 

interpolation kernel, as provided by MATLAB [2]. 

Finally, it was observed that one Challenger’s images were mirrored along the vertical axis, otherwise known 

as being fopped. A COTS fngerprint identifcation algorithm would fail to fnd a mate for such fngerprint 

images. All images from this Challenger were fopped to allow potential for successful identifcation, as 

shown in Fig. 5. 

Fig. 5. An example of correcting a fngerprint image by fopping, or reversing the columns of the image to mirror it over the vertical 

axis. This operation was not destructive to image data. 

All changes to Challenger images made by NIST were permitted by unanimous aÿrmative decisions by the 

N2N Judging Committee and the Challengers in question. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
10 N2N Challenge Prize Analysis

4.4 Omitted Data 

For fairness, study participants were omitted: 

• whose Baseline Data was not fully acquired, 

• who were removed from the test foor by N2N test sta ̇ for any reason, 

• who did not have a QR code scan at all required stations, 

• whose baseline images resulted in low mated comparison scores and could not be verifed as accurate 

after visual inspection, 

• whose baseline images resulted in high comparison scores for other N2N participants for all 10 fngers 

when searching (i.e., N2N backend server labeling error). 

4.5 Public Data 

IARPA is pleased to be able to provide much of the Challenger N2N data to biometric researchers. Refer to 

Appendix C for details. 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 11 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

5. Data — Latent 

Traces of an individual’s fngerprints have the potential to be left behind on nearly every surface they 

touch, primarily due to glands present in human skin. At a crime scene, these impressions are collected 

by an investigator for later analysis and automated identifcation. To that end, it was important that the 

latent fngerprints collected during the Nail to Nail Fingerprint Challenge mimicked the type of data that is 

typically seen in criminal investigations. 

A set of thirty activities were created to cause study participants to leave latent fngerprints on a variety 

of objects in a natural manner. The activities, described in Section 5.1, were chosen such that di ̇erent 

development techniques would be required, and were designed in consultation with the FBI to resemble 

the most common types of substrates latent prints are found on at crime scenes. 

When collecting data, there were little to no instructions provided to study participants related to fngerprint 

deposition on the substrates. For example, nothing was said about the amount of pressure that should be 

applied, the location of touches, or what fngers should be used. Instead, the activities and instructions 

created were common enough daily occurrences that the study participants were left to their own devices 

to complete the task they were presented with. There was no wrong way to perform a task. As a result, the 

data collected from performing these activities were as close to real-world law enforcement casework latent 

prints as possible, while allowing for a wide range of individual randomness between study participants 

performing the same activity. 

5.1 Activity Descriptions 

study participants each performed approximately 10 activities, using a mixture of porous, semiporous, 

nonporous, and adhesive objects. Each object and activity is described below. An example image resulting 

from each activity can be seen in Figs. 6 and 7, and their corresponding activity code, 1A through 7D, can 

be referred to in the footer of each page. Once study participants completed their required tasks, they 

placed the objects used into an evidence bag labeled with their study identifcation number. For activities 

that required writing a name or address, fake information was provided to prevent study participant 

re-identifcation. 

Peering Into Window (1A, nonporous) 

The palmar surface of the hands are placed on the sides of the head, about an inch in front of each 

ear. The outside edge of the little fnger extends forward beyond the nose. The hands and head are 

then brought toward a pane of glass until touching, simulating peering into a window at night while 

shadowing glare and refections. 

Fist Banging on Glass (1B, nonporous) 

Make a fst and strike the little fnger side on a pane of glass two or three times. This simulates 

knocking or angered banging on a door. 

Fingertip Window Slide (1C, nonporous) 

Fingertips are placed on a fat piece of glass. The hand is slid upwards with pressure, simulating 

opening a window sash without a handle. Examiners prompted the study participants randomly on 

the upward sliding angle to capture various tips and sides. 

Get-away Palm on Glass (1D, nonporous) 

Slap hand with fngers extended onto a piece of glass, simulating pushing open a push-exit door when 

in a rush. The hand will naturally slide upwards. 

“OK” Sign on Glass (1E, nonporous) 

Index fnger is curled toward thumb, while middle, ring, and little fngers remain extended. The entire 

hand is placed on a pane of glass. This activity, along with 1H, was designed to target extreme tips 

and sides of the distal phalanx. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
12 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Counter Vault on Glass (1F, nonporous) 

All fngers are extended while pushing down on a pane of glass, simulating applying pressure to a 

table or desk in order to assist in standing up from a sitting position, or vaulting a counter during a 

robbery. 

Cylinder Grab (1G, nonporous) 

A cylindrical plastic tube is grasped, simulating gripping a weapon such as a baseball bat, knife, or 

pistol magazine. 

Impatient Tapping on Glass (1H, nonporous) 

With the heel of the hand resting on a pane of glass, tap your fngers impatiently, striking at various 

distances from the palm. This activity, along with 1E, was designed to target extreme tips and sides of 

the distal phalanx. 

Samsung Galaxy S5 (2A, nonporous) 

Try to wake a Samsung Galaxy S5 by pressing its buttons and swiping and tapping on the screen. The 

device’s battery was disconnected during the collection. 

Apple iPhone 5s (2B, nonporous) 

Try to wake an Apple iPhone 5s by pressing its buttons and swiping and tapping on the screen. The 

device’s battery was disconnected during the collection. 

Check (3, porous) 

Write a check using American National Standards Institute (ANSI) X9 compliant magnetic ink checks 

from VersaCheck. Turn over the check and endorse it. Separate the check portion from the register 

portion of the paper. 

Lined Paper (4A, porous) 

Write a note on National college ruled, 27.94 cm × 21.59 cm, ≈ 64 g/m2, letter-sized fller paper. Fold 

the note in half, tear on the fold, then fold in half again. 

Low-quality Copy Paper (4B, porous) 

Write a note on Staples 96 bright, 75 g/m2, letter-sized inkjet paper. Fold the note in half, tear on the 

fold, then fold in half again. 

High-quality Copy Paper (4C, porous) 

Write a note on Hewlett Packard 165 bright, 24 g/m2, letter-sized inkjet paper. Fold the note in half, tear 

on the fold, then fold in half again. 

Yellow Lined Paper (4D, porous) 

Write a note on a sheet of paper from a Staples 75 g/m2 gold series letter-sized writing pad. Fold the 

note in half, tear on the fold, then fold in half again. 

Low-quality White Envelope (4E, porous) 

Address a Staples 90 g/m2, 10.5 cm × 24.1 cm privacy-tint envelope. Fold the envelope in half. 

Greeting Card and Envelope (4F, porous) 

Write a note inside a high-quality 12.7 cm ×17.78 cm greeting card from Markings by C.R. Gibson. Place 

the card into the provided greeting card envelope and address it. Fold the envelope in half. 

Manila Envelope (4G, porous) 

Address a Staples kraft, 105 g/m2, 8.6 cm × 15.2 cm gummed envelope. Fold the envelope in half. 

Photo Paper (5A, semiporous — processed as porous) 

Examine a piece of Kodak 10.2 cm ×15.2 cm, 240 g/m2, glossy premium letter-sized photo paper. Write 

a note on the back of it, then fold in half. 

Glossy Magazine (5B, semiporous — processed as porous) 

Hold a piece of letter-sized glossy magazine paper from a ULINE product catalog and identify a 

product of interest. Physically point out the item to the examiner, then fold in half. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 13 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

U.S. Currency (5C, semiporous — processed as porous) 

Examine an uncirculated $1 United States currency note to see if it is real or fake. All currency notes 

were real. 

Stamp (6A, nonporous) 

Aÿx a self-adhesive United States Postal Service stamp to a piece of clear acetate. Only the adhesive 

side of the stamp was developed. 

Address Label (6B, nonporous) 

Aÿx a self-adhesive Avery 2.54 cm×6.68 cm address label to a piece of clear acetate. Only the adhesive 

side of the label was developed. 

Clear Packing Tape (6C, nonporous) 

Unroll a 15 cm strip of Scotch 48 mm wide heavy duty shipping tape, and attach it to a piece of clear 

acetate. Only the adhesive side of the tape was developed. The examiner cut the end of the tape for 

the study participant with scissors. 

Black Electrical Tape (6D, nonporous) 

Unroll a 15 cm strip of Commercial Electric 19 mm wide black vinyl electrical tape, and attach it to a 

piece of clear acetate. Only the adhesive side of tape was developed. The examiner cut the end of the 

tape for the study participant with scissors. 

Duct Tape (6E, nonporous) 

Unroll a 15 cm strip of 3M 48 mm wide red duct tape, and attach it to a piece of clear acetate. Only the 

adhesive side of tape was developed. The examiner cut the end of the tape for the study participant 

with scissors. 

Circuit Board (7A, nonporous) 

Ask the study participant to read the serial number from an uncirculated Cofufu 3 cm × 7 cm doublesided universal printed circuit board. No circuit boards featured any serial numbers. 

CD/DVD (7B, nonporous) 

Pick up an uncirculated Memorex CD-R and hand it to the gloved examiner. The examiner holds 

onto the CD-R with moderate tension while the study participant pulls it away. This was to simulate 

loading and unloading a CD from a car stereo. 

Clear Plastic Bag (7C, nonporous) 

Smooth out an uncirculated Ziploc 16.5 cm × 14.9 cm plastic sandwich bag, then turn it inside out and 

back again. 

Black Plastic Bag (7D, nonporous) 

Smooth out an uncirculated ULINE 10.16 cm × 15.24 cm black bag, then turn it inside out and back 

again. 

5.2 Development 

The items used in the activities described in Section 5.1 were chosen in order to force a variety of latent 

friction ridge impressions and development techniques. Development or processing refers to the procedures 

under which a latent friction ridge on a surface is exposed. These techniques typically exploit properties of 

the various known oils, amino acids, lipids, and other compounds found in skin secretions. 

There are numerous techniques in which a friction ridge can be developed that are documented in the 

National Institute of Justice (Nœ)’s The Fingerprint Sourcebook [3] and FBI’s Processing Guide for Developing 

Latent Prints [4]. Although many processing techniques exist, four of the most popular and e ̇ective 

techniques were used in the Nail to Nail Fingerprint Challenge. 

The technique used to process an object is chosen based on the substrate that makes up the object. In terms 

of latent development, there are three primary categories of materials: porous, semiporous, and nonporous. 

Simply put, porous materials absorb skin secretions and nonporous materials do not. A semiporous material 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
14 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

1A 1B 1C 1D

1E 1F 1G 1H

2A 2B

Fig. 6. Examples of developing latent fngerprints for each latent activity using black powder and tape. The activities are described in 

Section 5.1. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 15 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

3 4A 4B 4C

4D 4E 4F 4G

5A 5B 5C 6A

Item was destroyed 

during processing. 

6B 6C 6D 6E

7A 7B 7C 7D

Fig. 7. Examples of developing latent fngerprints for each latent activity as a result of chemical reactions. The activities are described 

in Section 5.1. All items collected for activity 6B were accidentally destroyed during processing. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
16 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

may or may not absorb skin secretions, depending on the type and amount of secretion and the fdelity of 

the material. As such, development techniques for both porous and nonporous materials may be used on a 

semiporous object. An easy way to determine a material’s porosity is to place a drop of water onto it. The 

water droplet will act similarly to skin secretions. Additional subcategories of substrates, such as adhesives 

and thermal paper, require di ̇erent development methods and workfows. 

Black Powder 

The use of black powder in developing latent prints is one of the most common development techniques. The investigator coats a specialized brush made of strands of fberglass or another soft material 

with a fne colored powder. The brush is then twirled or painted over a nonporous surface. The particles 

in the powder attach to the skin secretions deposited when study participants touched the surface. 

After the print becomes visible, the investigator spreads a clear tape over the powdered surface and 

lifts the tape o ̇, placing it on a white evidence card. The powder that sticks to the secreted oils also 

sticks to the clear tape in the same shape. The evidence card is then scanned into a computer using a 

fatbed scanner. 

Activities 1A through 2B were developed with black powder. 

1,2-Indanedione 

One common skin secretion is sweat, which is a combination of many amino acids. On a nonporous 

surface, sweat dries quickly. When touching a porous material however, those small sweat secretions 

are absorbed into the substrate. 1,2-indanedione is a chemical reagent that reacts with the amino acids 

found in skin secretions. The reaction fuoresces when excited with green light. 

To develop, the object is immersed or sprayed with a solution of 1,2-indanedione. The item is allowed 

to dry under a vent hood, followed by direct heat to develop the latent prints. The fuorescence on 

the object can be viewed by illuminating the object with a green laser (532 nm) and photographing it 

through a curved orange flter. 

Activities 3 through 5C were developed with 1,2-indanedione. 

Adhesive-side Powder 

Although secretions and dead skin cells stick to the adhesive side of nonporous tape, so would all 

the fne particles in a powder typically used to develop a nonporous substrate. As an alternative, a 

mixture of water, a wetting agent, and a specialized adhesive-side powder can be combined to create a 

thin paste. This mixture is applied to the adhesive side of tape, allowed to sit on the surface to develop 

the latent prints, and then rinsed o ̇. The adhesive-side powder will remain adhered to the latent skin 

secretions exposing the print, just like traditional black powder would adhere to skin secretions on a 

non-adhesive nonporous object. 

Activities 6A through 6E were developed with adhesive-side powder. The color of the powder used 

was white or black — whichever would contrast with the substrate. After digitizing, the image was 

converted to ensure ridges were black. 

study participants stuck adhesive items from activities 6A through 6E onto a sheet of clear acetate. To 

remove the object for development, the acetate sheet was frozen, making the adhesive easier to free 

without destroying latent prints. In the case of 6B, this process accidentally destroyed the object. 

Cyanoacrylate 

Cyanoacrylate, more commonly known as “superglue,” can be used to develop prints from nonporous 

surfaces. To do so, an object is placed into a sealed chamber. Cyanoacrylate inside the chamber is 

heated to form a gaseous fume. After some time, the cyanoacrylate reacts with skin secretions to 

create a visible 3D polymer of print ridges on the surface of the object. The polymer can then simply 

be photographed. 

This development technique is convenient for developing latent prints from multiple types of surfaces. 

It avoids a potential pitfall of lifting black powder with tape from a surface that is textured or has 

an odd shape. In these cases, the resulting latent print in black powder on the tape is likely to be 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 17 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

incomplete or distorted, lacking suÿcient information for analysis. With cyanoacrylate, the developed 

polymer can be photographed without risk of destroying the print. 

Activities 7A through 7D were developed using cyanoacrylate fuming. 

5.3 Digitization 

Once latent friction ridges have been developed, they must be digitized. This allows expert examiners to 

analyze the print as well as submit the print to an automated latent identifcation algorithm. The digitization 

technique used depends on the latent development technique. 

Flatbed Scanner 

Latent prints developed with black powder were lifted o ̇ objects with clear tape and adhered to a 

white evidence card. These latent prints were scanned using a fatbed scanner confgured at various 

bit depths and resolutions. NIST software was deployed to automate the scan confguration changes 

without changing the region being scanned [5]. The scanners used in the Nail to Nail Fingerprint 

Challenge consisted of Epson models Perfection V700, Perfection V800, and Perfection V850. An 

International Organization for Standardization (ISO) 16067-1 refective scanner test target and an 

IT8.7/2 color refection test target were scanned prior to the evidence cards to confrm the scanners 

were digitizing correctly. 

Digital Camera 

For latent prints developed as a result of a chemical reaction, the reaction can simply be photographed. 

A ruler was placed on the same plane as the object being photographed in order to determine the 

capture resolution. A full-frame digital single-lens refex camera from Nikon (model D800) was 

used to photograph Nail to Nail Fingerprint Challenge latent prints developed with 1,2-indanedione, 

adhesive-side powder, and cyanoacrylate. 

Full Spectral Imaging System 

Not all chemical reactions can be easily photographed with a camera in ambient light. Additionally, 

photographs of reactions that take place on a noisy or refective background do not render well. A 

Full Spectral Imaging System (FSIS) can be used to produce better digitization for these objects. An 

FSIS can capture images in multiple ultraviolet, infrared, and visible light spectra. What an FSIS gains 

in detail over a digital camera, it can lack in resolution and dynamic range. 

A Full Spectral Imaging System was used to digitize activities 7A and 7B. An example of the di ̇erence 

of using a digital camera and an FSIS is quite visible in Fig. 8. In the digital camera image, there’s 

nearly no noticeable ridge structure. Under the various light spectrums produced by the FSIS, ridge 

structure begins to appear. 

Fig. 8. A visual comparison of digital camera and FSIS latent digitization methods. On the left, a portion of an object from activity 7A

is shown photographed using a Nikon D800 after cyanoacrylate fuming. Only very faint friction ridge detail is visible. On the right, 

the same object is imaged using a FSIS with 254 nm ultraviolet light and flter to create an image. The resulting image produced by the 

FSIS has much more pronounced ridge detail. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
18 N2N Challenge Prize Analysis

5.4 Image Format 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Latents developed using the black powder and tape technique were scanned at several resolutions and 

color depths, encoded as lossless Tagged Image File Format (TIFF) fles. NIST chose to use the 472.4 PPCM 

(1 200 PPI) 8 bit grayscale TIFF fles for searching with the Matcher. 472.4 PPCM (1 200 PPI) was the frst 

multiple of the scanner’s native scanning resolution above 393.7 PPCM (1 000 PPI). 

All other latents were photographed with a digital camera or an FSIS at varying resolutions calibrated to at 

least 393.7 PPCM (1 000 PPI). Objects were photographed in color and saved to a raw image fle using 14 bits 

per channel. After processing and enhancement in color (adjustments to contrast, hue, etc.), grayscale TIFFs 

were exported. NIST extracted regions of interest (Section 5.5) from the grayscale image and saved them as 

PNG fles for searching with the Matcher. 

5.5 Regions of Interest 

Regions encompassing individual latent prints in each image were marked by hand by Certifed Latent 

Print Examiners (CLPEs) at SFE. Examiners used the processed grayscale versions of images when defning 

these regions of interest. Using a graphics program, a polygonal path was defned around each latent 

print. Examiners had access to various image enhancement tools in the graphics program to help expose 

latent prints visually in the image. Each region of interest was marked as being from the distal phalanx, 

intermediate or proximal phalanges, palm, or other/unknown. 

The coordinates of the polygon were provided to NIST software to losslessly extract the region from the the 

image. The Matcher is color-agnostic, and so images were extracted from a grayscale version of the image 

at the actual capture resolution for searching. 

The Matcher operated in image-only mode. No quality values, minutia markings, background masks, or 

any examiner markup of any kind were provided to the Matcher. 

5.6 Groundtruth 

Ensuring that objects and evidence cards were associated with the correct study participants and activities 

was extremely important. Prior to the fngerprint data collection, CLPEs prepared latent kits for each study 

participant. Each latent kit consisted of a plastic bag full of randomly distributed objects from activities 3

through 5C and 7A through 7D, along with a sheet of adhesive labels containing a unique identifer. One of 

the identifer labels was adhered to the outside of the bag. 

At check-in, each study participant was provided with a set of adhesive labels depicting the QR code printed 

on their wristband. When a study participant arrived at the latent collection station, the SFE latent print 

technician adhered one of the study participant’s QR code labels to the front of a new latent kit. After the 

fngerprint data collection, N2N test sta ̇ created a mapping between QR code identifers and latent kit 

identifers. 

Activities 1A through 1H were developed from a 61 cm × 114 cm × 1.2 cm sheet of clear tempered glass. The 

glass sat on a table overtop a map of predefned regions for each activity, as seen in Fig. 9. The activity 

regions were subdivided into left and right regions, corresponding to the hand position used to create 

the impression. When the study participant left the station, SFE technicians would perform black powder 

development over each region of the glass that was touched. After placing the developed print onto an 

evidence card, the SFE technician adhered an activity, hand position, and latent kit identifer label to each 

card. The SFE technician also drew an arrow on the evidence card to indicate the orientation of the prints 

relative to where the study participant stood. All evidence cards were placed into an envelope with the study 

participant’s QR code label and latent kit identifer label on the front. A second SFE technician reviewed 

the contents of each evidence card and envelope before beginning digitization. An identical process was 

used for activities 2A and 2B, although hand labeling was not possible, as study participants held the objects 

in both hands. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 19 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Fig. 9. On top, a picture of the glass confguration used during the latent print collection. When performing activities 1A through 1H, 

study participants were instructed to keep their touches within the corresponding region shown underneath the glass. CLPEs would 

perform black powder development over the surface and adhere an activity and hand label to evidence cards produced from each 

section. study participants performed at most six of the eight glass activities. Below, a picture of an SFE technician preparing to lift 

developed black powder latent prints from the glass with clear tape. 

Digitizing evidence cards from activities 1A through 2B was performed one latent kit at a time, to reduce 

the risk of an erroneous latent kit identifer being entered. This was not feasible for activities 3 through 7D, 

due to the di ̇erent ways the objects in each latent kit needed to be processed. Instead, these objects were 

developed and digitized by activity. Prior to development, a CLPE wearing gloves emptied each labeled 

evidence bag into a clean bin. The CLPE would take the remaining latent kit identifer labels and adhere 

them to each object from the bag. The CLPE also wrote the latent kit identifer directly on the object, to 

enable traceability if a label came loose during development. While there was a strong possibility that the 

identifer label would obscure latent prints for many of the objects, the groundtruth beneft provided by the 

label was more important. When photographing the objects, the CLPE placed a clean activity and latent kit 

identifer label in the frame with the object. 

Identifers in flenames were confrmed manually for all activities by CLPEs as they inspected every image 

when defning regions of interest. Hand positions recorded in flenames and labels were additionally 

confrmed for activities 1A through 1H by examining the morphology of the hand. 

No attempt was made to associate fnger positions with any of the captured latents. Additionally, no hand 

information was recorded for any activity other than activities 1A through 1H. 

5.7 Public Data 

IARPA is pleased to be able to provide much of the developed latent print data to biometric researchers. 

Refer to Appendix C for details. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
20 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

6. Challengers 

Challengers in the Nail to Nail Fingerprint Challenge brought a variety of COTS and prototype devices 

for use. Descriptions of the devices, derived from information provided by the Challengers, are presented 

in Sections [6–6]. An example single fnger capture from each Challenger is shown in Fig. 10. The same 

examples are shown in Fig. 11 with minutia extracted from the NIST-provided Fingerprint Feature Extractor 

(the Extractor) superimposed. 

Operator-Assisted Baseline Data (Ω) 

The FBI training sta ̇ operated the traditional Baseline Data capture stations using Crossmatch L Scan 

1000 live scan capture devices. These devices captured baseline N2N, 4-4-2 slap, and palm data at 

393.7 PPCM (1 000 PPI). Each device platen was equipped with a Crossmatch silicone membrane. 

More information about the collection of Baseline Data can be found in Section 4.2. 

IDEMIA (A) 

IDEMIA used its MorphoWave Desktop COTS device for the Nail to Nail Fingerprint Challenge. It is 

a touch-free device that captured images of the fngers as a study participant passed their hand horizontally over the scanning area. Its uses three-dimensional modeling and reconstruction technology 

to create a two-dimensional fngerprint image from a 4-4-1-1 slap sequence. The device had a hard 

metal and plastic body, and a glass scanning surface. 

W https://idemia.com 

Advanced Optical Systems (B) 

The Automated Non-Contact Distance Identity (ANDI) N2N product from Advanced Optical Systems 

is a kiosk device that used passive optical sensors to collect fngerprints from study participants without 

them touching the device. Multiple optical sensor heads were housed in a vertical confguration inside 

the kiosk. Upon prompting from a display, the study participant made vertical passes, top to bottom, 

through the well of the kiosk with each hand, and then again for each thumb (4-4-1-1). Images 

produced from the multiple sensors were stitched together to produce an N2N fngerprint image. 

W https://aos-inc.com 

Green Bit (C) 

Green Bit used its COTS DactyScan40i device for the N2N unattended rolled fngerprint capture. It 

is an FBI Appendix F certifed dual fat fnger/single rolled fnger device with a scanning window of 

4.06 cm × 4.06 cm producing a 196.85 PPCM (500 PPI) image. Green Bit used algorithmic fngerprint 

quality assessments to accept only images that satisfed specifc quality checks. 

W http://greenbit.com 

Cornell University (D) 

Cornell’s prototype single fnger scanner used sonic and optical imaging for fngerprint capture. The 

study participant inserted one fnger downward into a foam fnger insert that sat at the top level of 

the device. Supported by a structure of acrylic and wood plates, metal screws, bolts, and shafts, the 

camera cylindrically swept around the centered fnger insert to capture an image. The device supports 

liveness detection and touts removable fnger inserts to support various fnger sizes. 

W http://sonicmems.ece.cornell.edu 

Jenetric (E) 

Marketed as the LIVETOUCH QUATTRO, Jenetric’s FBI Appendix F certifed COTS device collected 

fngerprints with a thin-flm transistor (TFT) sensor. The sensor itself is transparent, and has a 

graphical display integrated underneath. The user interface on the integrated display prompted the 

study participant to collect fngerprints without human intervention. 

W https://jenetric.com 

Touchless Biometric Systems (F) 

Touchless Biometric Systems used its S120 enrollment device at the Nail to Nail Fingerprint Challenge. 

The device captures one fnger at a time. The device sits a few inches high on a table and features a 

single circular cavity prominently on the front of the device. The study participant’s fnger is inserted 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

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N2N Challenge Prize Analysis 21 

into no-touch cavity and an image is captured. 

W https://tbs-biometrics.com 

Crossmatch (G) 

The Crossmatch Flexible Sensor is a prototype system that captures a single fnger at a time and 

outputs an 8 bit grayscale 196.85 PPCM (500 PPI) rolled image. It consists of a fexible sensor and 

illumination electronics. A computer provides prompts for the study participant to place one fnger 

onto a concave fexible sensor scanner flm, nail side up. Mechanics supporting the fexible sensor 

scanner flm receive a light downward pressure produced by the user, causing the sensor to wrap 

around the fnger. Near-infrared 850 nm light-emitting diodes (LEDs) and a di ̇user positioned 

above the fexible sensor project light towards the fnger, which scatters out of the skin in roughly a 

Lambertian distribution. Pixels in contact with friction ridges contain more light than those sitting 

beneath valleys of the fngerprint. 

W https://crossmatch.com 

Clarkson University (H) 

Clarkson’s device is a custom-built box of cameras designed to capture fngerprints in a traditional 

4-4-2 fnger scenario or two fngers at a time. The collection box is made of plastic with 3D printed 

components made out of photo-reactive resin. Inside are a set of 15 stationary cameras controlled 

through a computer that collects and processes the images. In combination, these cameras are capable 

of capturing 3 280 pixel × 2 464 pixel stills. Neutral white LED strip lighting circled the inside of the 

box, providing illumination around inserted fngers. 

W https://clarkson.edu/biomedical-signal-analysis-lab 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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22 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω A B

C D E

Permission not granted for 

reproduction. 

F G H

Fig. 10. Example captures from each Challenger. Each capture shows the same left middle fnger from the same study participant. 

Extracted minutia for these examples are shown in Fig. 11. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

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Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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N2N Challenge Prize Analysis 23 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω A B

C D E

Permission not granted for 

reproduction. 

F G H

Fig. 11. Example captures from each Challenger, with features discovered by the Extractor superimposed. Each capture shows the 

same left middle fnger from the same study participant. Green circles represent ridge bifurcations and blue circles represent ridge 

endings. The direction of the colored lines show the angle of the minutia. The red cross represents the computed minutia center of 

mass. Minutia are only extracted for approximately half of H, which may, in part, explain their poor identifcation performance. These 

images are shown without minutia superimposed in Fig. 10. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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24 N2N Challenge Prize Analysis

7. Metrics 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Metrics for determining prize winners were outlined in the Nail to Nail Fingerprint Challenge rules [6]. 

Each metric included a series of tiebreaker metrics that would be consulted if a tie on the primary metric 

was encountered. 

7.1 Accuracy 

A biometric identifcation system is a computer system that searches a biometric sample from an unknown 

subject against a set of samples from known subjects, to determine the identity of the source of the unknown 

sample. The set of samples from known subjects is referred to as an enrollment set. In an open-set identifcation 

system, not all searches have a corresponding known sample in the enrollment set. 

Recognition error in an open-set biometric identifcation system can be measured by assessing the results 

of two distinct search scenarios. One type, a mated search, is the search of a biometric sample from a subject 

enrolled in the system. The second, a non-mated search, is the search of a biometric sample of a subject not 

enrolled in the system. Each search in a biometric identifcation system produces a candidate list, which is a 

list of subjects in decreasing order of perceived similarity from the searched biometric sample. Each subject 

in the candidate list has an associated comparison score or a numerical value used to quantify the similarity 

when compared with the searched biometric sample. Collectively, these scores are known as similarity 

scores, where the higher the similarity score, the more similar the subjects. Each entry in a candidate list is 

referred to by rank. The subject at rank 1 — the subject in the candidate list with the highest similarity score 

— is the subject that the system determined to be the most similar to the searched biometric sample. When 

examining candidate lists, it is critical to consider similarity scores and not just ranks. In a mated search, the 

subject at rank 1 will ideally have a comparably-large similarity score. In a non-mated search, the subject 

at rank 1 will ideally have a comparably-small similarity score, indicating that the subject in the searched 

sample is not enrolled. 

Because there is not always a mate in the enrollment set for every search, open-set biometric identifcation 

systems, including the system used to perform all comparisons for the Nail to Nail Fingerprint Challenge, 

can produce two types of recognition error when searching. Type I error, or false positive, occurs during 

a non-mated search, when an enrolled subject is returned with a high similarity score, even though a 

subject not enrolled in the system was searched. The second type of error, Type II, or miss, occurs during a 

mated search, when the system returns an incorrect subject while searching with a sample of an enrolled 

subject. 

The accuracy metrics described herein borrow heavily from those outlined in Fingerprint Vendor Technology 

Evaluation (FpVTE), NIST’s one-to-many fngerprint identifcation technology evaluation [7]. 

7.1.1 Type I 

The rate at which a Type I error occurs is quantifed as the False Positive Identifcation Rate (FPIR). FPIR 

is the fraction of the non-mated searches where one or more enrolled identities are returned at or above 

threshold (T). FPIR is a function of the size of the enrollment set (N), the length of candidate lists (L), and 

the score threshold (T). In the general case, this can be summarized as shown in Equation (1). 

Number of searches with any non-mates returned 

FPIR(N, T, L)  above threshold T on candidate list length L (1) 

Number of non-mated searches conducted 

Equation (1) is equivalent to Equation (2), where Q is the number of searches performed for which there 

exists no mate in the enrollment set and dq1 is the highest similarity score reported by the Matcher for the 

q-th search. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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N2N Challenge Prize Analysis 25 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

ÍQ

q1 H(dq1 − T)

FPIR(T)  (2) Q

The function H(x) used in Equation (2) is the Heaviside step function, shown in Equation (3). 

(

0, if x < 0 H(x)  (3) 1, if x ≥ 0 

7.1.2 Type II 

The rate at which a Type II error occurs is quantifed as False Negative Identifcation Rate (FNIR). FNIR is 

the fraction of the mated searches where the enrolled mate is outside the top R rank or the similarity score 

is below threshold (T). FNIR is a function of the size of the enrollment set (N), the length of candidate lists 

(L), the score threshold (T), and the number of top candidates being considered (R). This is summarized in 

the general case as depicted in Equation (4). 

Number of searches with mates outside top R ranks or 

FNIR(N, R, T, L)  below threshold T on candidate list length L (4) 

Number of mated searches conducted 

Equation (4) is equivalent to Equation (5), where P is the number of searches performed for which there 

exists a mate in the enrollment set, dpr is the r-th lowest similarity score reported by the Matcher for the 

p-th search, and Ipr is 1 if the identity of the r-th candidate is the same as the identity of search p, or 0 

otherwise. 

1 ÕÕ

P L

FNIR(T)  1 − Ipr[1 − H(dpr − T)] (5) P

p1 r1 

All types of misses are accounted for identically when calculating FNIR. This includes situations in which 

the Matcher could not generate a template from the image data provided, or the fngerprints were labeled 

incorrectly by the Challenger. Challengers were responsible for providing the correct fnger positions. To 

be counted as correct identifcation, the Challenger needed to identify both the correct study participant 

identifer and the correct fnger position. Refer to Sections 7.1.4 and 8 for more details on accounting for 

failures and strategies for successful identifcation. 

CumulativeMatch Characteristic CumulativeMatch Characteristic (CMC) is the fraction of mated searches 

where the enrolled mate is at rank R or better, regardless of its comparison score. CMC is a special case of 

FNIR when the constraint on threshold is removed. It is generally considered an inadequate accuracy metric 

because it ignores comparison scores and does not report Type I errors. 

7.1.3 Detection Error Tradeo ̇ 

Detection Error Tradeo ̇ (DET) characteristic curves are the primary accuracy metric for o ̃ine testing 

of biometric recognition algorithms. Each point on a DET curve exhibits the false positive and negative 

identifcation rates associated with a certain threshold value. The DET curve spans the entire range of 

possible threshold values, which is normally the range of the similarity scores. 

DET curves are presented for each Challenger in Figs. 15 and 17. In a DET curve, Type I error rates are 

plotted on the x-axis and Type II error rates are plotted on the y-axis, giving uniform treatment to both types 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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26 N2N Challenge Prize Analysis

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of error. Both axes use a logarithmic scale, which spreads out the plot and better distinguishes di ̇erent 

well-performing systems. When calculating FPIR and FNIR, all ranks were considered (L  R  220). 

7.1.4 Handling Failures 

Failure to Acquire 

Challengers were required to capture an image of all ten fngers and study participants were required 

to have all ten fngers. As such, Challengers were inherently penalized during searches for any fngers 

that they failed to acquire (Section 7.3), since it would not be possible to correctly identify a latent 

fngerprint from a study participant’s fnger that was not acquired. This miss increased the FNIR. 

Failure to Extract/Encode 

The Extractor extracts fngerprint features and encodes the feature data into two types of templates: 

enrollment and search. In the event that an enrollment template could not be created, all mated 

latent searches would miss, increasing FNIR. If a search template could not be created during N2N 

identifcation, it was similarly counted as miss. Misses were accounted for by adding an instance of 

the lowest non-mated score to the set of mated scores. 

Failures to extract features in latent search template generation were removed from the test, since all 

search queries were identical for each Challenger. There was a noticeably high percentage of latent 

search failures. Latent search template failures to extract are a result of either the developed latent 

image or the Matcher, and not the Challenger. 

Failure to Search 

TheMatcher takes in search templates and produces a candidate list. Typically, if a biometric algorithm 

fails to perform a search and produce a candidate list, it is counted as a miss. However, a property 

of the Matcher is that a candidate list is always produced as long as a search template is produced. 

This means the only failures to search would be the result of the Extractor failing to create a search 

template. 

7.1.5 Tiebreaker 

FNIR was measured at an FPIR of 10−1. In the event of an FNIR tie, medians of the following NIST Fingerprint 

Image Quality (NFIQ) 2.0 feature values calculated over the Challenger’s captured images would be used 

to break the tie: 

1. NFIQ 2.0 

2. NFIQ 2.0: Frequency Domain Analysis (Standard Deviation) 

3. NFIQ 2.0: Ridge Valley Uniformity (Standard Deviation) 

7.2 Timing 

The time to acquire images of a study participant’s ten fngers was recorded by the JHU APL facility API, 

as explained in Section 3.8. The metric for determining the fastest overall acquisition time is defned in 

Equation (6). 

Timing Score  median × (1 + |skew|) (6) 

Median time is a more robust measure of the location of a distribution than the mean because means are 

greatly a ̇ected by outliers, such as study participants that are extremely diÿcult to fngerprint. 

Skew is a measure of the symmetry of a distribution from the left and right of the median, with 0 indicating 

a normal distribution. Skew was incorporated into the timing metric to observe the weight of the tails of 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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N2N Challenge Prize Analysis 27 

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the timing distribution. Extra processing time for a study participant would translate into longer wait times 

for enrollees as a queue forms when such a device is deployed in an operational setting. 

For the Nail to Nail Fingerprint Challenge, skew was defned as shown in Equation (7) [8], where X is the  ̄ set of all acquisition times, X is the mean of X, and n is the length of X. 

1 Í X ̄ ) n 3

i1 (Xi − n Skew   3/2 (7)

1 Ín  ̄ i1 (Xi − X)2 

n

7.2.1 Tiebreaker 

In the event of identical acquisition timing scores, the median acquisition time would be used to break the 

tie. 

7.3 Acquisition Rate 

All study participants were required to have all ten fngers accessible to be fngerprinted. If there was a 

study participant whose fngerprints could not be acquired by the traditional operator-assisted method, 

the study participant was excused from the fngerprint data collection. This means that it was possible to 

achieve a 100 % acquisition rate. 

A successful acquisition was achieved by acquiring images for all ten fngers from a subject, regardless of 

quality. Acquiring (1 to 9) fngers was considered a failure to acquire in terms of the acquisition rate metric. 

Regardless of the calculated acquisition rate, all images submitted to the N2N backend server were used 

when performing searches. 

Ω FBI Baseline A

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IDEMIA 

Jenetric 

B

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Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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28 N2N Challenge Prize Analysis

8. Methodology 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

A large-scale operational fngerprint feature extractor (Extractor) and identifcation algorithm (Matcher) 

were used to generate similarity scores. Each search with the Matcher resulted in a candidate list of at most 

220 candidates. 

8.1 Identifcation Algorithms 

The Matcher produced all similarity scores required to calculate FNIR and FPIR. The system consists of 

two distinct set of algorithms, one for N2N identifcation and another for latent print identifcation. Both 

algorithms are distributed systems, operating across many networked machines simultaneously. Identifcation was performed on a per-fnger (not per-subject) basis. Finger positions were provided to the algorithm 

when searching with N2N samples only. 

Although the exact mechanisms for identifcation in these two systems are proprietary and were not shared 

with Challenge sta ̇, both do export minutia from search samples. Refer to Fig. 10 and Fig. 12 for examples 

of the types of minutia extracted. When operating in latent mode, the Matcher searches the latent image 

by rotating in increments of 360◦, making the original orientation of the image inconsequential. In N2N 

mode, the Matcher does not rotate the search image, and so it was important that Challengers adhere to 

the Challenge image specifcations detailed in Section 4.1, requiring that the image be in an approximately 

upright orientation. 

Fig. 12. Example of minutia detected by the Extractor superimposed on a latent fngerprint image. Green circles represent ridge 

bifurcations and blue circles represent ridge endings. The direction of the colored line shows the angle of the minutia. The red cross 

represents the computed minutia center of mass. 

For fairness, in addition to using the Matcher, Challengers were given an option to submit their own 

template generation and identifcation algorithms that complied to the N2N Challenge API [9]. This option 

was designed for Challengers that felt images generated by their device would be better recognized by their 

own software. NIST would have computed all fgures of merit from both the Matcher and the Challenger’s 

software, and reported the better of the two. 

No Challengers opted to submit software. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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N2N Challenge Prize Analysis 29 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

8.2 Nail to Nail Identifcation 

Up to 3 310 N2N fngerprint images from 331 study participants were searched against a ten-print enrollment 

set from each of 2 999 999 non-mated subjects plus 331 study participants from Baseline Data in the N2N 

identifcation portion of the Nail to Nail Fingerprint Challenge. Each of the 2 999 999 non-mated subjects 

had between 1 and 10 rolled fngerprint images, for a total of 29 982 781 non-mated fngerprint images. 

Individual fngerprint images from each N2N study participant were enrolled as separate subjects in the 

enrollment set — one fnger per subject — for a total of 3 003 309 subjects identifers and 29 986 091 fngers 

in the enrollment set. 

8.2.1 Nail to Nail Non-mated Searches 

Operationally-collected law enforcement rolled fngerprint images from 2 999 999 subjects were encoded and 

inserted into the enrollment set of the Matcher. Each Challenger image was then encoded and searched 

against the enrollment set that contained no known mates. Each fnger was searched separately, for at most 

10 non-mated searches per study participant per Challenger, totaling 25 835 non-mated searches. 

8.2.2 Nail to Nail Mated Searches 

For each study participant, 10 individual fnger images from Ω1 — the frst of two sets of operator-assisted 

N2N Baseline Data (Section 3.4) — were encoded and inserted into the enrollment set of the Matcher. 

Operationally-collected law enforcement rolled fngerprint images from 2 999 999 non-mated subjects were 

added to the enrollment set. Each fnger image collected by each Challenger was searched separately, for 

at most 10 mated searches per study participant per Challenger. For Ω, images from Ω2 — the second of 

two sets of operator-assisted N2N Baseline Data (Section 3.4) – were used. A correct result was defned as 

returning the correct study participant identifer and correct fnger position. Any failures by Challengers 

to acquire study participant fngerprint images or failures to extract features from acquired images by the 

Matcher counted as a false negative by adding a small similarity score for the mated search that would 

have been performed (Section 7.1.4). In total, 25 835 mated searches were performed. 

8.3 Latent Identifcation 

62 721 latent fngerprint images were searched against ten-print enrollment sets of 2 999 999 non-mated 

subjects plus 331 study participants from each Challenger in the latent identifcation portion of the Nail 

to Nail Fingerprint Challenge. Each of the 2 999 999 non-mated subjects had between 1 and 10 rolled 

fngerprint images, for a total of 29 982 781 non-mated fngerprint images. Individual fngerprint images 

from each N2N study participant were enrolled as a separate subject in the enrollment set — one fnger per 

subject — for a total of 3 003 309 subject identifers and 29 986 091 fngers in the enrollment set. Each latent 

fngerprint contained traces of only one fngerprint. Only regions marked as distal phalanx by CLPEs were 

searched. 

8.3.1 Latent Non-mated Searches 

Operationally-collected law enforcement rolled fngerprint images from 2 999 999 subjects were encoded 

and inserted into the enrollment set of the Matcher. Each image extracted from a region of interest defned 

by CLPEs was then encoded and searched against the enrollment set that contained no known mates. A 

total of 62 721 non-mated latent searches were performed. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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30 N2N Challenge Prize Analysis

8.3.2 Latent Mated Searches 

For each Challenger, the images used to search the enrollment set in N2N identifcation were encoded and 

inserted to the enrollment set of the Matcher (Section 8.2). For Ω, images from Ω2 were used. Operationallycollected law enforcement rolled fngerprint images from 2 999 999 non-mated subjects were added to the 

enrollment set. 

Each image extracted from a region of interest defned by CLPEs was then encoded and searched against 

the enrollment set. A correct result was defned as returning any fnger position from the correct study 

participant identifer. In cases where the hand leaving the latent print was known (left or right), it was 

required that a fnger position from the known hand be returned with the correct study participant identifer 

to be considered a correct result. There were no latent fngerprints for which the correct fnger position was 

known ahead of time, and activities 1A through 1H had a known hand. Details regarding handling failures 

to search can be found in Section 7.1.4, and a ̇ected all Challengers equally. A total of 564 489 mated latent 

searches were performed. 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω FBI Baseline A

E

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B

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Touchless Biometric Systems 

C

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Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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N2N Challenge Prize Analysis 31 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

9. Results — Acquisitions 

Observed acquisition rates and times are detailed in Sections 9.2 and 9.3. Caveats listed in Section 9.1 help 

explain some particularly unusual values. 

9.1 Caveats 

On the frst day of the Challenge, B and D disclosed that they experienced failures using their software 

to submit acquired images to the N2N backend server. It was determined that the issue resided on 

the Challengers’ systems and not the N2N backend server. The N2N Judging Committee decided to 

allow Challengers to resubmit data that was missing from the N2N backend server but saved locally on 

Challenger hardware. In doing so, the Challenger would be voluntarily disqualifying themselves from any 

prizes related to acquisition timing (Section 9.3), due to the fact that timing is recorded by the JHU APL 

facility API. D opted to resubmit their missing data, but B did not. 

On the second day of the Challenge, G’s device, a prototype, was beginning to experience damage. The 

Challenger chose not to participate in the data collection on the third day, so that their device would still be 

presentable for a United States Government open house scheduled for that evening. On the fourth day, the 

Challenger continued to collect data until the device ultimately experienced irreparable damage. 

After interacting with several study participants, H observed that they were frequently unable to acquire a 

complete set of fngerprints from a study participant within the fve min the study participant visited their 

acquisition station. The Challenger continued to participate in the fngerprint data collection and opted to 

capture as many fngers from each study participant as possible, realizing that their acquisition rate would 

make them ineligible for many performance-based prizes. 

Challengers were permitted to process captured data after a study participant had left their capture station, 

so long as the data was submitted to the N2N backend server by the end of the Challenge. D performed a 

large amount of post-processing. Additionally, D resubmitted data missed on the frst day of the Challenge 

at various times throughout the week, creating large acquisition timing outliers. 

9.2 Acquisition Rate 

A successful acquisition in the Nail to Nail Fingerprint Challenge was defned as capturing all 10 fngers for 

a single study participant. All study participants were required to have all 10 fngers accessible, so acquiring 

anything less than 10 fngers was considered a failure to acquire. Refer to Section 7.3 for complete details 

on the metrics for determining acquisition rate. 

The observed acquisition rate outcomes are plotted in Fig. 13. To be eligible for most Nail to Nail Fingerprint 

Challenge prizes, Challengers needed to acquire fngerprint images from at least 90 % of study participants. 

This value was achieved for all Challengers, except for G and H. Ω was required to achieve a 100 % acquisition 

rate in order to establish baseline metrics. 

9.3 Acquisition Time 

A prize was awarded for having the best acquisition timing metric. The metric for acquisition time incorporated both the Challenger’s median acquisition time and the skew of the set of acquisition times. This 

was important in order to incorporate outliers into the prize. Refer to Section 7.2 for complete details on the 

acquisition time metric. 

The observed median acquisition time and timing metric are reported in Table 4. Boxplots and violin plots 

of acquisition times are shown in Fig. 14. 

Acquisition time statistics do not depend on the acquisition rate and are an overall refection of acquisitions 

performed during the Nail to Nail Fingerprint Challenge. That is, the times reported are respective of a 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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32 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

13.0%

33.5%

91.8% 94.0% 96.4% 97.9% 100.0% 100.0% 100.0%

0%

25%

50%

75%

100%

Ω A B C D E F G H

Challenger

Acquisition Rate

All Challengers

Acquisition Rate

Fig. 13. The rate of acquisition for all Challengers, in percentage of study participants acquired. The actual rate of acquisition is printed 

at the top of each Challenger bar. A successful acquisition was defned as acquiring all 10 fngers from a study participant. G and H

encountered unique issues that were documented in Section 9.1, and as such, did not meet the minimum 90 % acquisition rate to be 

eligible for most performance-based prizes. 

di ̇erent number of study participants (in the case of G) or a considerably lower acquisition rate (in the case 

of H). This was done to not penalize Challengers that struggled during the Challenge twice. 

Discussion 

• B’s decision not to resubmit data, as explained in Section 9.1, worked out in their favor. 

• Several Challengers acquired images faster than Ω. 

• The acquisition timing metric incorporating skew proved to be signifcant. The fastest Challenger, 

A, had many outliers, greatly skewing their timing distribution higher. Additionally, outliers caused 

Challengers with similar median times (B, C, and E) to achieve vastly di ̇erent scores. 

Prize Winner The Nail to Nail Fingerprint Challenge award listing in Table 1 indicates that the Speed Prize 

should be awarded to the Challenger with the smallest acquisition time score that also acquired ≥ 90 % of 

all data and scored within 80 % of Ω. This criteria is met by B. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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N2N Challenge Prize Analysis 33 

Challenger Median Score 

Ω

A

B

C

D 7267 7761.4 

E 6179 4328.8 

F 2105 3285.0 

G 3133 6492.4 

H 8984 82 880.8 

144 314.0 

159 2281.5 4159 1278.9 5160 5342.5 

Table 4. and acquisition timing score for all Challengers. The frst column is the Challenger’s letter identifer. The second column is 

the Median acquisition time, reported in s. The third column is the acquisition timing Score (Section 7.2), which incorporates the skew 

of all observed acquisition times. The Baseline Data is recorded in blue. The best non-baseline values in each column are reported in 

green and the worst are in red. Challenger values that meet or beat the baseline are shaded in blue. Superscript numbers preceding 

values represent the ranking of this value in each column. Several Challengers outperformed the Ω, a desired outcome of the Nail to 

Nail Fingerprint Challenge. 

●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ● ●● ●●●●●●●●●●●● ●●● 200

400

600

Ω A B C E F G

Acquisition Time (seconds)

All Challengers

Acquisition Time

●●●●●●●●●●●●●●●●●●●

2 000

4 000

6 000

H

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

0

20 000

40 000

60 000

80 000

D

Challenger

Fig. 14. Boxplots and violin plots of acquisition times for all Challengers. Acquisition time is defned as the number of s between 

frst encountering a study participant until the last fngerprint image of that study participant is received by the N2N backend server. 

Challengers are plotted amongst three di ̇erent y axis scales in order to display data and outliers comfortably. The upper and lower 

hinges of the boxplots indicate the frst and third quartiles, and the center lines indicates the medians. The dashed-outline violin plots 

show the density of the distribution for all values of the set, becoming wider in areas with higher density. A horizontal red dashed 

line is shown at 300 s, the maximum time in which a Challenger was permitted to interact with a study participant. Times signifcantly 

above 300 s indicate post-processing or an otherwise delayed submission of data, as noted in Section 9.1. Most Challengers were able 

to collect all 10 fngerprints around the same speed as or faster than the traditional operator-assisted N2N capture technique. 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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34 N2N Challenge Prize Analysis

10. Results — Nail to Nail Identifcation 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

The accuracy of the Matcher when searching N2N images from Challengers against an enrollment set of 

traditional operator-assisted N2N captures are shown in DET curves in Fig. 15 and Table 5. CMC curves 

showing hit rate are shown in Fig. 16 and Table 6. 

Discussion 

• Three distinct groupings of Challengers emerge. 

• No Challenger outperforms Ω, and the closest Challenger has an FNIR 11 % larger than Ω. 

• DET curves are relatively fat, showing that FNIR does not dramatically change by threshold. 

• CMC curves are also relatively fat, showing that for N2N identifcation for all Challengers, the 

Matcher typically fnds the correct mate at rank 1, or does not fnd the mate at all. 

• Although su ̇ering from a severe lack of study participants, G was not the worst performer. 

• None of H’s N2N images were successfully identifed. Availability of minutia, as seen in Fig. 11, may be 

part of the problem, although visual inspection shows that most fngerprint captures do not resemble 

the exemplar. 

Prize Winner The Nail to Nail Fingerprint Challenge award listing in Table 1 indicates that the Gallery 

Accuracy Prize should be awarded to the Challenger with the best N2N identifcation performance, so long 

as the Challenger acquires ≥ 90 % of all data. This criteria is met by C. 

The closest any Challenger comes to meeting Ω’s FNIR value is 11 %, achieved by C. A criterion for the Grand 

Prize is to achieve FNIR within 2 % of Ω, indicating that no Challenger could win the Grand Prize. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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5B

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Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 35 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Challenger FNIR @ FPIR  10−1 

Ω 0.088 ± 0.006 

A 30.214 ± 0.008 

B 50.279 ± 0.009 

C 10.098 ± 0.006 

D 70.824 ± 0.008 

E 20.198 ± 0.008 

F 40.235 ± 0.009 

G 60.724 ± 0.009 

H 81.000 ± 0.0002 

Table 5. Values for FNIR at a fxed FPIR of 10−1 for N2N identifcation. These values are extracted from those plotted on the vertical 

dashed line in Fig. 15. The Baseline Data is recorded in blue. The best non-baseline value is reported in green and the worst in red. 

Challenger values that meet or beat the baseline are shaded in blue . Superscript numbers preceding the FNIR value represent its 

ranking. The confdence intervals depicted are a 90 % Wilson confdence interval [10]. 

0.02

0.05

0.10

0.20

0.50

1.00

0.01 0.02 0.05 0.10 0.20 0.50

False Positive Identification Rate

False Negative Identification Rate

Challenger Ω A B C D E F G H

All Challengers, N2N Identification

Detection Error Tradeoff

Fig. 15. DET curves for each Challenger for N2N identifcation. This plot shows the accuracy of the Matcher when searching N2N 

images from each Challenger against the same enrollment set of traditional operator-assisted N2N captures. The vertical dashed line 

indicates the FPIR at which a prize was to be awarded for the lowest FNIR, and the values lying on this line are reported in Table 5. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

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36 N2N Challenge Prize Analysis

Challenger Hit Rate (Rank 1) 

Ω 91.2 % 

A 378.6 % 

B 572.1 % 

C 190.2 % 

D 717.6 % 

E 280.4 % 

F 476.6 % 

G 627.6 % 

H 80.0 % 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Table 6. Hit rate for rank 1 for the Matcher when searching N2N images from each Challenger against the same enrollment set of 

traditional operator-assisted N2N captures. The Baseline Data is recorded in blue. The best non-baseline value is reported in green 

and the worst in red. Challenger values that meet or beat the baseline are shaded in blue . Superscript numbers preceding the hit rate 

value represent its ranking. All ranks are depicted in Fig. 16, but the fatness of the curves indicate that the Matcher typically fnds 

the correct mate at rank 1 or not at all. 

● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ●

● ● ●

● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ●

25%

50%

75%

100%

1 5 10 15 20 25 30 35 40 45 50 55 60 65

Rank

Match Percentage

Challenger ● Ω ● A ● B ● C ● D ● E ● F ● G ● H

All Challengers, N2N Identification

Cumulative Match Characteristic

Fig. 16. CMC curves for each Challenger for N2N identifcation. This plot shows the rate at which the Matcher, when searching N2N 

images from each Challenger against the same enrollment set of traditional operator-assisted N2N captures, found the correct mate, 

regardless of similarity score. The values for rank 1 are reported in Table 6. If a curve stops before reaching the far right of the plot, it 

indicates that a mate was never found past the last corresponding rank. H is not visible on the plot because their hit rate never increased 

above the minimum match percentage shown for any rank. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

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N2N Challenge Prize Analysis 37 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

11. Results — Latent Identifcation 

The accuracy of the Matcher when searching study participant latent fngerprint images against an enrollment set seeded with N2N captures from each Challenger are shown in DET curves in Fig. 17, with FNIR 

values recorded at the Challenge’s fxed FPIR fgure of merit in Table 7. CMC curves showing hit rate are 

shown in Fig. 18 and Table 8. 

Some Challengers performed better on particular latent activities. Results separated by activity are available 

in Appendix A. 

Discussion 

• Overall automated latent identifcation performance is quite poor. Future analysis will study this 

latent dataset with varying smaller enrollment set sizes to see if it is a signifcant factor (Table 7). 

• Groupings present in N2N identifcation are less pronounced in latent identifcation (Fig. 17). 

• C outperforms Ω (Table 7). Given that C’s N2N identifcation results were comparable with Ω suggests 

that C might capture a larger fngerprint surface area than Ω. 

• Unlike N2N identifcation (Fig. 16), most Challengers do see an improvement in hit rate within the 

frst (5 to $10) ranks, although performance is still poor (Fig. 18). 

• Despite not correctly identifying any N2N images, the Matcher does achieve some hits during latent 

identifcation with H, perhaps indicating that certain areas of the fnger are imaged more accurately 

than others with H’s device (Fig. 18). 

• Activity and substrate appear to make a di ̇erence (Appendix A). Future studies will look into understanding why the di ̇erences exist. 

Prize Winner The Nail to Nail Fingerprint Challenge award listing in Table 1 indicates that the Latent 

Accuracy Prize should be awarded to the Challenger with the best latent identifcation accuracy that also 

collected ≥ 90 % of data. Using values from Table 7, the winner of the Latent Accuracy Prize is C. 

11.1 In Context 

Latent print identifcation results in this section deviate from expectations created by prior NIST studies. 

In Evaluation of Latent Fingerprint Technologies: Extended Feature Sets (ELFT-EFS) (Evaluation #2) [11], the best 

rank 1 identifcation rates for image-only identifcation are upwards of 60 %. These rates decrease when 

taking into account the latent print’s initial rotation angle (25 % to 50 % when unknown) and origin (20 % to 

40 % when casework). However, hit rates at a particular rank do not scale as enrollment set sizes increase, and 

the Nail to Nail Fingerprint Challenge makes use of an enrollment set 30 times larger than the enrollment 

set used in ELFT-EFS. Even still, FNIR values are more likely to be stable across di ̇erent enrollment set 

sizes, and ELFT-EFS indicates the best overall FNIR at FPIR  10−1 is ≈ 0.4. 

Latent prints in the N2N dataset are of varying quality and have unknown initial rotation angles. They 

are considered to be of law enforcement casework origin. Regions showing any amount of ridge structure 

were isolated by CLPEs and searched without any CLPE-assigned enhancements or feature demarcations. 

Visual inspection of the dataset indicates that there are numerous images of poor quality that would not be 

searched operationally without at least minimal feature extractions by CLPEs. Angle, feature, and quality 

quantifcation as well as the e ̇ect of the size of the enrollment set are topics for further examination. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

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4F

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5A

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6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
38 N2N Challenge Prize Analysis

Challenger FNIR @ FPIR  10−1 

Ω 0.921 ± 0.001 

A 20.927 ± 0.001 

B 40.934 ± 0.001 

C

D

E 20.927 ± 0.001 

F 50.950 ± 0.001 

G 60.981 ± 0.0006 

H 81.000 ± 0.00001 

10.915 ± 0.001 70.983 ± 0.0006 

Table 7. Values for FNIR at a fxed FPIR of 10−1 for latent identifcation with an enrollment set of 3 003 309 subjects. These values are 

extracted from those plotted on the vertical dashed line in Fig. 17. The Baseline Data is recorded in blue. The best non-baseline value 

is reported in green and the worst in red. Challenger values that meet or beat the baseline are shaded in blue . Superscript numbers 

preceding the FNIR value represent its ranking. The confdence intervals depicted are a 90 % Wilson confdence interval [10]. The 

identifcation performance depicted is quite poor. 

0.9

1.0

0.01 0.02 0.05 0.10 0.20 0.50

False Positive Identification Rate

False Negative Identification Rate

Challenger Ω A B C D E F G H

All Challengers, Latent Identification

Detection Error Tradeoff

Fig. 17. DET curves for each Challenger for latent identifcation. This plot shows the accuracy of the Matcher when searching study 

participant latent fngerprint images against an enrollment set seeded with N2N images from each Challenger. The vertical dashed 

line indicates the FPIR at which a prize was to be awarded for the lowest FNIR, and the values lying on this line are reported in Table 7. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

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4G

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Photo Paper 

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5B

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Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

N2N Challenge Prize Analysis 39 

Challenger Hit Rate (Rank 1) Hit Rate (Rank 5) Hit Rate (Rank 10) 

Ω 8.2 % 8.7 % 8.8 % 

A 27.6 % 28.2 % 28.3 % 

B 46.9 % 47.5 % 47.6 % 

C 18.8 % 19.3 % 19.4 % 

D 71.8 % 72.1 % 62.2 % 

E 37.6 % 38.1 % 38.2 % 

F 55.3 % 55.6 % 55.7 % 

G 62.0 % 62.1 % 72.1 % 

H 80.0 % 80.0 % 80.0 % 

Table 8. Hit rate for ranks 1, 5, and 10 for the Matcher when searching study participant latent fngerprint images against an enrollment 

set of Challenger N2N images. The best non-baseline value is reported in green and the worst in red. Challenger values that meet or 

beat the baseline are shaded in blue . All ranks are depicted in Fig. 18. A gradual improvement is noticed between rank 1 and rank 10 

for most Challengers, although the hit rates at highest rank are still poor. 

●

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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ●●●● ●●●●●●● ● ●● ●●● ●● ● ●●●●●● ●●●●●● ● ●●●●●●

●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●●● ●●●● ● ●●●● ●● ●●●●●● ●●

●●●●●●●●●●●●● ●●●●●● ●● ●●●● ●● ●● ●● ● ●●●●●● ● ● ● ●● ● ● ● ●● ●

●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●● ●●●● ● ●●● ●●● ●● ●●●●●● ●●● ●●● ●●● ● ● ● ●

●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●● ●●●●● ●●●●●● ● ● ●●● ● ● ●●● ●●●●●● ●● ●● ●● ●

●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ● ● ●● ●●●● ●● ● ●● ●● ● ● ●● ● ● ● ● ● ●● ●●

● ● ●● ● ● ● ● ● ●

●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ●●●● ● ● ● ● ●●●●●●●● ●● ●●● ● ●●● ●● ●●●● ● ● ● ●●

0.0%

2.5%

5.0%

7.5%

10.0%

1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Rank

Match Percentage

Challenger ● Ω ● A ● B ● C ● D ● E ● F ● G ● H

All Challengers, Latent Identification

Cumulative Match Characteristic

Fig. 18. CMC curves for each Challenger for latent identifcation. This plot shows the rate at which the Matcher, when searching 

study participant latent fngerprint images against an enrollment set seeded with N2N images from each Challenger, found the correct 

mate, regardless of similarity score. The values for ranks 1, 5, and 10 are reported in Table 8. If a curve stops before reaching the far 

right of the plot, it indicates that a mate was never found past the last corresponding rank. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
40 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 41 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Acknowledgments 

A data collection of the size and scale presented at the Nail to Nail Fingerprint Challenge requires the 

coordination and cooperation of dozens of individuals, without whom the Challenge would not have been 

possible. 

• Thank you to the Intelligence Advanced Research Projects Activity for sponsoring the Nail to Nail 

Fingerprint Challenge and supporting advancements in fngerprint capture and recognition. 

• Thank you to Rebecca Allegar, Nathaniel Short, and the many members of the Booz Allen Hamilton 

(BAH) team that helped IARPA create, organize, plan, and successfully execute the Nail to Nail 

Fingerprint Challenge. 

• Thank you to Ellen Fuller, Christopher Nardone, and the rest of the Johns Hopkins University Applied 

Physics Laboratory team for graciously hosting the Nail to Nail Fingerprint Challenge and providing 

the infrastructure by which N2N Challengers submitted imagery. 

• Thank you to the many Johns Hopkins University Applied Physics Laboratory, Booz Allen Hamilton, 

Intelligence Advanced Research Projects Activity, National Institute of Standards and Technology, and 

Schwarz Forensic Enterprises employees who spent countless hours digitizing and isolating latents. 

• Thank you to the Arizona Department of Public Safety, the Federal Bureau of Investigation, the Los 

Angeles County Sheri ̇’s Department, and the Texas Department of Public Safety for their donation 

of operational rolled images that were used to seed the Matcher’s enrollment set with impostor 

candidates. 

• Thank you to Crossmatch and IDEMIA for providing high-resolution palm scanners for the week of 

the Nail to Nail Fingerprint Challenge in order to help capture exemplar palm data for distribution 

with the Challenge latent print dataset. 

• Thank you to the Biometrics Research Group at Michigan State University for providing a prototype 

open source single fnger capture device for auxiliary data collection. 

• Thank you to sta ̇ of the Department of Homeland Security Maryland Test Facility for their assistance 

in prototyping the Challenge data collection. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
42 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

References 

[1] Simpson JA, Weiner ESC (1989) The Oxford English Dictionary. Vol. IX (Oxford University Press), 2nd 

Ed. 

[2] MathWorks MATLAB imresize, https://www.mathworks.com/help/images/ref/imresize.html. 

[3] International Association for Identifcation, et al (2011) The Fingerprint Sourcebook (National Institute of 

Justice), 1st Ed. 

[4] Trozzi TA, Schwartz RL, Hollars ML (2000) Processing Guide for Developing Latent Prints, https:// 

archives.fbi.gov/archives/about-us/lab/forensic-science-communications/fsc/jan2001/lpu.pdf. [Online; accessed 15 February 2018]. 

[5] Grantham J (2017) NISTscan, https://github.com/usnistgov/NISTscan. [Online; accessed 15 February 

2018]. 

[6] Intelligence Advanced Research Projects Activity (2017) IARPA Nail-to-Nail Challenge Rules & Regulations, https://www.iarpa.gov/challenges/n2n/N2NChallengeRules.pdf. [Online; accessed 15 February 

2018]. 

[7] Watson C, et al. (2014) Fingerprint Vendor Technology Evaluation. NIST Interagency Report 8034 https: 

//doi.org/10.6028/NIST.IR.8034 

[8] Shapiro SS, Wilk MB, Chen HJ (1968) A Comparative Study of Various Tests for Normality. Journal of 

the American Statistical Association 63(324):1343–1372. URL http://www.jstor.org/stable/2285889. 

[9] Fiumara G (2017) Nail-to-Nail Fingerprint Capture Challenge: Software Test Plan, https://github.com/ 

usnistgov/IARPA-N2N. [Online; accessed 15 February 2018]. 

[10] Wilson EB (1927) Probable Inference, the Law of Succession, and Statistical Inference. Journal of the 

American Statistical Association 22(158):209–212. URL http://www.jstor.org/stable/2276774. 

[11] Indovina M, Dvornychenko V, Hicklin RA, Kiebuzinski GI (2012) Evaluation of Latent Fingerprint 

Technologies: Extended Feature Sets [Evaluation #2]. NIST Interagency Report 7859 https://doi.org/ 

10.6028/NIST.IR.7859 

[12] Tabassi E (2016) Development of NFIQ 2.0, https://www.nist.gov/services-resources/software/ 

development-nfq-20. [Online; accessed 15 February 2018]. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 43 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

A. Results — Latent Identifcation — By Activity 

Overall performance for latent identifcation is quite poor (Section 11). Some Challengers perform comparably well on particular activities, while failing to identify any study participants in others. 

Activities that were more prone to leave behind the center area of the distal phalanx performed better, while 

those that encouraged leaving extreme tips and sides, a goal of a true N2N capture, did not. Regardless, 

even the best performing activities demonstrate poor accuracy. 

FNIRs for each activity can be seen in Table 9 and Fig. 19. CMCs for each activity can be seen in Table 10 

and Fig. 20. 

A breakdown of the number of searches performed in each activity is shown in Table 11. Only those images 

that were successfully searched by the Matcher are represented. Not all study participants left behind 

latent prints for a given activity, and so they are not represented in Table 11. Additionally, not all collected 

data was able to be developed and digitized prior to this results distribution. 

Activity Ω A B C D E F G H

1A 0.890 30.928 40.940 60.972 20.890 50.945 70.985 81.000 

1B 1.000 11.000 11.000 

10.860 11.000 11.000 11.000 11.000 11.000 11.000 

1C 0.905 20.919 40.932 10.907 70.987 30.920 50.949 60.983 81.000 

1D 0.710 30.744 40.763 70.932 20.722 50.792 60.924 81.000 

1E 0.829 30.877 40.885 70.973 20.838 50.917 60.971 81.000 

1F 0.682 30.729 40.745 70.939 20.720 50.797 60.913 81.000 

1G 0.978 50.990 20.984 

10.684 10.788 10.681 10.981 70.997 40.988 30.987 50.990 81.000 

1H 0.892 30.897 20.879 70.975 40.898 50.932 60.972 81.000 

2A 0.971 20.963 20.963 70.997 40.964 50.976 60.990 81.000 

2B 0.973 20.972 20.972 70.996 40.974 50.981 60.995 81.000 

3 0.984 

10.868 10.954 10.969 10.977 10.970 10.958 10.973 10.966

20.982 20.982 60.994 20.982 50.986 70.999 81.000 

4A 0.974 40.977 30.976 70.996 20.971 50.980 60.994 81.000 

4B 0.958 40.962 30.960 60.990 50.978 60.990 81.000 

4C 0.978 40.978 30.976 60.994 50.982 71.000 71.000 

4D 0.970 30.975 30.975 60.991 

10.958 10.973 20.971 50.981 70.996 81.000 

4E 0.970 30.972 40.975 20.971 70.993 50.984 60.992 81.000 

4F 0.983 20.982 40.989 70.998 

10.970 30.985 50.991 60.994 81.000 

4G 0.966 20.968 

10.980 10.965 10.952 10.978 10.978 10.967 10.893

30.969 60.992 40.972 50.977 70.993 81.000 

5A 0.962 40.962 20.957 70.993 30.960 50.968 60.986 81.000 

5B 0.981 40.981 20.979 60.997 30.980 50.989 71.000 71.000 

5C 0.992 20.981 50.989 60.997 30.986 30.986 71.000 71.000 

6A 0.975 20.973 40.976 60.994 30.975 50.990 60.994 81.000 

6C 0.897 20.905 40.918 70.978 30.906 50.933 60.970 81.000 

6D 0.894 20.907 40.911 10.895 70.977 30.909 50.920 60.970 81.000 

6E 0.890 20.898 40.908 60.968 30.900 50.925 70.974 81.000 

7A 1.000 11.000 11.000 

10.889 11.000 11.000 11.000 11.000 11.000 11.000 

7B 0.957 40.965 20.915 71.000 50.979 50.979 30.922 71.000 

7C 0.995 20.992 

10.887 10.991 50.996 60.998 20.992 40.994 60.998 81.000 

7D 0.986 30.992 10.990 10.990 70.998 30.992 50.995 60.996 81.000 

Table 9. Values for FNIR at a fxed FPIR of 10−1 for latent identifcation, separated by latent activity. These values are extracted from 

those plotted on the vertical dashed lines in Fig. 19. Each row represents one latent activity. In each row, the Baseline Data is recorded 

in blue, the best non-baseline values are reported in green and the worst in red. Challenger values that meet or beat the baseline for 

each activity are shaded in blue . Superscript numbers preceding the FNIR value represent its ranking in each row. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
44 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

7D

6E 7A 7B 7C

5C 6A 6C 6D

4F 4G 5A 5B

4B 4C 4D 4E

2A 2B 3 4A

1E 1F 1G 1H

1A 1B 1C 1D

0.01 0.1 0.5

0.01 0.1 0.5 0.01 0.1 0.5 0.01 0.1 0.5

0.7

0.8

0.9

1.0

0.7

0.8

0.9

1.0

0.7

0.8

0.9

1.0

0.7

0.8

0.9

1.0

0.7

0.8

0.9

1.0

0.7

0.8

0.9

1.0

0.7

0.8

0.9

1.0

0.7

0.8

0.9

1.0

False Positive Identification Rate

False Negative Identification Rate

Challenger Ω A B C D E F G H

All Challengers, Latent Identification

Detection Error Tradeoff: By Activity

Fig. 19. DET curves for each Challenger for latent identifcation, separated by latent activity. This plot shows the accuracy of the 

Matcher when searching study participant latent fngerprint images against an enrollment set seeded with N2N images from each 

Challenger. The vertical dashed line indicates the FPIR at which a prize was to be awarded for the lowest overall FNIR, and the values 

lying on this line are reported in Table 9. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 45 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Activity Ω A B C D E F G H

1A 11.2 % 38.0 % 46.4 % 62.8 % 211.5 % 56.1 % 71.5 % 80.0 % 

1B 0.0 % 10.0 % 10.0 % 

114.1 % 10.0 % 10.0 % 10.0 % 10.0 % 10.0 % 10.0 % 

1C 9.8 % 28.7 % 47.2 % 71.3 % 38.6 % 55.3 % 61.7 % 80.0 % 

1D 30.0 % 326.9 % 425.0 % 77.6 % 228.7 % 521.8 % 67.9 % 80.0 % 

1E 18.1 % 312.8 % 412.4 % 63.3 % 216.9 % 58.7 % 73.1 % 80.0 % 

1F 33.0 % 328.4 % 426.7 % 76.8 % 229.4 % 521.4 % 68.9 % 80.0 % 

1G 2.2 % 61.0 % 21.6 % 

19.8 % 133.0 % 122.2 % 133.4 % 12.0 % 70.3 % 41.3 % 31.3 % 51.0 % 80.0 % 

1H 11.4 % 310.8 % 212.6 % 72.7 % 410.7 % 57.1 % 62.9 % 80.0 % 

2A 3.3 % 24.0 % 33.8 % 70.4 % 43.7 % 52.7 % 61.0 % 80.0 % 

2B 2.9 % 33.0 % 23.1 % 70.4 % 42.7 % 52.1 % 60.5 % 80.0 % 

3 1.6 % 

113.6 % 14.8 % 13.2 % 41.7 % 22.0 % 60.6 % 31.9 % 51.4 % 70.1 % 80.0 % 

4A 2.9 % 42.3 % 32.7 % 70.5 % 23.0 % 52.2 % 60.6 % 80.0 % 

4B 4.4 % 44.0 % 24.4 % 

12.4 % 13.2 % 34.2 % 61.0 % 52.3 % 61.0 % 80.0 % 

4C 2.2 % 42.3 % 32.5 % 60.5 % 51.8 % 70.0 % 70.0 % 

4D 3.2 % 32.6 % 32.6 % 61.0 % 

14.6 % 12.8 % 22.9 % 52.1 % 70.5 % 80.0 % 

4E 3.0 % 32.7 % 42.6 % 

12.8 % 13.6 % 22.9 % 70.7 % 51.6 % 60.8 % 80.0 % 

4F 1.7 % 21.9 % 41.1 % 70.2 % 

13.0 % 31.7 % 50.9 % 60.6 % 80.0 % 

4G 3.7 % 23.4 % 

12.0 % 14.1 % 15.1 % 12.2 % 12.2 % 12.7 % 13.6 % 111.1 % 111.1 % 111.5 % 

33.3 % 60.8 % 

12.2 % 

43.2 % 52.6 % 70.7 % 80.0 % 

5A 3.8 % 43.9 % 24.6 % 70.7 % 34.0 % 53.3 % 61.4 % 80.0 % 

5B 1.9 % 41.9 % 60.3 % 51.4 % 70.0 % 70.0 % 

5C 0.8 % 21.9 % 51.1 % 60.3 % 31.4 % 31.4 % 70.0 % 70.0 % 

6A 2.8 % 23.1 % 42.8 % 60.7 % 32.9 % 51.3 % 70.6 % 80.0 % 

6C 10.6 % 210.0 % 48.6 % 72.4 % 39.6 % 56.9 % 63.0 % 80.0 % 

6D 10.8 % 29.8 % 49.5 % 72.7 % 39.6 % 58.1 % 63.1 % 80.0 % 

6E 11.4 % 210.7 % 49.7 % 63.5 % 310.3 % 57.9 % 72.7 % 80.0 % 

7A 0.0 % 10.0 % 10.0 % 10.0 % 10.0 % 10.0 % 10.0 % 10.0 % 10.0 % 

7B 4.3 % 43.5 % 28.5 % 70.0 % 52.8 % 62.1 % 37.8 % 70.0 % 

7C 0.5 % 20.9 % 

111.3 % 10.9 % 11.3 % 

50.5 % 60.3 % 30.8 % 40.6 % 70.2 % 80.0 % 

7D 1.3 % 30.9 % 21.1 % 70.1 % 40.9 % 50.6 % 60.4 % 80.0 % 

Table 10. Hit rate for Rank 1 for the Matcher when searching study participant latent fngerprint images against an enrollment set 

of Challenger N2N images, separated by latent activity. Each row represents one latent activity. In each row, the Baseline Data is 

recorded in blue, the best non-baseline values are reported in green and the worst in red. Challenger values that meet or beat the 

baseline for each activity are shaded in blue . Superscript numbers preceding the FNIR value represent its ranking in each row. All 

ranks are depicted in Fig. 20. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
46 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

7D

6E 7A 7B 7C

5C 6A 6C 6D

4F 4G 5A 5B

4B 4C 4D 4E

2A 2B 3 4A

1E 1F 1G 1H

1A 1B 1C 1D

1 25 50 75 100

1 25 50 75 100 1 25 50 75 100 1 25 50 75 100

0%

10%

20%

30%

40%

0%

10%

20%

30%

40%

0%

10%

20%

30%

40%

0%

10%

20%

30%

40%

0%

10%

20%

30%

40%

0%

10%

20%

30%

40%

0%

10%

20%

30%

40%

0%

10%

20%

30%

40%

Rank

Match Percentage

Challenger Ω A B C D E F G H

All Challengers, Latent Identification

Cumulative Match Characteristic: By Activity

Fig. 20. CMC curves for each Challenger for latent identifcation, separated by latent activity. This plot shows the rate at which the 

Matcher, when searching study participant latent fngerprint images against an enrollment set seeded with N2N images from each 

Challenger, found the correct mate, regardless of similarity score. The values for rank 1 are reported in Table 10. If a curve stops before 

reaching the far right of the plot, it indicates that a mate was never found past the last corresponding rank. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 47 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Activity Searches Study Participants FNIR Challenger 

1A 652 198 0.860 C

1B 31 15 1.000 — 

1C 5 468 272 0.905 Ω

1D 2 877 253 0.684 C

1E 2 586 272 0.788 C

1F 2 706 214 0.681 C

1G 2 102 222 0.978 Ω

1H 6 194 217 0.868 A

2A 2 306 163 0.954 A

2B 3 278 158 0.969 A

3 2 319 54 0.977 C

4A 4 447 74 0.970 C

4B 2 072 23 0.958 Ω, B, E

4C 1 009 17 0.973 C, E

4D 1 827 26 0.966 C

4E 2 078 62 0.970 Ω, E

4F 3 332 45 0.980 C

4G 1 650 80 0.965 B

5A 1 444 84 0.952 A

5B 2 557 74 0.978 B

5C 365 56 0.978 B

6A 867 306 0.967 C

6C 2 566 108 0.893 C

6D 1 056 111 0.894 Ω

6E 2 689 109 0.889 C

7A 296 34 1.000 — 

7B 141 6 0.887 B

7C 1 977 71 0.991 C

7D 1 829 82 0.986 Ω

62 721 

Table 11. A breakdown of the data used in latent identifcation. Searches shows the number of searches for a given Activity. Study 

Participants shows the number of study participants represented in Searches. FNIR is the lowest FNIR for each activity from Fig. 19. 

Challenger contains the Challengers that achieved FNIR. Only those images that were successfully searched by the Matcher are 

represented. Not all study participants left behind latent prints for all activities. Not all collected data was able to be developed and 

digitized prior to releasing results. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
48 N2N Challenge Prize Analysis

B. Podium Finishers 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

No Challenger was able to achieve an FNIR within 2 % of Ω for N2N identifcation, and as such, there was 

no winner for the Nail to Nail Fingerprint Challenge’s Grand Prize. The N2N Judging Committee opted to 

divide the funds that would have been awarded to the Grand Prize winner amongst the podium fnishers 

for all other prizes. 

B.1 Speed Prize 

From the values presented in Table 4, second place for the Speed Prize should be awarded to A, and third 

place should be awarded to F. 

B.2 Gallery Accuracy 

From the values presented in Table 5, second place for the Gallery Accuracy Prize should be awarded to E, 

and third place should be awarded to A. 

B.3 Latent Accuracy 

The values presented in Table 7 show a tie within the confdence interval for second place for the Latent 

Accuracy Prize. Per the metrics in Section 7.1.5, NFIQ 2.0 values, presented in Table 12, are used to break 

the tie. As is required by NFIQ 2.0, all images were downsampled, if necessary, to 196.85 PPCM (500 PPI) 

before running the algorithm [2]. 

Following the Nail to Nail Fingerprint Challenge rules, starting with the NFIQ 2.0 column in Table 7, the 

tie is broken. Second place for the Latent Accuracy Prize should be awarded to E, and third place should be 

awarded to A. 

It should be noted that the NFIQ 2.0 algorithm was trained on plain impression live scan images, which 

does not describe any of the images created by the Challengers. Because all Challenger images di ̇er from 

the training data used in NFIQ 2.0, the values produced may seem peculiar. As a result, it is prudent to 

use the individual NFIQ 2.0 feature values to break the tie. Even then, standard deviations for ridge valley 

uniformity and frequency domain analysis amongst Challengers have nearly indistinguishable distributions. 

Additionally, not all Challengers acquired the same number of images, which skews these distributions 

(Section 9.2). 

Challenger NFIQ 2.0 FDA σ RVU σ

Ω 53 0.231 0.975 

A 820 80.313 61.162 

B

C

153 10.211 10.876 449 50.269 41.138 

D 540 20.224 31.044 

E 634 60.293 81.291 

F 350 40.267 20.907 

G 731 70.294 71.172 

H 251 30.267 51.139 

Table 12. Median values produced by NFIQ 2.0 computed for images acquired by all Challengers. In the event of a tie for any prize, 

the tie would be broken based on these values. The Baseline Data is recorded in blue. The best non-Baseline values in each column are 

reported in green and the worst are in red. Challenger values that meet or beat the Baseline are shaded in blue . Superscript numbers 

preceding values represent the ranking of the value in each column. FDA refers to Frequency Domain Analysis and RVU refers to 

Ridge Valley Uniformity, as defned in [12], with σ indicating standard deviation. Refer to Appendix B.3 for NFIQ 2.0 caveats. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
N2N Challenge Prize Analysis 49 

C. Public Data 

Through the fngerprint data collection held at JHU APL, IARPA amassed a large and diverse set of friction 

ridge imagery. Much care was taken to ensure that informed consent documents and research protocols 

approved by the IRBs and agreed to by study participants allowed for the public distribution of the friction 

ridge data collected. 

IARPA is pleased to announce that data collected as part of the Nail to Nail Fingerprint Challenge is being 

released to biometric researchers to aid in the advancement of friction ridge research. NIST will be handling 

the distribution of the data on behalf of IARPA. 

To help support future NIST biometric technology evaluations and biometric research challenges, IARPA 

has agreed to allow NIST to hold back data from 30 % of the study participants. NIST plans to announce 

opportunities for the research community to participate in studies that will make use of this data. 

As of the publication date, the distribution is not yet ready. To be notifed when the data is ready for 

release, visit NIST’s website, https://nist.gov/programs-projects/n2n-fngerprint-capture-challenge. Once 

available, researches will be able to complete a web form and directly download the data. 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
50 N2N Challenge Prize Analysis

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

D. Glossary 

Backend Server 

The computer network and database infrastructure used to collect fngerprint images from N2N 

devices. 

Baseline Data 

Fingerprint images rolled on a live scan device by a skilled operator during the Nail to Nail Fingerprint 

Challenge. 

Challenger 

A fnalist in the Nail to Nail Fingerprint Challenge that brought a device to the fngerprint data 

collection. 

Extractor 

The fngerprint feature extraction and template generation algorithm provided by NIST. The algorithm 

extracts features from a fngerprint image and saves them into a template that can be inserted into an 

enrollment set or searched. 

Facility Application Programming Interface 

The programming interface by which Challengers submitted captured imagery to the N2N backend 

server. 

Judging Committee 

A three-person committee comprised of United States Government oÿcials who determined the 

winners of the Nail to Nail Fingerprint Challenge and ruled on changes to the test plan. 

Matcher 

The fngerprint template matching algorithm provided by NIST for use in searching N2N and latent 

fngerprint images against a large enrollment set. 

Moderated Capture 

A method of capturing fngerprints without the physical touch of a skilled operator. An operator is 

still present, but can only provide feedback to the subject. The operator may request that the subject 

retry an acquisition. This capture type is somewhere between fully controlled and fully uncontrolled. 

Nail to Nail 

The surface area of a fnger’s distal phalanx from one edge of the fngernail to the other, ideally 

including the tip. This is often abbreviated as N2N. 

Rolled 

A nail to nail capture, where the fnger comes in contact with a fat surface, such as a ink fngerprint 

card or a live scan platen. 

Rolled-equivalent 

A nail to nail capture. This di ̇ers from rolled in that it does not imply that the fnger comes in 

contact with a platen, only that the resulting image performs equivalently to one that does. It may 

be possible for a touch-free device to capture an image from one edge of the fngernail to the other 

without physically rolling the fnger on a fat surface. 

A IDEMIA B Advanced Optical Systems C Green Bit D Cornell University Ω FBI Baseline E Jenetric F Touchless Biometric Systems G Crossmatch H Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag 
51 

This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8210 

N2N Challenge Prize Analysis

Skilled Operator 

A individual with expert knowledge on fngerprint image quality that operates a fngerprint capture 

device. When capturing a rolled fngerprint, the individual holds the hand of the subject and physically 

performs the rolling action. 

Study Participant 

An individual recruited for the Nail to Nail Fingerprint Challenge who provided their fngerprints to 

all Challengers. 

Ω FBI Baseline A

E

IDEMIA 

Jenetric 

B

F

Advanced Optical Systems 

Touchless Biometric Systems 

C

G

Green Bit 

Crossmatch 

D

H

Cornell University 

Clarkson University 

1A Peering Into Window 1B Fist Banging on Glass 1C Fingertip Window Slide 1D Get-away Palm on Glass 1E “OK” Sign on Glass 

1F Counter Vault on Glass 1G Cylinder Grab 1H Impatient Tapping on Glass 2A Samsung Galaxy S5 2B Apple iPhone 5s 

3 Check 4A Lined Paper 4B Low-quality Copy Paper 4C High-quality Copy Paper 4D Yellow Lined Paper 

4E

5C

Low-quality White Envelope 

U.S. Currency 

4F

6A

Greeting Card and Envelope 

Stamp 

4G

6B

Manila Envelope 

Address Label 

5A

6C

Photo Paper 

Clear Packing Tape 

5B

6D

Glossy Magazine 

Black Electrical Tape 

6E Duct Tape 7A Circuit Board 7B CD/DVD 7C Clear Plastic Bag 7D Black Plastic Bag