Effects of JPEG 2000 Image Compression on  

1000 ppi Fingerprint Imagery

NIST Interagency Report 7778

Shahram Orandi, John M. Libert, John D. Grantham,  

Kenneth Ko, Stephen S. Wood, Jin Chu Wu

Image Group

Information Access Division

Information Technology Laboratory

National Institute of Standards and Technology

May 25, 2011
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ACKNOWLEDGEMENTS

The authors wish to give special thanks to the following individuals and organizations for their support of this work:

Federal Bureau of Investigation for all their support throughout this study

T.J. Smith and the LA County Sheriff’s Department  

R. Michael McCabe, IDTP

Margaret Lepley, MITRE

Tom Hopper, Cogent Systems

Stephen Meagher, Dactyl ID, LLC

Ron Smith & Associates

In addition, we appreciate the guidance, support and coordination provided by Michael Garris without whose help

this study would not have been possible.

DISCLAIMER

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

described in this document. In no case does identification of any commercial product, trade name, or vendor, imply

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

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

The criminal justice communities throughout the world exchange fingerprint imagery data primarily in 8‐bit gray‐scale

and at 500 pixels per inch1 (ppi) or 19.7 pixels per millimeter (ppmm).    The Wavelet Scalar Quantization (WSQ)

fingerprint image compression algorithm is currently the standard algorithm for the compression of 500 ppi

fingerprint imagery.  WSQ is a “lossy” compression algorithm.  Lossy compression algorithms employ data encoding

methods which discard (lose) some of the data in the encoding process in order to achieve an aggressive reduction in

the size of the data being compressed.   Decompressing the resulting compressed data yields content that, while

different from the original, is similar enough to the original that it remains useful for the intended purpose.  The WSQ

algorithm allows for users of the algorithm to specify how much compression is to be applied to the fingerprint image

at the cost of increasingly greater loss in fingerprint image fidelity as the effective compression ratio is increased (see

Figure 1 for an example of image degradation from lossy compression).    The WSQ Gray‐Scale Fingerprint Image

Compression Specification [WSQ] provides guidance based on an International Association for Identification (IAI)

study [FITZPATRICK] to determine the acceptable amount of fidelity loss due to compression in order for a WSQ

encoder and decoder to meet FBI certifications. These certifications are designed to ensure adherence to the WSQ

specification and thereby to ensure fidelity and admissibility in courts of law for images that have been processed by

such encoders and decoders.     

For 1000 ppi (39.4 ppmm) fingerprint imagery, MITRE has developed an informative guidance (“Profile for 1000 ppi

Fingerprint Compression” [MTR1]) that widely is recognized as the de facto standard guidance for utilizing JPEG 2000

for the compression of fingerprint imagery at 1000 ppi.  This document provides an excellent basis for a compression

profile for 1000 ppi fingerprint imagery using JPEG 2000, particularly in its specification of software parameters for

control and structure of the JPEG 2000 code stream, and its recommendations form the basis for the compression

strategy used in this study. The MITRE guidance is informative, however, and makes no attempt to prescribe an

optimal compression rate.   This study builds on the MITRE work, both to illuminate the effects of compression on

fingerprint features used for identification by trained examiners in the context of the MITRE JPEG 2000 profile for

1000 ppi imagery, and also to develop a basis from which a normative guidance regarding application of JPEG 2000 to

fingerprint images can be established.

Furthermore, this study extends the work of the IAI with 500 ppi WSQ images to 1000 ppi images compressed using

JPEG 2000.  Based on the IAI criteria for non‐Galton feature loss in formulating its 500 ppi guidance, findings detailed

in this report suggest  that a compression ratio lower than 15:1 be considered in a normative guidance for application

of JPEG 2000 to 1000 ppi images. This is partly due to the experimental observation that the generalized behavior of

JPEG 2000 is not as optimal as WSQ for fingerprint imagery.  Therefore a less aggressive compression strategy with

JPEG 2000 at 1000 ppi is needed to place it on an equal footing with WSQ for fingerprint imagery.

This study examines the effects of increasing compression in terms of image degradation as observed by examiners

as well as the effects of increasing compression on the ability of examiners to make their identification decisions.  This

study confirms that increasing compression does result in progressively greater image degradation.   It finds that this

degradation impacts some types of images more aggressively than others (e.g., matched rolled pairs are affected

more than matched flat pairs).  Although the study confirms that, even at the highest compression ratio examined

(38:1), the correctness of the examiners’ identification decisions is not affected in any statistically significant way, the

study does not attempt to measure either the total effort necessary to reach these decisions or the examiners’

confidence in those decisions.

                                                                    1

Resolution values for fingerprint imagery are specified in pixels per inch (ppi) throughout this document.  This is based on

widely used specification guidelines for such imagery and is accepted as common nomenclature within the industry. SI

units for these will be presented only once.
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VERSION HISTORY

Date Activity

05/25/2011 Initial Release

   

   
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TABLE OF CONTENTS

1. Investigative Goals and Objectives................................................................................................................................17

1.1. Background...........................................................................................................................................................17

1.2. Market drivers.......................................................................................................................................................18

2. Materials and Methods .................................................................................................................................................. 21

2.1. Compression Algorithm........................................................................................................................................22

2.2. Methodology ....................................................................................................................................................... 24

2.3. Participants .......................................................................................................................................................... 26

3. Analysis ...........................................................................................................................................................................27

3.1. Observed Image Quality.......................................................................................................................................27

3.2. Identification Errors............................................................................................................................................. 36

4. Results............................................................................................................................................................................ 43

4.1. Investigative Goal 1: Validate 15:1 target compression ratio.............................................................................. 43

4.2. Investigative Goal 2: Examine image degradation relative to compression ratio ............................................ 44

4.3. Investigative Goal 3: Assess impact of compression on identification error rates........................................... 45

4.4. Investigative Goal 4: Examine compression anomalies relative to impression type........................................ 47

5. Conclusions.....................................................................................................................................................................51

6. Future Work....................................................................................................................................................................51

References............................................................................................................................................................................. 53

Publications and Reports .................................................................................................................................................. 53

Standards........................................................................................................................................................................... 55

Appendix A. IAI WSQ Compression / Decompression Study Summary......................................................................... 57

Appendix B. Dataset Makeup.......................................................................................................................................... 59

Appendix C. Equipment Used for Study ......................................................................................................................... 65

Appendix D. Observation Data ........................................................................................................................................ 67

Ink Capture Compression Degradation Observations..................................................................................................... 67

Live Capture Compression Degradation Observations ...................................................................................................68

Ink Capture Compression Identification Observations ...................................................................................................69

Live Capture Compression Identification Observations.................................................................................................. 70

Appendix E. Statistical Parameters of Bootstrap Results and Hypothesis Testing....................................................... 71
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LIST OF TABLES

Table 1 ‐ Abbreviations ...........................................................................................................................................................13

Table 2 ‐ Impression Types Selected for This Study.............................................................................................................. 21

Table 3 ‐ Pairing of Each Original Images to Compressed Counterpart...............................................................................22

Table 4 ‐ Compression Levels Used in Study.........................................................................................................................23

Table 5 ‐ Other JPEG 2000 Compressor Settings Used in Study ..........................................................................................23

Table 6 ‐ Observation Codes for Compression Degradation Observation ......................................................................... 24

Table 7 ‐ Normalized Ratings and Assigned Costs ............................................................................................................... 28

Table 8‐ Z‐Scores ....................................................................................................................................................................32

Table 9 ‐ P‐Values for 2‐Tailed Tests...................................................................................................................................... 33

Table 10 ‐ Observation Codes for Determination of Identity .............................................................................................. 36

Table 11 ‐ Normalization Table for Identification Decision .................................................................................................. 36

Table 12 ‐ IAI Study Results.................................................................................................................................................... 57

Table 13 ‐ Key protocol differences between IAI WSQ study and This Study..................................................................... 58

Table 14 ‐ Ink Card Scan Data classification by Impression Type.........................................................................................60

Table 15 ‐ Ink Card Scan Pattern Classification for Single Finger Images by Impression Type ..........................................60

Table 16 ‐ Ink Card Scan Pattern Classification for Single Finger Images by Finger (Females)..........................................60

Table 17 ‐ Ink Card Scan Pattern Classification for Single Finger Images by Finger (Males)..............................................60

Table 18 ‐ Live‐Scan Data classification by Impression Type ................................................................................................61

Table 19 ‐ Live‐Scan Pattern Classification for Single Finger Images by Impression Type ..................................................61

Table 20 ‐ Live‐Scan Pattern Classification for Single Finger Images by Finger (Females).................................................61

Table 21 ‐ Live‐Scan Pattern Classification for Single Finger Images by Finger (Males)......................................................61

Table 22 ‐ Gender Breakdown for Data ................................................................................................................................ 62

Table 23 ‐ Age Breakdown for Data...................................................................................................................................... 62

Table 24 ‐ Other Metadata: Height and Weight................................................................................................................... 62

Table 25 ‐ Other Metadata: Eye Color................................................................................................................................... 62

Table 26 ‐ Image Geometry Data .......................................................................................................................................... 63

Table 27 ‐ Ink Capture Degradation Results ......................................................................................................................... 67

Table 28 ‐ Live Capture Degradation Results .......................................................................................................................68

Table 29 ‐ Ink Capture Identification Results .......................................................................................................................69

Table 30 ‐ Live Capture Identification Results...................................................................................................................... 70

Table 31  ‐  Distribution parameters of bootstrap replications and hypothesis tests of differences in degradation

between lossless baseline and compressed images (Ink Card Scan).......................................................................... 71

Table 32  ‐  Distribution parameters of bootstrap replications and hypothesis tests of differences in degradation

between lossless baseline and compressed images (Digital Live Capture)................................................................72
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LIST OF FIGURES

Figure 1 ‐ Example of Fidelity Degradation Due to Lossy Compression (JPEG 2000 at 800:1) ...........................................18

Figure 2 ‐ Example of Behavioral Differences between JPEG 2000 and WSQ (15:1 compression).....................................19

Figure 3 ‐ Split‐Screen Presentation of Image Pairs..............................................................................................................25

Figure 4 ‐ Trending of Responses with Increasing Compression.........................................................................................27

Figure 5 ‐ Histogram typical of degradation score data replicates output from bootstrap procedure with the normal

distribution function fitted to data. ............................................................................................................................. 29

Figure 6 ‐ Normal probability plot typical of degradation score data. Normality is indicated by linearity. ...................... 30

Figure 7 ‐ Mean compression anomaly measure with 95 % confidence intervals for inked, rolled‐to‐rolled, fingerprints

(match pairs) for compression ratios from 1:1 to 38:1. ................................................................................................ 34

Figure 8  ‐  Identification error rates for ink card scan rolled to ink card scan flat fingerprint comparisons at the 14

compression levels with confidence limits. ................................................................................................................. 38

Figure 9 ‐ Identification error rates for digital live capture rolled to digital live capture flat fingerprint comparisons at

the 14 compression levels with confidence limits........................................................................................................ 39

Figure 10 ‐ Identification error rates for ink card scan flat to ink card scan rolled fingerprint comparisons at the 14

compression levels with confidence limits. ................................................................................................................. 39

Figure 11 ‐ Identification error rates for digital live capture flat to digital live capture rolled fingerprint comparisons at

the 14 compression levels with confidence limits........................................................................................................40

Figure 12  ‐  Probability of inconclusive determination vs. compression ratio for 10 comparison scenarios and 14

compression ratios.........................................................................................................................................................41

Figure 13 ‐ Summaries over all comparison scenarios showing least squares fit to the mean probability of inconclusive

determination. Slight increasing trend is noted with increasing compression.......................................................... 42

Figure 14 ‐ Observed Compression Anomalies for Case‐1 (Ink Rolled to Ink Rolled).......................................................... 43

Figure 15 ‐ Z‐Score Trends (linear) for Ink Card Scan ........................................................................................................... 44

Figure 16 ‐ Z‐Scores Trends (linear) for Digital Live Capture ............................................................................................... 44

Figure 17 ‐ Comparison of Very High Compression Rates.................................................................................................... 45

Figure 18 ‐ Z‐Scores for Ink Card Scan Cases ........................................................................................................................ 47

Figure 19 ‐ Z‐Scores for Digital Live Capture Cases ..............................................................................................................48

Figure 20 ‐ Impression Comparison Examples ..................................................................................................................... 59

   
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TERMS AND DEFINITIONS

The abbreviations and acronyms of Table 1 are used in many parts of this document.

Table 1 ‐ Abbreviations

NIST National Institute of Standards and Technology

JPEG Joint Photographic Experts Group – ISO/IEC committee developing standards for image compression

IAI International Association for Identification

FBI Federal Bureau of Investigation

SIVV Spectral Image Validation/Verification Metric

NBIS NIST Biometric Image Software

WSQ The Wavelet Scalar Quantization algorithm for compression of fingerprint imagery

NGI Next Generation Identification

IAFIS Integrated Automated Fingerprint Identification System

   
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ABSTRACT

This paper presents the findings of a study conducted to measure the impact of JPEG 2000 compression on 1000 ppi

fingerprint imagery at various levels of compression.    The impact of compression to both Galton and non‐Galton

based features of a fingerprint is measured by utilizing the professional judgment of trained and seasoned fingerprint

examiners.   This impact is analyzed by consolidating and quantifying multiple decisions and associating a cost with

the different levels of image compression loss incurred during compression.  In addition to measuring the perceived

visual impact of compression on the aforementioned features of the fingerprint as a result of compression, this paper

also looks at the impact of compression on the examiner’s ability to render identification decisions.

KEYWORDS

Fingerprint compression; 1000 ppi fingerprint imagery; JPEG 2000 fingerprint compression
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1. Investigative Goals and Objectives

In July of 2009 NIST in partnership with the Federal Bureau of Investigation (FBI) commenced an investigation on the

use of JPEG 2000 [JPEG2K] for compressing fingerprint imagery with the following objectives:

1. Validate 15:1 target compression ratio:   Validate the 15:1 effective compression ratio target for fingerprint

imagery captured at 1000 ppi as defined in the current informative guidance for 1000 ppi [MTR1] using the

legacy methodology that formed the 500 ppi guidance [FITZPATRICK].

2. Examine image degradation relative to compression ratio: Assess if higher compression ratios result in

increased perceived image degradation and note any patterns in degradation relative to compression.

3. Assess impact of compression on identification error rates:   Assess if higher compression ratios result in

increased identification error rates for fingerprint examiners and note any patterns in the error rates.

4. Examine compression anomalies relative to impression type:  Determine whether any particular fingerprint

impression types are more susceptible to compression related anomalies than others at the various

examined compression ratios.

While addressing the above objectives, the investigators also set out to expand upon prior work that examined only

rolled‐single‐fingerprint images by also taking into account flat, rolled and slap fingerprint imagery for both inked card

scan and digital live scan modalities.  

1.1. Background

The criminal justice communities throughout the world exchange fingerprint imagery data primarily in 8‐bit gray‐scale

at 500 pixels per inch (ppi).    The Wavelet Scalar Quantization (WSQ) fingerprint image compression algorithm is

currently the standard algorithm for the compression of 500 ppi fingerprint imagery.  The WSQ standard defines a

class of encoders and decoders with sufficient interoperability to ensure that images encoded by one compliant

encoder can be decoded by any other compliant decoder.     

WSQ is a “lossy” compression algorithm.  Lossy compression algorithms employ data encoding methods that discard

(lose) some of the data in the encoding process in order to achieve an aggressive reduction in the size of the data

being compressed.    Decompressing the resulting compressed data yields content that, while different from the

original, is similar enough to the original that it remains useful for the intended purpose.    Lossless compression

algorithms on the other hand can produce a compressed image that can be decompressed back to original form with

no loss or change to the resulting image.  The disadvantage to lossless algorithms is that they produce compressed

images that can be many times larger than compressed images produced by lossy algorithms.

The lossy WSQ algorithm allows for users of the algorithm to specify how much compression to apply to the

fingerprint image at the cost of increasingly greater loss in fingerprint image fidelity as the effective compression

ratio is increased (see Figure 1 for example of image degradation from lossy compression).    The WSQ Gray‐Scale

Fingerprint Image Compression Specification [WSQ] provides guidance for the acceptable amount of fidelity loss due

to compression in order for the encoder and decoder to meet FBI certifications for 500 ppi fingerprint imagery. These

certifications are designed to ensure adherence to the WSQ specification to ensure fidelity and admissibility in courts

of law for images that have been processed by such encoders and decoders.     
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Original Image Compressed Image (Lossy)

Figure 1 ‐ Example of Fidelity Degradation Due to Lossy Compression (JPEG 2000 at 800:1)

A study conducted by the International Association for Identification (IAI) [FITZPATRICK] established 15:1 as the

maximum WSQ compression ratio that would retain acceptable image fidelity.   The study used the opinions of expert

fingerprint examiners to measure the fidelity loss due to compression.    In order to reduce bias due to subjectivity,

multiple examiner decisions were used to build consensus.  Utilizing examiner opinion does not imply that automated

fingerprint matcher performance is not an important criterion in a given biometric system, but it must be noted that if

fingerprints are to be admissible as evidence in a court of law their ultimate utility lies in the expert examiner’s

opinion of the fidelity of those fingerprints.

1.2. Market drivers

In modernizing its environment as part of the Next Generation Identification (NGI) initiative, the FBI seeks to expand

its ability to exchange fingerprints at 1000 pixels per inch in an effort to improve upon the capacity of systems in

fingerprint identification and verification tasks and meet the FBI mandate to:

‐ Protect the United States from terrorist attack, foreign intelligence operations and espionage

‐ Support federal, state, local and international partners in their efforts to prevent or reduce crime and

violence

‐ Upgrade technology to support the FBI's missions

Toward meeting this goal, the FBI seeks to set guidance for the next generation encoders and decoders based on the

open JPEG 2000 compression standard in order to ensure interoperability, fidelity and admissibility in courts of law for

1000 ppi images in the criminal justice community.

In support of the FBI, the National Institute of Standards and Technology (NIST) conducted a study to determine an

optimal compression approach that follows on the IAI study of WSQ compression for 500 ppi fingerprint imagery,

build upon existing guidance for JPEG 2000 compression of fingerprint imagery, and formulate a basis with which a

normative compression guidance can be established in the ANSI/NIST standard [AN27].    NIST has an established

expertise in evaluating biometric systems and standards, and has been assigned by the USA PATRIOT Act (Public Law

107‐56) the responsibility for developing and certifying biometric technology standards.   NIST has been supporting

biometric standards and evaluation activities for over forty years, starting with fingerprint analysis which began in

1965.

MITRE has developed an informative guidance that is widely recognized as the de facto standard guidance for utilizing

JPEG 2000 for the compression of 1000 ppi fingerprint imagery in MTR‐04B0000022 [MTR1].   While this document

provides an excellent basis for a compression profile for 1000 ppi fingerprint imagery using JPEG 2000, some of the

informative guidance provided is based loosely on existing accepted guidance for WSQ at 500 ppi.    
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It should also be noted that unlike WSQ, JPEG 2000 supports both lossy functionality similar to WSQ (see 1.1), as well

as a lossless mode.  This study examines JPEG 2000 in lossy mode as it has been prescribed in the existing guidance

[MTR1].  

A preliminary analysis of WSQ behavior vs. JPEG 2000 was conducted early in the present study in which empirical

observations were made that indicated significant behavioral differences between the two compression algorithms.

These observations reinforced the need to conduct a full study of JPEG 2000 compression at 1000 ppi rather than

attempting to incorporate existing informative guidance on a normative basis.    This exploratory evaluation of

compression algorithm behavior was conducted at 500 ppi due to the 500 ppi limitation of the WSQ’s inherent design.  

In conducting this exploratory evaluation, compression parameters based on the 1000 ppi profile [MTR1] were

adjusted for use of  the JPEG 2000 compressor operating at 500 ppi.

  

Figure 2 ‐ Example of Behavioral Differences between JPEG 2000 and WSQ (15:1 compression)

The example in Figure 2 shows spectral energy [LIBERT] for a rolled 500 ppi fingerprint image without compression

(labeled “Original”) and the spectral energies of the corresponding WSQ and JPEG 2000 compressed copies of this

image.    WSQ maintains near‐identical image energy fidelity well into 0.35 cycles/pixel, whereas JPEG 2000 appears to

lose image energy at frequencies as low as 0.20 cycles/pixel.   Image ridge information is represented in this figure as a

peak located at approximately 0.10 cycles/pixel on the image energy spectrum.  This demonstrates WSQ outperforms

JPEG 2000 by maintaining fidelity over a greater range than JPEG 2000.  This is primarily due to the fact that WSQ has

been very specifically tuned and designed for its intended purpose of compressing gray‐scale fingerprints, whereas

JPEG 20002 was designed as a more generalized algorithm for applications beyond fingerprints, such as color

photographs.  

The empirical evidence observed in this preliminary study demonstrated the fact that the accepted 15:1 guidance for

WSQ may not be directly applicable to JPEG 2000 due to systematic differences between the two algorithms, and that

this behavioral difference warrants an in‐depth study of the JPEG 2000 algorithm and its application in fingerprint

compression.

                                                                    2 JPEG 2000 Part 2 provides for additional wavelet filter customization that may improve its performance for fingerprint image

compression. However, these features are not compatible with most JPEG 2000 decoders, so they were not included in the MITRE

JPEG 2000 profile, nor were they included in the present data preparation.

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.00 0.10 0.20 0.30 0.40

Normalized Log Power (dB)

Frequency (cycles/pixel)

WSQ Behavior vs. JPEG 2000

Original

WSQ

 JPEG 2000 (OpenJPEG)
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2. Materials and Methods

In this experiment a deck of 4,000 1000 ppi images was assembled by sampling randomly from actual operational

data supplied to NIST by state and federal partners.  The 4,000 image set contained various image impression types as

described in Table 2 from a total of 223 unique individuals (100 for ink card scan, and 123 for digital live capture) as

described in Appendix B and this set was sampled for both match and non‐match pairings.    Also, as noted in Table 2,

image pairs were selected from both match samples (where the two images were from the same finger/same person)

as well as non‐match samples of corresponding fingers of the same Galton [GALTON] three‐pattern classification (the

same pattern of either Arch, Loop or Whorl, but from different individuals and therefore non‐matching).

Table 2 ‐ Impression Types Selected for This Study

Case Number Data Medium Source (Original) Image Comparison Image

(Compressed, plus Control Case)

Match Pair Count

1 Scanned Fingerprint Card Rolled single finger Rolled single finger Yes 200

2 Scanned Fingerprint Card Rolled single finger Rolled single finger No 200

3 Scanned Fingerprint Card Flat single finger Rolled single finger Yes 200

4 Scanned Fingerprint Card Flat single finger Rolled single finger No 200

5 Scanned Fingerprint Card Rolled single finger Flat single finger Yes 200

6 Scanned Fingerprint Card Rolled single finger Flat single finger No 200

7 Scanned Fingerprint Card Flat single finger Flat single finger Yes 200

8 Scanned Fingerprint Card Flat single finger Flat single finger No 200

9 Scanned Fingerprint Card Four finger slap Four finger slap Yes 200

10 Scanned Fingerprint Card Four finger slap Four finger slap No 200

11 Digital Live Capture Rolled single finger Rolled single finger Yes 200

12 Digital Live Capture Rolled single finger Rolled single finger No 200

13 Digital Live Capture Flat single finger Rolled single finger Yes 200

14 Digital Live Capture Flat single finger Rolled single finger No 200

15 Digital Live Capture Rolled single finger Flat single finger Yes 200

16 Digital Live Capture Rolled single finger Flat single finger No 200

17 Digital Live Capture Flat single finger Flat single finger Yes 200

18 Digital Live Capture Flat single finger Flat single finger No 200

19 Digital Live Capture Four finger slap Four finger slap Yes 200

20 Digital Live Capture Four finger slap Four finger slap No 200

Total: 4,000

Each pairing from the 20 cases in Table 2 consists of a source image and a comparison image.  The source image is the

original image with no compression operations applied to it in that image’s lifecycle.   The comparison image from

each of the 20 cases described in Table 2 was processed by compressing that image into each of the 14 ratios

described in Table 3, and then decompressing the image. Compression is “lossy” in all cases except that for the 1:1

ratio (control case) where no compression was applied. This processed image has now passed through the

compression and decompression stages of the algorithm representing what would be the typical lifecycle of the

image in a normal operational environment just prior to processing/enrollment.   Each image pair for every case in

Table 2 is then presented to the examiners as described in section 2.2.   Further details on the selection of images for

this study are described in Appendix B.   

It should also be noted that images may be decompressed many subsequent times, but the decompression process

has no effect on the compressed stream and therefore not a topic of study.   The images were compressed only once,

thereby limiting compression degradation to only that first compression pass.   Additional information on the makeup

of the experimental image deck is provided in Appendix B.

   
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The compressed images resulting from processing were then paired with the original image, creating a set of 56,000

image pairs.   Thus one image of each pair is a copy of the original image, and the second is a copy of the processed

image at one of the various compression ratios, including a control image as shown in Table 3.

Table 3 ‐ Pairing of Each Original Images to Compressed Counterpart

Pair Number Image 1 from pair Image 2 from pair

1 Original / Non‐compressed 1 to 1 (Non‐compressed control pair)

2 Original / Non‐compressed 2 to 1

3 Original / Non‐compressed 5 to 1

4 Original / Non‐compressed 7 to 1

5 Original / Non‐compressed 10 to 1

6 Original / Non‐compressed 12 to 1

7 Original / Non‐compressed 15 to 1

8 Original / Non‐compressed 17 to 1

9 Original / Non‐compressed 20 to 1

10 Original / Non‐compressed 22 to 1

11 Original / Non‐compressed 26 to 1

12 Original / Non‐compressed 30 to 1

13 Original / Non‐compressed 34 to 1

14 Original / Non‐compressed 38 to 1

2.1. Compression Algorithm

The focus of this study is to measure the effects of compression on fingerprint image fidelity from using the JPEG

2000 algorithm [JPEG2K] on fingerprint imagery.    JPEG 2000 is an image compression standard and coding system

that was created by the Joint Photographic Experts Group committee (JPEG) in 2000 to improve on the original JPEG

image compression standard’s discrete cosine transform‐based methodology [JPEG] by utilizing a wavelet‐based

methodology.   This modification yielded increases in both data compression and subjective image quality. Moreover,

JPEG 2000 provides additional flexibility in the creation and manipulation of the code‐stream and is based on the

same family of wavelets as WSQ which is currently the standard for fingerprint image compression at 500 ppi. The

flexibility offered by JPEG 2000 as well as the greater availability of JPEG 2000 implementations, which are commodity

products as opposed to the much more specialized WSQ implementations, make JPEG 2000 a good candidate as the

successor to WSQ.   

The implementation of JPEG 2000 used in this experiment was Open JPEG’s [OPENJPEG] reference implementation

version 1.3.   This reference implementation has been incorporated into the NIST Biometric Image Software (NBIS)

public domain software distribution [NIST2].

   
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For this experiment it was necessary to create a specially tailored compression approach to generate test images for

all necessary compression ratios being examined. This tailoring strategy utilized several exploratory studies which

established bounds and baselines for parameters such as the compression ratio, intermediate compression layers and

decomposition levels.  This tailoring yielded 13 sets of parameters, one set for each of the 13 compression ratios to be

investigated.    Each of the original images used was processed at each of the 13 compression levels, yielding 13

compressed images plus one control image that was not compressed.   Thus each original image used in the study

yielded 14 test images.    The specially tailored compression approach was based on the guidance found in the

informative “Profile for 1000 ppi Fingerprint Compression” [MTR1].  The guidance, which called for 6 decomposition

levels, was adjusted to use 5 decomposition levels using data from the exploratory studies in creating the tailored

parameter sets. These target compression ratios and associated intermediate compression levels are listed in Table 4.   

Note that JPEG 2000 enables structuring of the code stream such that it may be progressively decompressed at any of

a series of intermediate compression levels in addition to the final “target” compression level.  This feature of JPEG

2000 is intended to allow for the display of lower fidelity versions of the image to suit, for example, lower resolution

displays while adding negligibly to the size of the compressed data stream and having no effect on the image at the

target compression level. Other specific configuration parameters used for the compression of images are provided in

Table 5.

Table 4 ‐ Compression Levels Used in Study

Target Compression Ratio Target and Intermediate Compression Levels3

1 to 1 Control image, no processing was performed

2 to 1 214, 144, 86, 58, 34, 24, 15, 10, 2

5 to 1 214, 144, 86, 58, 34, 24, 15, 10, 5

7 to 1 214, 144, 86, 58, 34, 24, 15, 10, 7

10 to 1 324, 214, 144, 86, 58, 34, 24, 15, 10

12 to 1 324, 214, 144, 86, 58, 34, 24, 15, 12

15 to 1 540, 324, 214, 144, 86, 58, 34, 24, 15

17 to 1 540, 324, 214, 144, 86, 58, 34, 24, 17

20 to 1 540, 324, 214, 144, 86, 58, 34, 24, 20

22 to 1 540, 324, 214, 144, 86, 58, 34, 24, 22

26 to 1 980, 540, 324, 214, 144, 86, 58, 34, 26

30 to 1 980, 540, 324, 214, 144, 86, 58, 34, 30

34 to 1 1930, 980, 540, 324, 214, 144, 86, 58, 34

38 to 1 1930, 980, 540, 324, 214, 144, 86, 58, 38

Table 5 ‐ Other JPEG 2000 Compressor Settings Used in Study

Compressor Configuration Setting Description

‐n 6 6 resolution levels (original + 5 levels of decomposition4)

‐p RPCL Resolution‐Position‐Component‐Layer (RPCL) progression order

‐b 64, 64 Code block size of 64x64

‐r [rate values from Table 4] Specifies the target top‐layer rate, plus other quality layers

‐d 0,0 Image origin offset

‐I Use irreversible compression (lossy)

‐S 1,1 Use subsampling factor  of 1,1

                                                                    3 Note that at higher target compression ratios, intermediate compression levels indicated in the guidance were found to cause

malfunction of the Open JPEG 2000 v.1.3 encoder. Partially influenced by the image size, intermediate layers exceeding 324:1 to

540:1 caused the algorithm to return a minimally compressed image rather than the expected ratio. Accordingly, problem images

were re‐run using more modest intermediate layer specifications. The intermediate levels were found to have no effect on the

output image at the target compression ratio.  This issue has been corrected in v.1.4 of the Open JPEG 2000 encoder. 4 The Open JPEG 2000 codec sets the number of decomposition levels to one less than the value specified by this command line

parameter. Hence, ‐n 6 yields 5 decomposition levels.
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2.2. Methodology

Each image pair from Table 2 was shown to exactly three examiners.   Each examiner was first asked to determine if

the image pair being displayed constitutes a matched pair from the same individual (referred to throughout this

document as the “Identification decision” or “Non‐Identification decision”).    Their responses in determining the

identity of the pair being presented to them can be one of three choices:

 The presented image pair is from the same individual ([positive] Identification decision);

 The presented image pair does not appear to be from the same individual (Non‐Identification decision);

 Determination of identity cannot be made (Inconclusive).

Subsequent to their identity determination for the pair of images each examiner then evaluated the image pair on

fidelity loss.   To aid in analysis and quantification of fidelity loss, the examiner’s evaluation was collected by utilizing a

Likert‐type response scale [LIKERT].  The choices that the examiners were allowed to make are provided in Table 6

below.   Observation codes are ordered in ascending order indicating a progressively greater amount of degradation

from 1 to 4.  Furthermore, the features summarized in observation code 3 are among those typically used for forensic‐

level decisions while features summarized in observation code 4 are those used primarily by automated‐matchers in

rendering a match decision.

Table 6 ‐ Observation Codes for Compression Degradation Observation

Observation Numeric

Code

Description

1 No apparent image quality degradation and the quality of Level II (2) 5 and Level III (3)

detail in either image should not cause any difficulty in reaching a conclusive decision

of identification or exclusion.

2 A noticeable degradation in the quality of Level II (2) or Level III (3) detail in either

image, but not enough to have a negative impact on reaching a conclusive decision of

identification or exclusion, though the amount of time to reach a decision may

increase.

3 Level III (3) detail quality diminished in either image to the extent that a Level III (3)

identification is questionable or not possible, and/or is significantly more difficult.

4 Level II (2) detail quality diminished in either image to the extent that a Level II (2)

identification becomes questionable or not possible, and/or is significantly more

difficult.

Examiner responses were recorded by custom test apparatus consisting of a commodity computer and software

designed and developed specifically for this study.    The examiners were not provided any time limits on their

responses.

The 56000 image pairs were queued on each examiner’s workstation and their presentation order was shuffled

randomly on each of the three workstations.

The examiners were provided the basic ability to independently reposition, rotate, invert and zoom in and out of each

of the two images from the pair being examined.  This provided them with the basic tools that they typically employ in

their standard operating environments in performing their duties.  While they were provided with basic tools, more

advanced assistive technologies normally available to some examiners, such as on‐screen feature marking or image

adjustments were not provided to them in the interest of experimental control.    

   

                                                                    5 The commonly accepted nomenclature defines Level 1 fingerprint details as the overall friction ridge pattern and flow, Level 2

detail as classic Galton features [GALTON] like minutiae points, and Level 3 as pores, creases, line shapes, incipient ridges and other

non Level 1 or 2 features [JAIN].
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The image pairs were presented on split‐screen to the examiners in randomized order (see Figure 3), and randomized

placement (left/right split screen placement) to mitigate potential order effects or positional bias.    Scientists

overseeing the tests were blind to the placement order of the images, as well as to the compression level of image

pairs.  These factors were tracked by the test apparatus.

Figure 3 ‐ Split‐Screen Presentation of Image Pairs

Each of the 56000 image pairs was guaranteed to be observed by 3 different examiners over the course of the study

without repeating.  Due to the physical limitations of even the best modern monitors, it is impossible to show a 1000

ppi image without zooming or interpolation.    The software apparatus enabled the examiner to view images at

approximately 10x to 50x of the original size (see Appendix C for more information).

Once the examiner made a determination for a given pair of images, that pair was marked clearly as complete.   The

examiner was allowed to return to a completed pair and re‐examine that image pair without penalty.   The examiner

was also allowed to jump to any image pair in the queue regardless of that pair’s position in the examiner’s queue.

Examiners were provided basic verbal instructions and a demonstration on how to use their workstations and were

allowed a brief practice session using image pairings that were not part of the study to gain familiarity with the

procedure, scoring, and workstation controls.  Examiners were allowed to freely ask questions or clarification on their

workstations or tasking.  The examiners were located in the same room and were allowed to interact freely as they do

in their normal professional practice.   Finally, the examiners were advised that once they’ve selected one of the three

workstations on which to process images, they continue to use that workstation exclusively.   This was done in order

to eliminate the possibility of an examiner processing an image pair more than once.
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2.3. Participants

This study utilized 39 paid professional fingerprint examiners to look at fingerprint image pairs in their professional

capacity and to render their professional judgment much as they do in their normal professional activities.   

There was no attempt to evaluate the examiner’s accuracy or proficiency for this study either prior, during or after the

study.   There was also no attempt to identify or maintain the identity of the individual examiners utilized in this study.  

The examiners had anywhere from two years to well over forty years of experience in fingerprint examination.

A pre‐requisite for examiners being selected to participate in this study was that they be trained latent‐print‐

examiners (LPE’s).  This requirement ensures that the examiners have received the most rigorous training possible and

can resolve even the most difficult fingerprint identification cases which are typically from latent fingerprint images

that, unlike controlled fingerprint captures, are typically fragmentary and often distorted. Of the 39 examiners

participating in this study, 90 % had earned the IAI’s latent examiner certification (and were referred to as certified

latent print examiners, or CLPE’s) with the remaining four being non‐certified latent examiners.     Selection of

examiners for this study was not meant to bias the results by utilizing latent‐only examiners and all of the examiners

selected for this study perform 10‐print case work in addition to latent case work as part of their regular professional

duties.  While not the case in this study, it should be noted that some of LPE’s or CLPE’s working with larger agencies

may be assigned latent‐only case work with almost no 10‐print case work.

The examiners were recruited from various federal, state, local and commercial entities and were permanently based

in 16 states:  Alabama, Arkansas, California, Colorado, Florida, Illinois, Iowa, Louisiana, Maryland, Mississippi, Missouri,

North Carolina, Pennsylvania, South Carolina, Texas, and Virginia.

  
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3. Analysis

3.1. Observed Image Quality

After the completion of data collection a certain degree of preprocessing had to be performed on the raw data in

order to aid in the analysis of the results.  In the case of compression observations, the observation selections by the

examiners were captured by the test apparatus as numeric codes as indicated in Table 6 above.  

Expectation based on anecdotal evidence suggested that with increasing compression one should observe a general

trend in ratings from “1” toward ratings of “4” indicating increasing amounts of observed image degradation due to

compression.  Indeed this is observed to be the case as demonstrated by Figure 4 below.  

Figure 4 ‐ Trending of Responses with Increasing Compression

This trending pattern is generally true for every fingerprint pairing scenario (Table 2) where the frequency of higher

rating codes increases with increasing compression rate.  What is not clear in this is the statistical significance of the

trends observed in image degradation with increasing compression.   

Each rating consists of a composite of response codes by the three examiners.    Several methods were tested to

determine the best means by which to represent the 3‐valued composite evaluations.    In the case of unanimous

assignment of response codes by all examiners, there is no ambiguity in their observation.  However, investigators

had to determine the best way to reconcile any disagreement among examiners.    That is, how can the 3‐valued

ratings be ordered to reflect qualitative and quantitative levels of image degradation?  Given 20 possible 3‐way ratings

from “1,1,1” to “4,4,4” as shown in Table 7, one method explored was to assign an average cost, a real value ranging

from 0 to 1, to each of the 20 possible ratings combinations.  

Based on further examination of how the IAI study utilized a majority‐rule decision in their study [FITZPATRICK], a

“majority‐rule” partitioning of cost was devised as shown in Table 7 where the 20 cases were collapsed into 4 cases.  

The legacy IAI study utilized a mechanism similar to this, but they utilized 2 examiner decision points and employed a

third examiner/decision only where a deadlock was reached, where the third examiner was used to establish

consensus and break the deadlock.   

0

500

1000

1500

2000

2500

1:12:15:17:1

10:112:115:117:120:122:126:130:134:138:1

Number of Observations

Compression Ratio

Observation	Numeric	Code	1

(No	Visible	Degradation)

Observation	Numeric	Code	2

(Some	Benign	Degradation)

Observation	Numeric	Code	3

(Level	3	Detail	Degradation)

Observation	Numeric	Code	4

(Level	2	and	Level	3	Detail	

Degradation)
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Table 7 ‐ Normalized Ratings and Assigned Costs  

Responses from

Observers

Normalized

Rating

Associated

Cost

Justification

1,1,1

1,1,2

1,1,3

1,1,4

0 0 These cases represent either a unanimous or majority ruling of

observation code 1 from Table 6 indicating no apparent image

degradation.

1,2,2

1,2,3

1,2,4

2,2,2

2,2,3

2,2,4

1 0.25 These cases represent either a unanimous or majority ruling of

observation code 2 from Table 6.  This case also includes the

special split‐decision cases of 1, 2, 3 and 1, 2, 4.  These cases are

considered borderline cases but are assigned to this bin as they

are biased towards an acceptable image rating by two

examiners noting little or no observable loss.

1,3,4

2,3,4

1,3,3

2,3,3

3,3,3

3,3,4

2 0.5 These cases represent either a unanimous or majority ruling of

observation code 3 from Table 6. This case also includes the

special split‐decision cases of 1, 3, 4 and 2, 3, 4.  These cases are

considered biased‐towards, and indicative of level‐3 detail loss.

1,4,4

2,4,4

3,4,4

4,4,4

3 1 These cases represent either a unanimous or majority ruling of

observation code 4 from Table 6.

For every condition of each fingerprint type and compression ratio, we have 200 3‐value ratings. Each 3‐value rating is

converted to a single normalized rating (Table 7) reflective of the degree to which one of the fingerprints is judged to

have lost some features useful for identification.  From these 200 ratings, we count the frequency, i f ,  of each of the

normalized rating values (i = 0...3) and convert each frequency to a probability by dividing frequency by 200 (equation

(1)).  

௜݂ ൌ௜ ݌

200 ݅ ൌ0...3 (1)

Then using the cost values specified in Table 7 as a measure of the magnitude of degradation we compute a

degradation score ( Q ) observed for a particular condition as the sum of the products of probabilities of ratings and

cost values assigned to the ratings as shown in equation (2).

Thus, for each matching scenario and compression level, the value of the score,Q , provides a summary measure of

the degree of degradation observed among the 200 image pairs for the condition under examination.   As the 200

measurements for each matching scenario and compression level combination yield only a single value of the

degradation score ( Q ), a bootstrap procedure as described in section 3.1.3 was used to generate a distribution of

such values (bootstrap replicates) from which to estimate uncertainties and parameters to be used in hypothesis

tests of differences among experimental conditions.

௜ܿ௜݌෍ൌ ܳ

ଷ

௜ୀ଴

(2)
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3.1.1. Normality of Degradation Score

In order to apply hypothesis tests as described in the next section, we examined the degree to which the distributions

of bootstrap replicates (see section 3.1.3) of the degradation score approximate a standard normal distribution.

Attempts to apply quantitative tests of normality such as the Shapiro‐Wilks normality test [SHAPIRO] proved

problematic due to the quantized nature of the scores. Such tests compare a smooth, continuous cumulative normal

distribution to similarly behaved data under test.  Our data originate from frequency counts of categorical examiner

responses weighted by a limited number of cost values.   The resulting distributions, though normal in shape, have

cumulative distribution functions composed of a series of discrete steps to which quantitative tests of normality are

quite sensitive. Accordingly, we applied and accept the normality assumption on the basis of inspection of histograms

with overlain normal fitted functions and normal probability plots. Typical examples are displayed in Figures 5 and 6

below. The normal probability plot [CHAMBERS] provides a nonparametric means by which to compare quantiles of

two distributions. Distributions may be taken as normal if a quantile plot overlays that of the standard normal

distribution (a straight line in this type of plot) without major departures from linearity.  

Figure 5 ‐ Histogram typical of degradation score data replicates output from bootstrap procedure with the normal

distribution function fitted to data.

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0

50

100

150

200

250

300
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Figure 6 ‐ Normal probability plot typical of degradation score data. Normality is indicated by linearity.

0.17 0.18 0.19 0.2 0.21 0.22 0.23

0.001

0.003

0.01

0.02

0.05

0.10

0.25

0.50

0.75

0.90

0.95

0.98

0.99

0.997

0.999

Data

Probability

Normal Probability Plot
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3.1.2. Hypothesis Testing

It is important to keep in mind that all values of the degradation scores reflect the relative observed degradation of a

comparison image with respect to an original source image.  In all cases the source image is not compressed.  The

comparison image may or may not have undergone compression processing, and may be a match or non‐match

fingerprint.  

Specifically, the comparison image may be a compressed version of the source image (same person/same

impression), a compressed version of a match image (same person, same finger, but impression taken at different

time), or a compressed image of a non‐match (impostor) fingerprint (different person, any finger pairing).    For

experimental control, every source image in all scenarios includes a comparison of the non‐compressed source image

with itself.

Thus, every comparison includes a non‐compressed source image compared with another image at each of the 14

compression levels from Table 4 including the non‐compressed/control image, also referred to as the 1:1 ratio.

Accordingly, the data supports hypothesis testing of the degradation contrast between non‐compressed images and

paired comparison images having compression ratios from 1:1 to 38:1 as described previously.  Contrast between non‐

compressed, identical, image pairs serves as a control and as the baseline for evaluation of degradation observed at

other compression ratios, as well as an important indicator of the inherent quality of that particular fingerprint

impression.

Hence, the control data enables testing the hypothesis that degradation at each of the compression levels is equal to

that observed among comparisons of non‐compressed images. Inasmuch as each set of 200 rating scores from Table

2 yields only a single value of the degradation score, Q , we use a bootstrap procedure as described in the next

section to estimate the values of the test statistic (the mean of Q for bootstrap replicates) and uncertainty (the

standard error for bootstrap replicates) to be used for hypothesis testing.  The difference between the values of the

test statistic for any two compression levels may be tested in the manner applied to comparing different algorithms

as described by Wu, et al. [WU1].

For each of the 20 matching scenarios from Table 2, m = 1...20, we let Q1,m and Qn,m denote the score, Q , as defined

above for the control case (1:1) and compression case respectively, where n = 2...14 compression ratios from Table 4.

The null and alternative hypotheses, then, are ,

0 1, ,

,

1, ,

:

:

n m

m nm

n m

a m nm

H Q Q

H Q Q





Assuming normality of these statistics, the Z test statistic is

where 1,

ˆQ m and , ˆQn m are estimators (means) of the scores, 1,ˆ ( ) m SE Q and , ˆ ( ) n m SE Q are the standard errors of the two

estimators of the Q scores, and 1, : , mnm r is the correlation coefficient between Q1, , m k and Qnmk , , , where k = 1...K

bootstrap replicates.

3.1.3. Bootstrap Procedure

Given 200 observations (normalized ratings) for each of the 20 matching scenarios (see Table 2) at each of the 14

compression levels (Table 4), the values of the test statistic, 1,

ˆQ m and , ˆQn m , the standard errors, 1,ˆ ( ) m SE Q and , ˆ ( ) n m SE Q ,

and corresponding 1, : , mnm r values are computed using a non‐parametric two‐sample bootstrap procedure [WU2].

The bootstrap procedure consists of K=2000 executions of the procedure involving equations (1) and (2) above, each

run using a uniform random sample of 200 cases drawn, with replacement, from the original 200 observations.  The

1, ,

, 2 2

1, , 1, : , 1, ,

ˆ ˆ

ˆ ˆ ˆˆ ( ) ( )2 ( ) ( )

m nm

n m

m nm mnm m nm

Q Q Z

SE Q SE Q r SE Q SE Q

 

 

(3)
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result of the bootstrap procedure is a matrix of score values, Qk n, , k=1...2000 bootstrap replicates and n=1...14 for the

14 compression levels.   Such a matrix was computed for each of the 20 fingerprint type matching conditions from

Table 2.  For each fingerprint type and compression level, statistics are computed including the mean score value and

standard error of the mean.   Moreover, for each scenario the correlation is computed between the first column of

the matrix of values and each of the other columns.    Following the procedure described above, Z‐scores were

computed and are shown in Table 8.  These results together with descriptive statistics supporting this analysis appear

in Appendix E.

Table 8‐ Z‐Scores

1:1 2:1 5:1 7:1 10:1 12:1 15:1 17:1 20:1 22:1 26:1 30:1 34:1 38:1

Ink Card Scan

Rolled to Rolled, Match Pair ‐‐‐  0.44 ‐2.07 ‐4.56 ‐7.52 ‐9.46 ‐9.61 ‐11.94 ‐12.93 ‐15.38 ‐14.85 ‐15.92 ‐18.60 ‐17.02

Rolled to Rolled, Non‐Match Pair ‐‐‐  0.16  ‐0.40 ‐1.12 ‐2.97 ‐1.85 ‐3.57 ‐4.46 ‐5.36 ‐6.96  ‐7.34  ‐6.63  ‐8.93 ‐8.43

Rolled to Flat, Match Pair ‐‐‐  0.80 ‐0.07 ‐1.59 ‐3.11 ‐2.63 ‐3.75 ‐4.96 ‐4.48 ‐5.19 ‐6.06 ‐6.74 ‐7.58 ‐8.46

Rolled to Flat, Non‐Match Pair ‐‐‐  ‐0.62  ‐1.23 ‐1.29 ‐2.22 ‐1.79 ‐3.50 ‐4.07 ‐3.16 ‐5.15  ‐6.04  ‐6.61  ‐8.79 ‐10.60

Flat to Rolled, Match Pair ‐‐‐  ‐2.14 ‐1.69 ‐1.59 ‐3.27 ‐2.39 ‐3.25 ‐4.12 ‐5.36 ‐5.60 ‐5.58 ‐6.95 ‐8.44 ‐9.39

Flat to Rolled, Non‐Match Pair ‐‐‐  ‐1.68  ‐1.89 ‐2.94 ‐3.17 ‐2.80 ‐6.02 ‐4.86 ‐5.18 ‐5.80  ‐5.94  ‐7.59  ‐8.33 ‐8.42

Flat to Flat, Match Pair ‐‐‐  0.75 2.53 ‐0.79 ‐2.91 ‐5.32 ‐6.28 ‐7.91 ‐8.36 ‐11.56 ‐12.04 ‐10.33 ‐12.58 ‐13.34

Flat to Flat, Non‐Match Pair ‐‐‐  1.39  ‐0.98 ‐1.04 ‐0.85 ‐2.07 ‐2.05 ‐3.86 ‐4.61 ‐4.48  ‐4.26  ‐5.51  ‐9.36 ‐8.28

Slap to Slap, Match Pair ‐‐‐  ‐0.02 ‐0.32 ‐1.35 ‐2.70 ‐5.13 ‐6.28 ‐7.54 ‐10.10 ‐9.56 ‐13.34 ‐13.37 ‐13.20 ‐13.78

Slap to Slap, Non‐Match Pair ‐‐‐  0.17 0.58 ‐1.11 ‐1.00 ‐0.21 ‐0.57 ‐0.39 ‐2.42 ‐0.73  ‐4.24  ‐2.95  ‐2.96 ‐5.25

Digital Live Capture

Rolled to Rolled, Match Pair ‐‐‐  0.98 0.11 ‐1.74 ‐3.45 ‐7.60 ‐8.34 ‐11.99 ‐12.58 ‐15.29 ‐16.56 ‐17.22 ‐17.90 ‐18.01

Rolled to Rolled, Non‐Match Pair ‐‐‐  ‐0.35  ‐0.87 ‐1.89 ‐3.97 ‐4.91 ‐5.75 ‐6.41 ‐8.50 ‐8.69  ‐9.25  ‐9.02  ‐10.16 ‐10.30

Rolled to Flat, Match Pair ‐‐‐  0.97 ‐0.12 1.26 0.37 0.13 ‐0.85 ‐1.50 ‐0.43 ‐0.95 ‐1.06 ‐2.14 ‐1.46 ‐1.81

Rolled to Flat, Non‐Match Pair ‐‐‐  0.96  ‐0.25 ‐0.02 0.84 0.15 ‐0.41 ‐0.54 ‐1.37 ‐1.79  ‐1.23  ‐1.97  ‐1.91 ‐1.98

Flat to Rolled, Match Pair ‐‐‐  ‐0.17 ‐1.23 ‐0.79 ‐1.66 ‐0.35 ‐1.36 ‐1.85 ‐1.24 ‐1.43 ‐2.08 ‐2.84 ‐0.67 ‐2.24

Flat to Rolled, Non‐Match Pair ‐‐‐  1.30  ‐0.43 0.06 ‐0.89 ‐0.48 0.98 ‐0.77 ‐0.64 0.43  ‐1.23 0.32  ‐1.00 ‐2.39

Flat to Flat, Match Pair ‐‐‐  ‐0.37 ‐1.04 ‐1.18 ‐2.31 ‐2.33 ‐3.89 ‐5.40 ‐5.89 ‐4.69 ‐6.70 ‐6.99 ‐7.55 ‐6.32

Flat to Flat, Non‐Match Pair ‐‐‐  ‐0.34 0.63 ‐0.48 ‐1.30 ‐2.93 ‐2.13 ‐3.17 ‐2.87 ‐3.28  ‐4.60  ‐4.78  ‐5.41 ‐5.12

Slap to Slap, Match Pair ‐‐‐  1.03 0.66 ‐0.98 ‐3.67 ‐6.77 ‐10.19 ‐12.89 ‐16.80 ‐17.16 ‐19.49 ‐20.24 ‐21.20 ‐23.58

Slap to Slap, Non‐Match Pair ‐‐‐  0.37 0.50 ‐0.23 ‐3.41 ‐4.71 ‐9.28 ‐9.76 ‐12.10 ‐11.89  ‐12.96  ‐14.12  ‐16.85 ‐16.90

The Z‐scores correspond to standard deviation units of the standard normal distribution which has a mean of zero and

a standard deviation of one.  As one may recall from basic statistics, standard deviation units mark off areas of the

standard normal distribution that correspond to probabilities.  Hence, 95 % of the distribution lies between Z‐scores

±1.96. In our two‐sample Z‐test, we apply the Z‐test to assess the difference between 1,

ˆQ m and , ˆQn m . A Z‐score,

computed using equation (3),  greater than +1.96 or less than ‐1.96 would indicate less than a 5 % chance (p<0.05) that

two samples drawn from the same population could differ by the observed amount. We have set the probability

threshold, or α level, beyond which we will reject the null hypothesis at α=0.05. That is, we reject the null hypothesis if

p<0.05 and accept the alternative hypothesis that the two values of the comparison statistic are significantly different

and, by implication, that the algorithms under examination, i.e. the two compression rates, produce different results.  

Table 9 shows probability values corresponding to the Z‐scores of Table 8. Note that inasmuch as we allow that the

difference between the two statistic estimators may be either positive or negative, we distribute the 5 % rejection

region between the two tails of the distribution, hence the hypothesis test is a “two‐tailed” test.
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3.1.4. Two Tailed Test

Table 9 displays the results of comparisons of the test statistic (p‐values) among all match scenarios and compression

levels for the ink card scan images and digital live capture fingerprint images. As indicated above, each of the

comparisons tests significance of the contrast between the degradation score Q for the control case of pairs of non‐

compressed images and those for which one of the images has been compressed at one of the ratios exclusive of the

1:1. Thus, probability values less than the α=0.05 level indicate significant differences between perceived image

degradation relative to that for non‐compressed images. Inspection of Table 9 indicates that for most of the

scenarios, the significant degradation beyond that of non‐compressed imagery first occurs at 10:1 compression.  

Table 9 ‐ P‐Values for 2‐Tailed Tests

    1:1 2:1 5:1 7:1 10:1 12:1 15:1 17:1 20:1 22:1 26:1 30:1 34:1 38:1 Ink Card Scan

Rolled to Rolled, Match Pair ‐‐‐  0.657 0.039 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Rolled, Non‐Match Pair ‐‐‐  0.869 0.689 0.262 0.003 0.064 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Flat, Match Pair ‐‐‐  0.424 0.942 0.113 0.002 0.009 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Flat, Non‐Match Pair ‐‐‐  0.534 0.218 0.197 0.027 0.073 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000

Flat to Rolled, Match Pair ‐‐‐  0.032 0.092 0.111 0.001 0.017 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Flat to Rolled, Non‐Match Pair ‐‐‐  0.092 0.059 0.003 0.001 0.005 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Flat to Flat, Match Pair ‐‐‐  0.451 0.011 0.427 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Flat to Flat, Non‐Match Pair ‐‐‐  0.163 0.325 0.297 0.396 0.038 0.041 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Slap to Slap, Match Pair ‐‐‐  0.980 0.748 0.177 0.007 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Slap to Slap, Non‐Match Pair ‐‐‐  0.862 0.560 0.266 0.317 0.837 0.567 0.698 0.016 0.467 0.000 0.003 0.003 0.000

Digital Live Capture

Rolled to Rolled, Match Pair ‐‐‐  0.326 0.915 0.082 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Rolled, Non‐Match Pair ‐‐‐  0.725 0.383 0.059 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Flat, Match Pair ‐‐‐  0.330 0.903 0.206 0.713 0.895 0.393 0.134 0.666 0.343 0.291 0.032 0.145 0.070

Rolled to Flat, Non‐Match Pair ‐‐‐  0.336 0.802 0.984 0.403 0.879 0.682 0.588 0.170 0.074 0.217 0.049 0.056 0.047

Flat to Rolled, Match Pair ‐‐‐  0.864 0.218 0.430 0.098 0.724 0.174 0.064 0.214 0.154 0.037 0.004 0.504 0.025

Flat to Rolled, Non‐Match Pair ‐‐‐  0.192 0.666 0.956 0.372 0.630 0.328 0.444 0.523 0.667 0.218 0.750 0.315 0.017

Flat to Flat, Match Pair ‐‐‐  0.710 0.298 0.240 0.021 0.020 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Flat to Flat, Non‐Match Pair ‐‐‐  0.738 0.530 0.628 0.195 0.003 0.034 0.002 0.004 0.001 0.000 0.000 0.000 0.000

Slap to Slap, Match Pair ‐‐‐  0.304 0.510 0.327 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Slap to Slap, Non‐Match Pair ‐‐‐  0.714 0.616 0.815 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

   
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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Earlier in this section, Figure 4 provided anecdotal evidence of a monotonically increasing number of observed

compression degradation as the level of compression increases.    The Z‐scores presented in Table 8 reinforces this

progressive increase of observed compression anomalies as the greater compression ratios are utilized on images.    

Yet, it has been suggested that for inked prints, a small degree of compression can improve image quality by filtering

the image of some noise.  Several plots such as Figure 7 below (inked roll‐to‐roll match case) appear to reinforce this

notion, e.g. by virtue of the slight dip in perceived degradation from 1:1 to 2:1 compression ratios. However, these data

generally do not provide evidence of a statistically significant improvement in image quality at very low compression

ratios (such as 2:1) over the non‐compressed image.   (The exception is the inked, flat‐to‐flat, match pair case that

shows a positive z‐score at 2:1, increasing to become significant at 5:1 with p<0.05.)

Figure 7 ‐ Mean compression anomaly measure with 95 % confidence intervals for inked, rolled‐to‐rolled, fingerprints

(match pairs) for compression ratios from 1:1 to 38:1.

0 5 10 15 20 25 30 35 40 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Mean Compression Anomaly Metric and 95% CI

 Roll to Roll ID

Compression Ratio (x:1)

Q
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

PAGE 36 OF 72

3.2. Identification Errors

In addition to studying the effects of compression and the resulting image anomalies, this experiment also studied

the effects of compression on the ability of the examiner to make an identification decision.   Investigators expected

that even at the most extreme level of JPEG 2000 compression used in the present study, the rate of misidentification

would be low among the experienced examiners. However, the task was included in the study to capture possible

increase in some nominal misclassification rate with increased compression.

As with the analysis on observed compression anomalies, normalization was performed on the individual decisions to

allow for them to be analyzed as a singular unit for each image pair.  Prior to normalizing decision triplets into a single

decision value, the individual decisions were assigned a code.   In the case of identity determination, the codes are

provided in Table 10 below.

Table 10 ‐ Observation Codes for Determination of Identity

Observation Code Description

Y A correct determination of identity was made.

N A correct determination of non‐identity was made.

U A determination of identity was not made given the image pair.

For each pair of images examined, a match decision was provided by 3 different examiners.  The decision triplets were

then categorized into a singular code and each was assigned a normalized code.  For example, decision “YYN”, “NYY”

and “YNY” were all treated as and a single decision of “YYN”, and then assigned a normalized code of “0” (from

Table 11)   Categorization of identity determination triplets are provided in Table 11 below.

Table 11 ‐ Normalization Table for Identification Decision

Responses from

Observers

Normalized

Code

Justification

Y,Y,Y

Y,Y,N

Y,Y,U

0 These cases represent a majority‐identification decision by the

examiners.

N,N,N

N,N,Y

N,N,U

1 These cases represent a majority‐non‐identification decision by the

examiners.

N,U,Y

U,U,Y

N,U,U

U,U,U

2 These cases represent a majority‐inconclusive decision by the

examiners.  Split decision cases (N, U, Y) are also considered

inconclusive in this study.

3.2.1. Analysis of Identification Error Rates

In order to examine the extent of misclassification, we take advantage of the fact that for each of the matching

conditions, e.g. slap‐to‐slap, flat‐to‐roll, etc. we have equal numbers of identification and non‐identification pairings.

Thus, we are able to examine error rates in a conventional way, i.e. considering overall error to include cases in which

known identification cases are classified as non‐identification, false negatives (type  II error), and the cases in which

known non‐identifications are classified as identifications, false positives (type    I error). In order to deal with the

classification as a binary decision, we must include the inconclusive decisions with one or the other category.
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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In conventional fingerprint examination, the conservative approach is to consider an inconclusive determination as a

“correct” response. That is, in the absence of a high degree of certainty over the identification, the preferable

approach is to consider an inconclusive determination to be the correct response. However, for purposes of the

present analysis, we are also interested in any possible increase in the number of inconclusive determinations with

increasing compression level.  Accordingly, in the analysis to follow, we treat the inconclusive classifications as correct

responses. Then, in a separate analysis we examine the frequency of inconclusive responses among identification and

non‐identification pairings over compression level.

Given frequency distributions of identification determinations for each of the 10 fingerprint matching cases6

, j, and 14

compression ratios, k, we can define the error rate as

where

and

The error rates are determined by counting the numbers of erroneous classifications for the 200 match and non‐

match pairs of each fingerprint type. The confidence intervals for the error rates are estimated using two approaches

described in the following.

Where the number of observed errors is less than or equal to 3 out of the total 200 trials for both match or non‐match

cases, we use a 95 % confidence interval    derived from properties of the binomial distribution rather than from

quantiles of the error estimate distribution.    The rationale for and derivation of this approach is elaborated in

references such as [EYPASCH, HANLEY, and JOVANOVIV]. The so‐called “Rule of Three” derives from the notion that

in experiments involving binomial trials, the observation of zero errors (or adverse effects) does not imply that the

actual failure rate is zero. To accept such an estimate of the failure rate would violate standards of conservatism in

such studies typical of medical research. Given zero errors (or failures) in n observations, we can be 95 % confident

that the actual error rate will lie between 0 and 3/n. In our case, this upper limit corresponds to 3/200 = 0.015 or 1.5 %.

For cases in which the average number of observed errors is less than or equal to three, but greater than zero, we

extend the rule of three such that confidence intervals become [0, 4/n], [0, 6/n], and [0, 7/n] for average7 error

frequencies of 1, 2, and 3, respectively, to augment the case [0, 3/n] for observed error = 0, giving us four “Extended

Rule of Three” confidence intervals.  

For cases in which the error frequency of either false negatives (fn) or false positives (fp) are greater than 3, we use

the error rate estimate computed according to equations (5) or (6) and apply a bootstrap procedure to estimate the

limits of the confidence interval that contains this error estimate. The bootstrap procedure samples with replacement

from the 200 match and/or non‐match pair cases, tabulates the false negatives and/or false positives and computes a

                                                                    6 We have combined the identification and non‐identification scenarios for the error analysis, hence 20 mentioned earlier reduces

to 10.

7 Here we average the frequencies of false negatives and false positives, 2

fn fp  , and round the result to the nearest integer.

2

jk jk

jk

fpr fnr E  (4)

false positives fpr false positives true negatives   (5)

false negatives fnr false negatives true positives   (6)
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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bootstrap replicate of the error estimate.   Each iteration of the bootstrap combines the observed error frequency

found to be less than or equal to three with a new count of false negatives or false positives from the sample. The

bootstrap procedure [WU2] is applied only when number of errors exceeds 3 in 200 for false positives, false negatives,

or both. For example, if fn  3 and fp  3 , fnr computed via equation (4) is held constant while a new value of fpr is

calculated for each iteration of the bootstrap, i.e., for each sample of non‐match pair cases. A composite error

replicate is computed according to equation (4). If fn  3and fp  3 , we hold the fp constant and apply the

bootstrap sampling to the match pair cases. If fn  3 and fp  3 we apply the bootstrap to both. In all cases, we

generate a distribution of 2000 replicates of the error estimate.  Quantiles 0.025 and 0.975 of this distribution mark

the lower and upper limits of the 95 % confidence interval, i.e. for a two‐tailed significance test with  0.05 .

In spite of the rather elaborate analysis protocol developed to examine identification errors, we observed only a very

small number of misidentifications.    In the graphs below we plot for each fingerprint comparison modality the

match/non‐match pair error estimate with 95 % confidence intervals for each of the 14 compression ratios considered

in the present study. In all cases error estimates were found to fall within the region covered by the “Extended Rule

of Three” as described above. Error was either zero, with confidence interval [0, 3/n], or one, with confidence interval

[0, 4/n]. The only scenarios for which any error is observed involved rolled‐to‐flat or flat‐to‐rolled comparisons as

shown in Figures 8 – 10 below. In each case the error estimate is plotted with confidence limits for each of the

compression ratios. No clear trend is seen over increasing compression, and the few errors include only false

negatives, i.e., cases in which identification was misclassified as non‐identification. Inspection of the fingerprint pairs

involved in the misclassification generally revealed ambiguity over the region of meaningful overlap between the

rolled print and the corresponding flat impression. Moreover, the image manipulation “toolkit” provided to the

examiners did not facilitate overlays or include other more sophisticated tools that might assist in comparing

fingerprint features.

Figure 8 ‐ Identification error rates for ink card scan rolled to ink card scan flat fingerprint comparisons at the 14

compression levels with confidence limits.

0 5 10 15 20 25 30 35 40

-0.01

0

0.01

0.02

0.03

0.04

0.05

Error Rates and 95% CI

 Inked Roll to Flat

Compression Ratio (x:1)

Error Rate
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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Figure 10 ‐ Identification error rates for ink card scan flat to ink card scan rolled fingerprint comparisons at the 14

compression levels with confidence limits.

0 5 10 15 20 25 30 35 40

-0.01

0

0.01

0.02

0.03

0.04

0.05

Error Rates and 95% CI

 Inked Flat to Roll

Compression Ratio (x:1)

Error Rate

0 5 10 15 20 25 30 35 40

-0.01

0

0.01

0.02

0.03

0.04

0.05

Error Rates and 95% CI

 Live Roll to Live Flat

Compression Ratio (x:1)

Error Rate

Figure 9 ‐ Identification error rates for digital live capture rolled to digital live capture flat fingerprint comparisons at the

14 compression levels with confidence limits.
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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Thus, for all 10 fingerprint matching conditions and 14 compression levels from lossless to 38:1 we see mainly no error

in making the identification/non‐identification classification. Where error is observed, it does not exceed one

comparison out of 200. In all cases, we have used the Rule of Three, and extensions thereof, to present the most

conservative estimates of the 95 % confidence intervals for low probability events.   Also, examination of Figure 8

through Figure 11 provides some anecdotal evidence that the identification error rates for ink card scan images may

exhibit more variance than the digital live scan images at lower compression ratios.

   

0 5 10 15 20 25 30 35 40

-0.01

0

0.01

0.02

0.03

0.04

0.05

Error Rates and 95% CI

 Live Flat to Live Roll

Compression Ratio (x:1)

Error Rate

Figure 11 ‐ Identification error rates for digital live capture flat to digital live capture rolled

fingerprint comparisons at the 14 compression levels with confidence limits.
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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3.2.2. Analysis of Inconclusive Cases

As to the possibility that compression might inhibit the ability of the examiners to make an identification decision, we

examine the frequency of inconclusive determinations over compression levels for each of the fingerprint comparison

modes. For this analysis we compare for each of the cases in Table 2 the relative probabilities of an inconclusive

determination over the 14 compression levels from Table 4.  In Figure 12 below, we observe that while there is some

variability in the probability of an inconclusive decision on the part of examiners, particularly for the inked prints and

comparison of rolled to flat impressions, there is no clear trend toward an increase in the probability of an

inconclusive determination with increasing compression level.

Figure 12 ‐ Probability of inconclusive determination vs. compression ratio for 10 comparison scenarios and 14

compression ratios.

   

0 5 10 15 20 25 30 35 40

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

Compression Ratio (x:1)

P(inconclusive)

Estimated Probability of Inconclusive Decision in 

 Ident Classification

Ink Rolled to Ink Rolled

Ink Rolled to Ink Flat

Ink flat to Ink Rolled

Ink Flat to Ink Flat

Ink Slap to Ink Slap

Livescan Rolled to Livescan Rolled

Livescan Rolled to Livescan Flat

Livescan Flat to Livescan Rolled

Livescan Flat to Livescan Flat

Livescan Slap to Livescan Slap
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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Summarizing the probability data by averaging over all comparison scenarios shows a slight increasing trend with

increasing compression ratio as shown in Figure 13. This increase, however, is less than the variation among the

compression levels. Moreover, inspection of Figure 12 shows that the incidence of the inconclusive determination is

more likely with comparisons of inked rolled to inked flat prints over all compression levels.  Inspection of images for

a number of the inconclusive pairings suggests that the inability to make the definite identification decision may have

more to do with ambiguity over the region of overlap of the flat print with the rolled rather than with compression

effects.

0 5 10 15 20 25 30 35 40

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

Compression Ratio (x:1)

Mean P(inconclusive)

Mean Estimated Probability of Inconclusive Decision in 

 Identification Task

Figure 13 ‐ Summaries over all comparison scenarios showing least squares fit to the mean probability of inconclusive

determination. Slight increasing trend is noted with increasing compression.
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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4. Results

4.1. Investigative Goal 1: Validate 15:1 target compression ratio

The first investigative goal of this study is to validate the current 15:1 target for effective compression ratio as

suggested in the informative guidance for 1000 ppi [MTR1] using the legacy methodology that formed the 500 ppi

guidance [FITZPATRICK].  The IAI results (see Appendix A) utilized an error count threshold that was judged to yield

an acceptable amount of detail loss using lossy WSQ compression for 500 ppi ink‐captured rolled fingerprint imagery.  

Based on the IAI study, 7 of 202 image pairs compared (3.4 % of the image pairs) at 15:1 exhibiting non‐Galton‐level

detail loss and zero image pairs exhibiting Galton‐level detail loss constituted the acceptable level of degradation in

establishing 15:1 compression for WSQ.

4.1.1. Investigative Analysis 1

The experimental case from the present study that maps to the IAI experimental scenario is the Ink‐Rolled‐to‐Ink‐

Rolled‐Match‐Pair case (Table 2  ‐  Case 1 and Figure 14 below), as the IAI study focused only on rolled fingerprint

impressions.   In this case, the present study notes 8 observations of non‐Galton‐level detail loss (level‐3 feature loss)

or 4 % at the 15:1 compression ratio.   This falls slightly outside of the 3.4 % guidance used to establish 15:1.   If similar

guidance were to be used, a ratio of 10:1 would be the highest acceptable compression ratio to meet the IAI criterion

for the rate of non‐Galton‐level detail loss.

Figure 14 ‐ Observed Compression Anomalies for Case‐1 (Ink Rolled to Ink Rolled)

4.1.2. Investigative Result 1

The results of the study show that 15:1 compression of rolled‐to‐rolled ink card scan imagery at 1000 ppi using the

current informative guidance [MTR1] falls just slightly outside of the same criteria used to establish this compression

guidance at 500 ppi.   While 15:1 compression of 1000 ppi imagery using the current informative guidance is a viable

compression ratio and does not result in significant errors relative to lower compression ratios (see section 0), a ratio

of 10:1 would meet the legacy IAI requirement while at the same time not resulting in any significant change in

identification error rates as a result of compression.   

   

1:1 2:1 5:1 7:1 10:

1

12:

1

15:

1

17:

1

20:

1

22:

1

26:

1

30:

1

34:

1

38:

1

Level 2 and 3 detail degradation 00000000001003

Level 3 detail degradation 2 0 4 4 6 10 8 16 11 23 28 29 32 36

Some benign degradation 33 34 45 67 92 107 112 125 133 131 126 133 141 134

No visible degradation 165 166 151 129 102 83 80 59 56 46 45 38 27 27

IAI Cutoff (3.4%, adjusted) 88888888888888

0

20

40

60

80

100

120

140

160

180

200

Observation Count

Compression Anomalies (Ink Rolled to Ink Rolled, Mated Pair)
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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4.2. Investigative Goal 2: Examine image degradation relative to compression ratio

The second investigative goal of this study was to assess if higher compression ratios result in increased perceived

image degradation, and note any patterns in degradation relative to compression.   

4.2.1. Investigative Analysis 2

Examination of computed Z‐scores (see Table 8) yields trends that indicate increased perceived image degradation

with progressively higher compression rates relative to the mean at 1:1 (control case/no compression).   Such negative

sloping trends can be observed in the Z‐score linear trend graphs for both ink card scan (Figure 15) and digital live

capture (Figure 16) cases.   All cases trend negatively up to the maximum compression ratio examined in this study

(38:1).   The actual data from which these linear trends were generated from can be found in Figure 18 and Figure 19.

Figure 15 ‐ Z‐Score Trends (linear) for Ink Card Scan

Figure 16 ‐ Z‐Scores Trends (linear) for Digital Live Capture

‐25.00

‐20.00

‐15.00

‐10.00

‐5.00

0.00

5.00

1:12:15:17:1

10:112:115:117:120:122:126:130:134:138:1

Z‐Scores

Compression Ratio

Z‐Value Trends (Ink Card Scan)

Linear (Rolled to Rolled, Mated Pair)

Linear (Rolled to Rolled, Non‐Mated Pair)

Linear (Rolled to Flat, Mated Pair)

Linear (Rolled to Flat, Non‐Mated Pair)

Linear (Flat to Rolled, Mated Pair)

Linear (Flat to Rolled, Non‐Mated Pair)

Linear (Flat to Flat, Mated Pair)

Linear (Flat to Flat, Non‐Mated Pair)

Linear (Slap to Slap, Mated Pair)

Linear (Slap to Slap, Non‐Mated Pair)

‐25.00

‐20.00

‐15.00

‐10.00

‐5.00

0.00

5.00

1:12:15:17:1

10:112:115:117:120:122:126:130:134:138:1

Z‐Scores

Compression Ratio

Z‐Value Trends (Digital Live Scan)

Linear (Rolled to Rolled, Mated Pair)

Linear (Rolled to Rolled, Non‐Mated Pair)

Linear (Rolled to Flat, Mated Pair)

Linear (Rolled to Flat, Non‐Mated Pair)

Linear (Flat to Rolled, Mated Pair)

Linear (Flat to Rolled, Non‐Mated Pair)

Linear (Flat to Flat, Mated Pair)

Linear (Flat to Flat, Non‐Mated Pair)

Linear (Slap to Slap, Mated Pair)

Linear (Slap to Slap, Non‐Mated Pair)
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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4.2.2. Investigative Result 2

Examination of standard scores (Z‐scores) relative to the mean demonstrates that perceived image quality trends

negatively with increased compression across all image types as shown by Figure 15 and Figure 16 demonstrating that

increasing compression rates yields images that are perceived as having progressively greater amounts of image

degradation relative to the non‐compressed image as judged by human examiners.   Examination of the degradation

trends also show that certain impression types are impacted more than others.   For example, the ink card scan rolled‐

to‐rolled‐match‐pair case degrades more with increasing compression than any of the other ink card scan impression

types.   The same trending can be seen with digital live scan slap‐to‐slap‐match‐pair relative to other digital live scan

impression types.

4.3. Investigative Goal 3: Assess impact of compression on identification error rates

The third investigative goal of this study was to assess if higher compression ratios result in increased identification

error rates for fingerprint examiners, and note any patterns in the error rates.   

4.3.1. Investigative Analysis 3

Analysis of observation data shows that identification errors for the experimental data occurred at very low

frequency and only for a very small subset of image impression types.   Specifically, only rolled‐to‐flat (or vice versa)

image comparisons for both ink card scan and digital live capture images exhibited any errors in observation.   

Analysis of the observed error rates in section 3.2.1 showed that no case posed any statistically significant source of

error at any compression ratio, up to and including the highest compression ratio in this study which was 38:1.   

This reinforces the adage of “driving a car with a dirty windshield” where the operator of the vehicle may observe and

identify severe anomalies in the field of view, but these anomalies still will not impact the operator’s ability to control

the vehicle and navigate the road.

It is also theorized that the features needed for most identification cases reside in the lower end of the frequency

spectrum rendering such core features highly compressible, while non‐Galton features may be occupying higher

frequency bands and contributing the most to image entropy rendering them more costly in terms of compression.   

For the purposes of discussion,  Figure 17 below shows examples of images compressed at up to 800:1 which shows

that the ridge structure of the example set remains mostly intact even at 400:1 compression.

Lossless 100:1 200:1 400:1 800:1

Figure 17 ‐ Comparison of Very High Compression Rates
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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4.3.2. Investigative Result 3

Analysis of the observed error rates in section 3.2 demonstrates that while expert fingerprint examiners can identify

image degradation and feature loss at relatively low compression rates, their ability to make an identification does not

appear to be measurably impacted.  This seemed to hold true even up to the maximum compression level examined in

this study (38:1) where the rates of identification errors for each experimental case were not statistically significant

when compared to the 1:1 control pairing (see section 0).  However, given the opinion of the examiners that images

with increasingly greater compression exhibited sufficient degradation to the extent that it may impact identification,

it is plausible to conclude that systematic use of images with very high compression is inadvisable even if a statistically

significant impact to identification rates is not observed with the limited data sample set utilized in this study.   An

analogy that describes this can be observing for cracks in a structural component of a bridge under increasing load

where the cracks represent image feature degradation and increasing load represents increasing compression.   As

load is increased, a greater number of cracks are being observed.   Observing an increasing number of cracks is a

negative indicator and a disincentive for using very high loads even if the bridge doesn’t fail under the given set of

test conditions.

The disincentive for utilizing very high compression may also lie in the examination of latent fingerprint matching

where features beyond traditional features may play a key role in establishing identity.  While very high compression

rates may yield images that can be utilized for establishing identity in 10‐print casework, their utilization in latent case

work may be greatly impacted.
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

PAGE 47 OF 72

4.4. Investigative Goal 4: Examine compression anomalies relative to impression type

The fourth investigative goal of this study was to determine if any particular fingerprint impression type is more

susceptible to compression related anomalies than other impression types at the various examined compression

ratios.   

4.4.1. Investigative Analysis 4

As noted in section 4.2, examination of Z‐scores for all cases shows that all cases trend negatively in terms of

perceived image quality with increasing compression rates.    Examination of the actual data plots shows some

stratification of the data that indicates that not all impression types are impacted equally.   This stratification can be

seen in Figure 18 and Figure 19 below.

Figure 18 ‐ Z‐Scores for Ink Card Scan Cases

‐20.00

‐15.00

‐10.00

‐5.00

0.00

5.00

1:12:15:17:1

10:112:115:117:120:122:126:130:134:138:1

Z‐Scores

Compression Ratio

Computed Z‐Scores by Ratio (Ink Card Scan)

Rolled to Rolled, Mated Pair Rolled to Rolled, Non‐Mated Pair Rolled to Flat, Mated Pair

Rolled to Flat, Non‐Mated Pair Flat to Rolled, Mated Pair Flat to Rolled, Non‐Mated Pair

Flat to Flat, Mated Pair Flat to Flat, Non‐Mated Pair Slap to Slap, Mated Pair

Slap to Slap, Non‐Mated Pair
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

PAGE 48 OF 72

Figure 19 ‐ Z‐Scores for Digital Live Capture Cases

4.4.2. Investigative Result 4

Some impression types are more susceptible to compression degradation such as the ink card scan rolled‐to‐rolled

case which exhibits a far more aggressive negative trend in Z‐scores than the ink card scan flat‐to‐flat case.    Not all of

these cases present statistically significant results at very low compression ratios (see Table 31 and Table 32 in

Appendix E) as compared to their non‐compressed counterparts, but cases such as the aforementioned ink card scan

rolled‐to‐rolled vs. ink card scan flat‐to‐flat case exhibit statistically significant results at compression levels as low as

5:1.  For example, the rolled‐to‐rolled ink card scan case shows significant degradation over the mean with a Z‐score of

‐2.068 at 5:1 while the flat‐to‐flat ink card scan case shows a significant improvement over mean with a Z‐score of

2.530 at 5:1, both at p<0.05 level.

As noted above in the case of flat‐to‐flat ink card scan at 5:1, an interesting finding in this study was that certain cases

and/or certain image types at various levels of lossy compression levels exhibit a positive trend in quality relative to

the non‐compressed case in terms of perceived image quality as noted by the examiners.    Based on anecdotal

‐25.00

‐20.00

‐15.00

‐10.00

‐5.00

0.00

5.00

1:12:15:17:1

10:112:115:117:120:122:126:130:134:138:1

Z‐Scores

Compression Ratio

Computed Z‐Scores by Ratio (Digital Live Scan)

Rolled to Rolled, Mated Pair Rolled to Rolled, Non‐Mated Pair Rolled to Flat, Mated Pair

Rolled to Flat, Non‐Mated Pair Flat to Rolled, Mated Pair Flat to Rolled, Non‐Mated Pair

Flat to Flat, Mated Pair Flat to Flat, Non‐Mated Pair Slap to Slap, Mated Pair

Slap to Slap, Non‐Mated Pair
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

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evidence as well as some empirical studies of image entropy, it is theorized that the perceived improvement may be

due to some low‐pass filtering/noise removal effect on the given image.  The Z‐scores do provide supporting evidence

that this is indeed the case.   Examination of the Z‐scores in Table 8 demonstrates that 13 of the 20 cases exhibit a

standard score above the mean (Z‐score >0), and of these 13 cases 12 of them do so at the ratio of 2:1 indicating that

the image has been perceived as having better quality than the mean compared to the 1:1 control case.  The evidence

for this is not general nor statistically significant for most cases except one case, ink card scan flat‐to‐flat match pair,

at the compression ratio of 5:1 (satisfying the condition of z>0, p<0.05).   
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5. Conclusions

This experiment was conducted with four primary goals:

1. Validate the 15:1 target compression ratio as defined in the current informative guidance for 1000 ppi using

the legacy methodology that formed the 500 ppi guidance.

2. Assess if higher compression ratios result in increased perceived image degradation.

3. Assess if higher compression ratios result in increased identification error rates for fingerprint examiners.

4. Assess if any particular fingerprint image type is more susceptible to compression related anomalies.

Based on the results, it can be concluded that the existing informative guidance [MTR] for 15:1, while viable for usage

in identification without resulting in any significant amount of error in identification decisions, may fall just outside of

the criteria used by the IAI in establishing the effective compression ratio for 500 ppi.  A lower effective compression

ratio can be more helpful in the retention of non‐Galton level details which may play an important role for more

difficult operational scenarios such as latent fingerprint images.

The study also showed that perceived image quality does trend negatively with increased compression across all

impression type comparisons.   While this effect varies according to the type of image pairing being examined, the

trends were all consistently negative with increasing compression.

    

Finally, the study shows that compression levels up to the maximum compression ratio of 38:1 examined in this study

did not result in any significant impact on the ability of the examiners to make their identification decision thereby

reinforcing the anecdotal evidence that human examiners can mitigate quite a bit of image quality loss while still

successfully conducting the identification decision.

6. Future Work

The work laid out in this study is informative, but will serve as the basis for a normative guidance on compression for

1000 ppi fingerprint imagery.  This normative guidance will be published separately by NIST, and will also take into

account data from other related studies.  

Also, given that compression impacts some impression types more than others, it may be possible to adopt a

compression strategy that incorporates the image impression type into the decision making pathway and dynamically

adjust compression.
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References

Publications and Reports

CHAMBERS Chambers, John; William Cleveland, Beat Kleiner, and Paul Tukey (1983). Graphical Methods for Data

Analysis. Wadsworth

CIA1 “The World Factbook”, https://www.cia.gov/library/publications/the‐world‐factbook/geos/us.html, Retrieved 2010‐

12‐06.

DOJ "The Science of Fingerprints" [rev. December 1984], United States Department of Justice, Federal Bureau of

Investigation, USGPO, ISBN 0‐16‐050541‐0, Page 18.

EYPASCH Eypasch, Ernst; Rolf Lefering, C K Kum, Hans Troidl (1995‐09‐02). "Probability of adverse events that

have not yet occurred: a statistical reminder". BMJ 311 (7005): 619‐620. PMID 7663258. PMC 2550668.

http://www.bmj.com/cgi/content/full/311/7005/619. Retrieved 2008‐04‐15.

FITZPATRICK Fitzpatrick, M. et al. 1994, “WSQ Compression / Decompression Algorithm Test Report”, IAI Annual

Conference.

GALTON Galton, F. (2005). Finger prints. Mineola, NY: Dover Publications. (Original work published 1892)

HANLEY Hanley, JA; Lippman‐Hand A (1983). "If nothing goes wrong, is everything alright?". JAMA 249 (13):

1743‐5. PMID 6827763.

JAIN Jain, A., “Pores and Ridges: High‐Resolution Fingerprint Matching Using Level 3 Features”,  IEEE

Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 1, January 2007.

JOVANOVIV Jovanoviv, B. D., and P. S. Levy. A look at the Rule of Three. The American Statistician, Vol. 51, No. 2,

May 1997, pp. 137‐139.

LIBERT “A 1D Spectral Image Validation/Verification Metric for Fingerprints”.  Libert, J.M.; Grantham, J.; Orandi,

S.  August 19, 2009. http://www.nist.gov/customcf/get_pdf.cfm?pub_id=903078.  Retrieved 2011‐01‐12.

LIKERT Likert, R. (1932). A Technique for the Measurement of Attitudes, Archives of Psychology 140, 55.

MTR1 "Profile for 1000 ppi Fingerprint Compression".  Lepley, M.A.  

http://www.mitre.org/work/tech_papers/tech_papers_04/lepley_fingerprint/lepley_fingerprint.pdf.  Retrieved

2011‐01‐11.

NIST1 National Institute of Standards and Technology. Summary of NIST Patriot Act Recommendations.  Gaithersburg, MD.  

Retrieved January 4, 2007 from http://www.itl.nist.gov/iad/894.03/pact/NIST_PACT_REC.pdf

NIST2 "NIST Biometric Image Software". http://Fingerprint.nist.gov/NFIS/. Retrieved 2011‐01‐12.

OPENJPEG "OpenJPEG library : an open source JPEG 2000 codec".

http://www.openjpeg.org/index.php?menu=news. Retrieved 2011‐01‐12.

SD27 M.D. Garris & R.M. McCabe, "NIST Special Database 27: Fingerprint Minutiae from Latent and Matching

Tenprint Images," NIST Technical Report NISTIR 6534 & CD‐ROM, June 2000.

SHAPIRO Shapiro, S. S.; Wilk, M. B. (1965). "An analysis of variance test for normality (complete samples)".

Biometrika52 (3‐4): 591–611. doi:10.1093/biomet/52.3‐4.591. JSTOR 2333709MR205384.

WU1 Wu, Jin Chu, Alvin F. Martin, and Raghu N. Kacker.  Measures, uncertainties, and significance test in

operational ROC analysis,  Journal of Research of the National Institute of Standards and Technology,

116(1), 517‐537, (2011).

WU2 Wu, Jin Chu. Studies of Operational Measurement of ROC Curve on Large Fingerprint Data Sets Using

Two‐Sample Bootstrap. NISTIR 7449, U.S. Department of Commerce, National Institute of Standards

and Technology, September 2007, 25 pages.

WU3 Wu, Jin Chu. Operational Measures and Accuracies of ROC Curve on Large Fingerprint Data Sets. NISTIR

7495, U.S. Department of Commerce, National Institute of Standards and Technology, May 2008, 23

pages.
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Standards

AN27 NIST Special Publication 500‐271:  American National Standard for Information Systems — Data Format

for the Interchange of Fingerprint, Facial, & Other Biometric Information – Part 1. (ANSI/NIST ITL 1‐2007).  

Approved April 20, 2007.

JPEG "T.81 : Information technology – Digital compression and coding of continuous‐tone still images –

Requirements and guidelines". http://www.itu.int/rec/T‐REC‐T.81. Retrieved 2011‐01‐12.

JPEG2K "ISO/IEC 15444‐1:2004 ‐ Information technology ‐‐ JPEG 2000 image coding system: Core coding system".

http://www.iso.org/iso/iso_catalogue/catalogue_ics/catalogue_detail_ics.htm?csnumber=27687.

Retrieved 2009‐11‐01.

WSQ "WSQ Gray‐Scale Fingerprint Image Compression Specification" Version 3.1.

https://www.fbibiospecs.org/docs/WSQ_Gray‐scale_Specification_Version_3_1.pdf.  Retrieved 2010‐01‐11.
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Appendix A. IAI WSQ Compression / Decompression Study Summary

The study that became the normative basis for the 15:1 compression guidance currently in use is the IAI WSQ

Compression Decompression Algorithm Test Report [FITZPATRICK].    In the IAI study, 100 fingerprint cards were

selected from the Illinois State Police AFIS system.  Four fingerprints were identified from each card and scanned in as

500 ppi 8bit gray‐scale images.  Each one of the four fingerprints were randomly compressed at settings of 5:1, 10:1,

15:1, and 20:1 totaling 400 rolled fingerprints.   The selection process yielded approximately 100 fingerprints at each of

the compression settings.    The fingerprints were then decompressed and the resulting image paired (split‐screen

fashion) with the original and printed together on a single sheet with the left image consistently being the non‐

compressed image and the right image being the image that has passed through WSQ compression at a given ratio,

but the ratio was not identified on the printed image.    The selection process employed for the cards yielded a

candidate pool that was comprised of 86 % males, and 14 % females.  The pattern classes were balanced to reflect a

distribution of 65 % loops, 29 % whorls and 6 % arches.  The quality of the test fingerprints was identified as ranging

from poor to good but no specific metrics were specified.

Two experienced and competent latent print examiners were each provided with the full test set of 400 paired

[printed8

] images.    Each examiner independently compared the standard image (non‐compressed) with the

compressed/decompressed image to determine any differences between the standard image and the

compressed/decompressed Image. Each examiner recorded his findings on the evaluation form by marking one of the

three following evaluation codes for each test pairing:  

1. No noticeable reduction in image quality

2. Slight reduction in image quality which may interfere with an identification based on poroscopy, ridgeology,

or other non‐Galton details.

3. Noticeable reduction in image quality which may interfere with an identification based on the Galton details.

Only one evaluation code was assessed for each fingerprint evaluated. If a test pairing contained a combination of

Code 2 and Code 3 observations, Code 3 was used to evaluate the test pairing. When an evaluation Code 3 was

assigned to a test pairing, the examiner highlighted the minutiae area on the test pairing and recorded the

observation on the test evaluation form.   When an evaluation Code 2 was assigned to a test pairing, the specific

problem was recorded on the test evaluation form.  Upon completion of the test, each latent print examiner turned

over the completed test evaluation forms to the test coordinator. The test results were tabulated and a post‐test

review was conducted between the test participants and the test coordinator. Differences of opinions were resolved

using a third examiner and the test conclusions were finalized.  The results of the study are as follows:

Table 12 ‐ IAI Study Results

Compression Level Result Code Total

1 2 3

5:1 202 0 0 202

10:1 200 0 0 200

15:1 195 7 0 202

20:1 37 159 0 196

Total 634 166 0 800

Based on the IAI results, an incidence of 3.4 % in Code 2 responses was deemed as acceptable and the compression

ratio of 15:1 was recommended as the acceptable standard setting for the transmission and storage of electronic

fingerprint images.

   

                                                                    8 Very little information could be recovered on the exact printing process used to produce the IAI study’s examination deck.   Based

on interviews with individuals who were somewhat familiar with the original study, it appears that the images were printed using a

specialized high‐resolution thermal printer on specialized thermal paper in order to minimize any aberrations caused by the

printing process.
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The following table summarizes the key differences between the protocols of the IAI study and this study.

Table 13 ‐ Key protocol differences between IAI WSQ study and This Study

IAI WSQ Study This Study

Examiners Two Three

Deadlock Resolution Third examiner for

deadlocked cases

Not needed

Capture Types Examined Ink card scan Ink card scan and digital live scan

Impression Types Examined Rolled prints only Rolled, Flat and Slap prints

Compression ratios examined 5:1, 10:1, 15:1 and 20:1 2:1, 5:1, 7:1, 10:1, 12:1, 15:1, 17:1, 20:1, 22:1,

26:1, 30:1, 34:1, and 38:1

Impression pairings examined Rolled to Rolled only. Rolled to Rolled, Rolled to Flat, Flat to

Rolled, Flat to Flat and Slap to Slap (see

Table 2)

Identification determination requested for

Pairing

No Yes

   
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Appendix B. Dataset Makeup

For the ink card scan portion of the tests, the study utilized fingerprint images based on the Base Demonstration

Model (BDM) fingerprints utilized in early tests of the FBI IAFIS system, and later used as the basis for the NIST SD‐27

special database [SD27].  This ink card scan data was collected as a result of law enforcement activities and represents

actual field data with collection dates ranging from 08/18/1973 through 04/12/1994.   The original FD‐249 fingerprint

collection cards with these images were retrieved by NIST and rescanned at 1000 ppi by NIST personnel under

controlled conditions.    The images were scanned at 8 bits per pixel gray‐scale using FBI certified software (Appendix

F complaint) and stored in a non‐compressed format to ensure no compression anomalies are introduced into the

original set.

For the digital live capture portion of the tests, the study again utilized actual operational data captured during

normal enforcement activities with collection dates ranging from 01/04/2010 through 04/13/2010.    The digital live

capture data was stored in an non‐compressed format to ensure no compression anomalies are introduced into the

original set prior to inclusion in the compression study.

Where possible, the image sets were equally balanced by gender, finger, pattern class and hand.  It should be noted

that balancing equally does not follow the natural demographic behavior of the population such as gender (48 %

males/52 % female [CIA1]) or pattern class (65 % Loops, 30 % Whorls, 5 % Arches [DOJ]).   The goal in having equal

distributions of each subsample was to present a sufficiently large set of each subsample so that they are equally

represented to the examiners and avoid the potential statistical bias of very small subsamples.   That is, all subsamples

are equally important with respect to compression irrespective of their relative incidence in the population.

Selection of match and non‐match data for ink card scan and digital live‐scan sets were made from each respective set

and pairing between the two was not made.  In comparing a particular impression to that same impression, the same

exact image/impression was used.    For example, in the rolled‐to‐rolled match cases, one rolled fingerprint image

served as both the original uncompressed image as well as the compressed case image (see Case 1 in Figure 20).  In

the cases where one impression could not be used (i.e., comparing a flat impression to a rolled impression, such as

Case 2 in Figure 20) different impressions from the same person were used in formulating the pair.  For non‐match

data, an image from a similar pattern class was selected but from a different subject.

Case 1 – Comparison of Same Impression Case 2 – Comparison of Mixed Impressions

Figure 20 ‐ Impression Comparison Examples
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Demographic Make‐up of Ink Card Scan Datasets

Ink card scan images used in this study consisted of 200 rolled images, 200 flat images, and 200 four‐finger slap

impressions.     

Table 14 ‐ Ink Card Scan Data classification by Impression Type

All Data

Impression Type Males Females Right Left

Flat Single Finger 100 100 96 104

Rolled Single Finger 100 100 96 104

Four Finger Slaps 100 100 100 100

Table 15 ‐ Ink Card Scan Pattern Classification for Single Finger Images by Impression Type

Data From Females (Single Finger) Data From Males (Single Finger)

Pattern Class Flat Rolled Right Left Pattern Class Flat Rolled Right Left

Arch 34 34 30 38 Arch 34 34 34 34

Loop 33 33 32 34 Loop 33 33 32 34

Whorl 33 33 34 32 Whorl 33 33 34 32

Total 100 100 96 104 Total 100 100 100 100

Table 16 ‐ Ink Card Scan Pattern Classification for Single Finger Images by Finger (Females)

Data From Females (Single Finger)

Pattern Class

R. ThumbR. IndexR. MiddleR. RingR. LittleL. ThumbL. IndexL. MiddleL. RingL. LittleTotal

Arch 4 4 3 4 0 3 5 4 3 4 34

Loop 3 3 4 3 3 4 3 3 4 3 33

Whorl 3 4 3 3 4 3 3 4 3 3 33

Total 10 11 10 10 7 10 11 11 10 10 100

Table 17 ‐ Ink Card Scan Pattern Classification for Single Finger Images by Finger (Males)

Data From Males (Single Finger)

Pattern Class

R. ThumbR. IndexR. MiddleR. RingR. LittleL. ThumbL. IndexL. MiddleL. RingL. LittleTotal

Arch 4 3 3 4 3 3 4 3 3 4 34

Loop 3 3 4 3 3 4 3 3 4 3 33

Whorl 3 4 3 3 4 3 3 4 3 3 33

Total 10 10 10 10 10 10 10 10 10 10 100
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Make‐up of the digital live capture data sets

Digital live capture images used in this study consisted of 200 rolled images, 200 flat images, and 200 four‐finger slap

impressions.     

Table 18 ‐ Live‐Scan Data classification by Impression Type

All Data Records

Impression Type Males Females Right Left

Flat Single Finger 100 100 100 100

Rolled Single Finger 100 100 100 100

Four Finger Slaps 100 100 96 104

Table 19 ‐ Live‐Scan Pattern Classification for Single Finger Images by Impression Type

Data From Females (Single Finger) Data From Males (Single Finger)

Pattern Class Flat Rolled Right Left Pattern Class Flat Rolled Right Left

Arch 33 33 36 30 Arch 33 33 36 30

Loop 34 34 30 38 Loop 34 34 30 38

Whorl 33 33 34 32 Whorl 33 33 34 32

Total 100 100 100 100 Total 100 100 100 100

Table 20 ‐ Live‐Scan Pattern Classification for Single Finger Images by Finger (Females)

Data From Females (Single Finger)

Pattern Class

R. ThumbR. IndexR. MiddleR. RingR. LittleL. ThumbL. IndexL. MiddleL. RingL. LittleTotal

Arch 4 4 4 3 3 3 3 3 3 3 33

Loop 3 3 3 3 3 3 4 4 4 4 34

Whorl 3 3 3 4 4 4 3 3 3 3 33

Total 10 10 10 10 10 10 10 10 10 10 100

Table 21 ‐ Live‐Scan Pattern Classification for Single Finger Images by Finger (Males)

Data From Males (Single Finger)

Pattern Class

R. ThumbR. IndexR. MiddleR. RingR. LittleL. ThumbL. IndexL. MiddleL. RingL. LittleTotal

Arch 4 4 4 3 3    3 3 3 3 3 33

Loop 3 3 3 3 3 3 4 4 4 4 34

Whorl 3 3 3 4 4    4 3 3 3 3 33

Total 10 10 10 10 10 10 10 10 10 10 100
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Data demographics

The fingerprint images used to compile the datasets as described above were taken from several subjects. The

balancing of the samples used was based on the uniqueness of a single fingerprint and not individual subjects. As

such, multiple, yet distinct, fingerprint impressions were taken from some subjects (i.e., some subjects contributed

more than one finger).  

Table 22 ‐ Gender Breakdown for Data

Subjects by Gender and Race

Males Females White Black Hispanic Asian

Ink Capture Dataset 72 17 38 47 3 1

Live Capture Dataset 60 63 11 51 59 2

Combined Dataset (ALL) 132 80 49 98 62 3

Table 23 ‐ Age Breakdown for Data

Subjects by Age            

Under 18 18‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50+

Ink Card Scan Dataset 5 54 18 3 1 4 3 1

Digital Live Capture Dataset 9 38 23 15 18 8 4 8

Combined Dataset (ALL) 14 92 41 18 19 12 7 9

Table 24 ‐ Other Metadata: Height and Weight

Subjects by Height and Weight          <5’0” 5’0” – 5’5” 5’6” – 5’11” 6’0”+ <100 lbs 100 – 149 lbs 150 – 199 lbs 200 – 249 lbs 250+ lbs

Ink Card Scan Dataset 0 11 59 19 0 27 48 10 4

Digital Live Capture Dataset 4 45 63 11 1 45 56 17 4

Combined Dataset (All) 4 56 122 30 1 72 104 27 8

Table 25 ‐ Other Metadata: Eye Color

Subjects by Eye Color    

Brown Black Blue Green Hazel

Ink Card Scan Dataset 69 2 12 1 5

Digital Live Capture Dataset 111 1 2 6 3

Combined Dataset (ALL) 180 3 14 7 8
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Table 26 ‐ Image Geometry Data

Impression Type Image Width (Pixels) Image Height (Pixels) Image Size (KB, Non‐compressed)

Mean Median Min Max Mean Median Min Max Mean Median Min Max

Ink Card Scan Rolled 1016 1002 652 1718 1166 1165 643 2063 1170.8 1134.4 535.0 2568.6

Ink Card Scan Flat 602 592 444 843 785 801 497 1008 459.6 476.0 269.0 564.1

Ink Card Scan Slap 3192 3192 3045 3334 2009 2013 1744 2082 6264.4 6264.7 5339.3 6597.5

Digital Live Scan Rolled 1600 1600 1600 1600 1500 1500 1500 1500 2343.8 2343.8 2343.8 2343.8

Digital Live Scan Flat 687 674 515 825 1057 993 672 1500 725.1 652.5 377.9 1171.9

Digital Live Scan Slap 3200 3200 3200 3200 2000 2000 2000 2000 6250.0 6250.0 6250.0 6250.0
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Appendix C. Equipment Used for Study

1 x Commodity Router:  

The router provides connectivity among the three workstations and NAS device, as well as providing remote access to

the workstations and NAS for administration.  

3 x Commodity Workstations:  

The workstations are configured with 8 GB RAM, 300 GB HD, 64‐bit operating system, FIXT software and data.

3 x 24 inch Monitors:  

The IPS‐panel monitors are connected via DVI‐D and calibrated (see below) for optimal accuracy and consistency.  The

monitors were operated at their native resolution of 1920x1200, yielding a dpi measurement of approximately

94.3ppi.   The zoom functionality in software allowed the examiners to zoom in and out of the image, and view them

in the range of approximately 10x to 50x given these specific monitors.

1 x Network Attached Storage (NAS):

The NAS contains master copies of the FIXT software and data, as well as iterative copies of each stations logs/results

(saved at the end of each session).  

1 x Monitor Calibration Device:

The monitors were calibrated using a system which consists of a colorimeter paired with proprietary software

designed specifically for use with the colorimeter and for the purpose of monitor calibration. The colorimeter is a

sensor which provides an accurate measurement of colors as they actually appear on the monitor screen. During the

calibration process, the colorimeter is physically attached to the monitor while the software displays a series of solid

colors on the screen. The colorimeter measures the actual color values displayed on the monitor and then provides

these measurements to the software. The software uses these measurements to calculate the difference between

the color values as they are displayed on the monitor against the true color values within the software. The software

then applies configuration changes to the system in order to correct the color values displayed by the monitor,

ensuring accurate color reproduction. Due to the fact that each monitor, even of the same model, performs slightly

differently in terms of the accuracy of color reproduction, this process was completed independently on each of the

three FIXT workstations.  
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Appendix D. Observation Data

Ink Capture Compression Degradation Observations

Table 27 ‐ Ink Capture Degradation Results

 

1:1 2:1 5:1 7:1 

10:1 12:1 15:1 17:1 20:1 22:1 26:1 30:1 34:1 38:1 

Case 1 - Ink Rolled to Ink Rolled, Match Pair 

Level 2 and 3 detail degradation 0 0 0 0 0 0 0 0 0 0 1 0 0 3 

Level 3 detail degradation 2 0 4 4 6 10 8 16 11 23 28 29 32 36 

Some benign degradation 33 34 45 67 92 107 112 125 133 131 126 133 141 134 

No visible degradation 165 166 151 129 102 83 80 59 56 46 45 38 27 27 

Case 2 - Ink Rolled to Ink Rolled, Non-Match Pair 

Level 2 and 3 detail degradation 1 1 1 0 1 0 1 1 1 1 0 4 5 4 

Level 3 detail degradation 27 34 25 32 45 40 46 47 61 63 80 66 76 77 

Some benign degradation 96 80 105 104 97 98 105 116 97 104 93 99 100 95 

No visible degradation 76 85 69 64 57 62 48 36 41 32 27 31 19 24 

Case 3 - Ink Rolled to Ink Flat, Match Pair 

Level 2 and 3 detail degradation 4 1 0 3 5 4 4 2 4 4 7 6 6 13 

Level 3 detail degradation 29 26 41 41 46 48 54 62 51 61 69 72 80 79 

Some benign degradation 120 128 113 120 124 118 118 118 131 123 108 111 102 97 

No visible degradation 47 45 46 36 25 30 24 18 14 12 16 11 12 11 

Case 4 - Ink Rolled to Ink Flat, Non-Match Pair 

Level 2 and 3 detail degradation 2 4 4 2 3 4 4 5 1 3 6 4 5 7 

Level 3 detail degradation 27 32 22 29 36 36 45 41 35 50 60 57 85 96 

Some benign degradation 91 82 109 102 101 90 94 108 121 109 96 112 79 76 

No visible degradation 80 82 65 67 60 70 57 46 43 38 38 27 31 21 

Case 5 - Ink Flat to Ink Rolled, Match Pair 

Level 2 and 3 detail degradation 4 9 6 1 6 2 5 4 4 5 4 3 4 11 

Level 3 detail degradation 32 36 35 46 51 41 48 59 75 79 75 84 104 101 

Some benign degradation 109 110 118 116 109 131 116 109 101 95 102 104 75 80 

No visible degradation 55 45 41 37 34 26 31 28 20 21 19 9 17 8 

Case 6 - Ink Flat to Ink Rolled, Non-Match Pair 

Level 2 and 3 detail degradation 3 3 2 2 2 2 3 5 1 3 4 8 2 2 

Level 3 detail degradation 25 28 30 38 41 34 47 43 55 55 52 63 81 82 

Some benign degradation 82 99 102 99 96 105 115 105 102 105 108 101 87 86 

No visible degradation 90 70 66 61 61 59 35 47 42 37 36 28 30 30 

Case 7 - Ink Flat to Ink Flat, Match Pair 

Level 2 and 3 detail degradation 1 4 0 0 0 0 3 1 0 1 3 3 4 5 

Level 3 detail degradation 6 2 3 3 2 10 6 9 11 18 20 18 35 47 

Some benign degradation 30 19 20 47 68 79 98 107 112 118 118 119 109 104 

No visible degradation 163 175 177 150 130 111 93 83 77 63 59 60 52 44 

Case 8 - Ink Flat to Ink Flat, Non-Match Pair 

Level 2 and 3 detail degradation 2 6 4 4 2 4 6 5 6 5 7 7 15 14 

Level 3 detail degradation 28 17 35 35 30 41 37 39 44 54 50 55 69 78 

Some benign degradation 87 75 78 79 94 84 81 105 101 88 85 94 89 65 

No visible degradation 83 102 83 82 74 71 76 51 49 53 58 44 27 43 

Case 9 - Ink Slap to Ink Slap, Match Pair 

Level 2 and 3 detail degradation 1 1 1 0 1 0 0 1 0 1 1 2 0 1 

Level 3 detail degradation 1 0 1 4 1 9 11 11 8 8 14 14 18 24 

Some benign degradation 18 20 20 26 37 51 67 80 101 103 117 116 117 115 

No visible degradation 180 179 178 170 161 140 122 108 91 88 68 68 65 60 

Case 10 - Ink Slap to Ink Slap, Non-Match Pair 

Level 2 and 3 detail degradation 1 0 0 0 1 1 0 1 1 0 1 0 1 2 

Level 3 detail degradation 30 35 33 43 41 36 36 34 41 36 58 55 45 62 

Some benign degradation 92 83 82 85 84 83 92 89 104 94 92 90 105 102 

No visible degradation 77 82 85 72 74 80 72 76 54 70 49 55 49 34 
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Live Capture Compression Degradation Observations

Table 28 ‐ Live Capture Degradation Results

 

1:1 2:1 5:1 7:1 

10:1 12:1 15:1 17:1 20:1 22:1 26:1 30:1 34:1 38:1 

Case 11 - Digital Live Capture Rolled to Digital Live Capture Rolled, Match Pair 

Level 2 and 3 detail degradation 1 0 0 1 0 2 1 1 1 1 1 0 1 1 

Level 3 detail degradation 2 1 1 0 2 4 2 12 13 33 45 62 63 69 

Some benign degradation 38 36 43 58 75 109 124 139 146 136 131 119 117 113 

No visible degradation 159 163 156 141 123 85 73 48 40 30 23 19 19 17 

Case 12 - Digital Live Capture Rolled to Digital Live Capture Rolled, Non-Match Pair 

Level 2 and 3 detail degradation 3 2 5 4 3 4 2 4 6 6 5 3 5 6 

Level 3 detail degradation 17 12 7 6 22 19 29 32 57 50 73 82 89 81 

Some benign degradation 113 131 135 150 149 156 153 151 125 135 110 107 97 105 

No visible degradation 67 55 53 40 26 21 16 13 12 9 12 8 9 8 

Case 13 - Digital Live Capture Rolled to Digital Live Capture Flat, Match Pair 

Level 2 and 3 detail degradation 7 7 5 7 7 8 7 4 6 3 5 6 5 5 

Level 3 detail degradation 23 13 22 14 17 12 21 29 27 30 31 34 30 42 

Some benign degradation 113 120 125 116 120 130 128 132 114 127 120 123 126 108 

No visible degradation 57 60 48 63 56 50 44 35 53 40 44 37 39 45 

Case 14 - Digital Live Capture Rolled to Digital Live Capture Flat, Non-Match Pair 

Level 2 and 3 detail degradation 8 6 11 8 6 4 9 5 7 4 6 4 4 6 

Level 3 detail degradation 20 14 12 23 18 18 21 33 22 43 32 46 50 35 

Some benign degradation 121 129 128 115 122 139 120 114 138 115 121 110 105 127 

No visible degradation 51 51 49 54 54 39 50 48 33 38 41 40 41 32 

Case 15 - Digital Live Capture Flat to Digital Live Capture Rolled, Match Pair 

Level 2 and 3 detail degradation 5 6 5 6 6 6 4 9 6 9 11 9 4 8 

Level 3 detail degradation 16 11 20 17 20 10 19 24 19 19 22 28 19 28 

Some benign degradation 117 125 124 122 127 130 132 110 121 115 112 116 123 113 

No visible degradation 62 58 51 55 47 54 45 57 54 57 55 47 54 51 

Case 16 - Digital Live Capture Flat to Digital Live Capture Rolled, Non-Match Pair 

Level 2 and 3 detail degradation 8 7 9 9 11 8 7 8 7 8 7 2 10 9 

Level 3 detail degradation 17 12 21 19 16 15 17 27 19 18 26 31 18 31 

Some benign degradation 123 121 117 114 125 133 115 113 130 115 124 115 126 123 

No visible degradation 52 60 53 58 48 44 61 52 44 59 43 52 46 37 

Case 17 - Digital Live Capture Flat to Digital Live Capture Flat, Match Pair 

Level 2 and 3 detail degradation 1 0 1 1 1 1 0 1 0 1 0 1 1 0 

Level 3 detail degradation 0 0 0 1 0 2 0 1 3 3 6 6 8 13 

Some benign degradation 25 31 30 31 42 37 55 64 68 56 75 73 77 63 

No visible degradation 174 169 169 167 157 160 145 134 129 140 119 120 114 124 

Case 18 - Digital Live Capture Flat to Digital Live Capture Flat, Non-Match Pair 

Level 2 and 3 detail degradation 2 2 1 2 1 3 2 3 2 3 3 2 3 2 

Level 3 detail degradation 2 3 5 3 4 4 5 5 7 8 5 8 10 12 

Some benign degradation 79 80 72 81 91 96 89 98 92 92 108 107 111 107 

No visible degradation 117 115 122 114 104 97 104 94 99 97 84 83 76 79 

Case 19 - Digital Live Capture Slap to Digital Live Capture Slap, Match Pair 

Level 2 and 3 detail degradation 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Level 3 detail degradation 0 0 0 0 0 0 1 2 1 3 1 3 9 11 

Some benign degradation 8 5 6 12 30 50 81 103 125 129 141 143 146 149 

No visible degradation 192 195 194 188 170 150 118 95 74 68 58 54 45 40 

Case 20 - Digital Live Capture Slap to Digital Live Capture Slap, Non-Match Pair 

Level 2 and 3 detail degradation 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Level 3 detail degradation 2 1 0 0 1 3 2 3 4 3 8 10 8 17 

Some benign degradation 38 37 38 44 70 78 124 126 140 144 146 149 165 152 

No visible degradation 160 162 162 156 129 119 74 71 56 53 46 41 27 31 
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

PAGE 69 OF 72

Ink Capture Compression Identification Observations

Table 29 ‐ Ink Capture Identification Results

 

1:1 2:1 5:1 7:1 

10:1 12:1 15:1 17:1 20:1 22:1 26:1 30:1 34:1 38:1 

Ink Rolled to Ink Rolled, Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Correct Identification 200 200 200 200 200 200 200 200 200 200 200 200 200 200 

Ink Rolled to Ink Rolled, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 3 3 3 3 1 1 4 1 3 3 3 4 2 1 

Correct Identification 197 197 197 197 199 199 196 199 197 197 197 196 198 199 

Ink Rolled to Ink Flat, Match Pair 

Incorrect Identification 1 1 2 1 0 2 0 0 1 1 2 1 2 2 

Indeterminate Identification 5 6 5 6 7 5 6 7 4 6 5 6 8 7 

Correct Identification 194 193 193 193 193 193 194 193 195 193 193 193 190 191 

Ink Rolled to Ink Flat, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 5 3 4 0 3 1 3 3 3 2 3 1 1 3 

Correct Identification 195 197 196 200 197 199 197 197 197 198 197 199 199 197 

Ink Flat to Ink Rolled, Match Pair 

Incorrect Identification 0 1 0 1 0 1 0 1 1 0 0 1 0 0 

Indeterminate Identification 6 4 6 7 7 5 6 6 6 8 7 7 7 6 

Correct Identification 194 195 194 192 193 194 194 193 193 192 193 192 193 194 

Ink Flat to Ink Rolled, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 3 4 3 5 2 3 3 3 3 3 5 4 2 5 

Correct Identification 197 196 197 195 198 197 197 197 197 197 195 196 198 195 

Ink Flat to Ink Flat, Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 0 0 0 0 0 2 1 0 0 0 0 1 1 

Correct Identification 200 200 200 200 200 200 198 199 200 200 200 200 199 199 

Ink Flat to Ink Flat, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 2 1 3 3 1 2 1 3 2 2 3 4 5 3 

Correct Identification 198 199 197 197 199 198 199 197 198 198 197 196 195 197 

Ink Slap to Ink Slap, Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Correct Identification 200 200 200 200 200 200 200 200 200 200 200 200 200 200 

Ink Slap to Ink Slap, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Correct Identification 200 200 200 200 200 200 200 200 200 200 200 200 200 200 
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

PAGE 70 OF 72

Live Capture Compression Identification Observations

Table 30 ‐ Live Capture Identification Results

 

1:1 2:1 5:1 7:1 

10:1 12:1 15:1 17:1 20:1 22:1 26:1 30:1 34:1 38:1 

Digital Live Capture Rolled to Digital Live Capture Rolled, Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Correct Identification 200 200 200 200 200 200 200 200 200 200 200 200 200 200 

Digital Live Capture Rolled to Digital Live Capture Rolled, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 1 3 2 1 1 1 3 0 3 1 1 3 1 

Correct Identification 200 199 197 198 199 199 199 197 200 197 199 199 197 199 

Digital Live Capture Rolled to Digital Live Capture Flat, Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 2 1 1 1 0 0 1 

Indeterminate Identification 3 2 3 3 3 3 3 1 1 1 1 3 4 1 

Correct Identification 197 198 197 197 197 197 197 197 198 198 198 197 196 198 

Digital Live Capture Rolled to Digital Live Capture Flat, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 1 3 1 2 1 1 1 0 1 1 2 1 0 2 

Correct Identification 199 197 199 198 199 199 199 200 199 199 198 199 200 198 

Digital Live Capture Flat to Digital Live Capture Rolled, Match Pair 

Incorrect Identification 1 1 0 0 0 0 1 1 0 0 0 0 0 0 

Indeterminate Identification 3 2 2 2 3 2 2 2 2 2 2 3 2 3 

Correct Identification 196 197 198 198 197 198 197 197 198 198 198 197 198 197 

Digital Live Capture Flat to Digital Live Capture Rolled, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 2 1 3 3 4 2 1 1 2 3 3 2 3 2 

Correct Identification 198 199 197 197 196 198 199 199 198 197 197 198 197 198 

Digital Live Capture Flat to Digital Live Capture Flat, Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 0 0 0 0 1 0 0 0 0 0 0 0 0 

Correct Identification 200 200 200 200 200 199 200 200 200 200 200 200 200 200 

Digital Live Capture Flat to Digital Live Capture Flat, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 1 2 1 2 1 1 1 1 1 1 1 2 2 2 

Correct Identification 199 198 199 198 199 199 199 199 199 199 199 198 198 198 

Digital Live Capture Slap to Digital Live Capture Slap, Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Correct Identification 200 200 200 200 200 200 200 200 200 200 200 200 200 200 

Digital Live Capture Slap to Digital Live Capture Slap, Non-Match Pair 

Incorrect Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Indeterminate Identification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Correct Identification 200 200 200 200 200 200 200 200 200 200 200 200 200 200 
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

PAGE 71 OF 72

Appendix E. Statistical Parameters of Bootstrap Results and Hypothesis Testing

Table 31 and Table 32 summarize means, medians, and standard errors of the mean for bootstrap replicates of the degradation

score for each of 20 matching scenarios at each of 14 compression levels. Z‐tests are used to test the difference between test

score values for non‐compressed images and those at each of the compression levels. P‐values less than 0.05 (shown below in

dashed boxes) indicate differences significant at the 5 % level.   It should be noted that these tests examine the degradation

observed at each level of compression in contrast to that observed between two images that have not been compressed. In the

“id” case, both images are identical except for the level of compression applied to one of the two images in the match pair.  

For each matching scenario, significance tests reveal the compression level at which observer ratings of the relative degradation

depart from that of the non‐compressed case. For most of the scenarios, this occurs at 10:1 compression.  

Table 31 ‐ Distribution parameters of bootstrap replications and hypothesis tests of differences in degradation between

lossless baseline and compressed images (Ink Card Scan)

1:1 2:1 5:1 7:1 10:1 12:1 15:1 17:1 20:1 22:1 26:1 30:1 34:1 38:1

Ink Card Scan

Rolled to Rolled, Match Pair Mean 0.046 0.042 0.066 0.094 0.130 0.159 0.160 0.197 0.194 0.221 0.232 0.238 0.256 0.272

StdErr 0.007 0.007 0.009 0.009 0.010 0.010 0.010 0.010 0.010 0.010 0.011 0.010 0.010 0.012

Median 0.046 0.043 0.066 0.094 0.130 0.159 0.160 0.196 0.194 0.221 0.233 0.239 0.256 0.273

z  ‐‐‐‐‐‐‐  0.444  ‐2.068 ‐4.559 ‐7.518 ‐9.465 ‐9.607 ‐11.942 ‐12.930  ‐15.381  ‐14.851  ‐15.916 ‐18.603 ‐17.016

p ‐‐‐‐‐‐‐  0.657 0.039 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Rolled, Non‐Match Pair Mean 0.193 0.190 0.199 0.210 0.239 0.223 0.251 0.268 0.279 0.293 0.316 0.309 0.340 0.331

StdErr 0.012 0.013 0.012 0.012 0.013 0.012 0.013 0.012 0.013 0.012 0.012 0.014 0.013 0.013

Median 0.193 0.190 0.199 0.210 0.239 0.223 0.251 0.268 0.279 0.293 0.316 0.309 0.340 0.331

z ‐‐‐‐‐‐‐  0.165 ‐0.400 ‐1.123 ‐2.967 ‐1.851 ‐3.571 ‐4.464 ‐5.359 ‐6.964 ‐7.341 ‐6.632 ‐8.926 ‐8.427

p  ‐‐‐‐‐‐‐  0.869 0.689 0.262 0.003 0.064 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Flat, Match Pair Mean 0.242 0.230 0.244 0.267 0.295 0.287 0.303 0.312 0.311 0.326 0.342 0.349 0.358 0.383

StdErr 0.013 0.011 0.012 0.012 0.013 0.013 0.013 0.011 0.012 0.012 0.013 0.013 0.013 0.015

Median 0.243 0.230 0.244 0.268 0.295 0.288 0.303 0.313 0.311 0.326 0.341 0.349 0.356 0.384

z  ‐‐‐‐‐‐‐  0.800  ‐0.072 ‐1.586 ‐3.115 ‐2.626 ‐3.755 ‐4.960 ‐4.479  ‐5.191  ‐6.056  ‐6.738 ‐7.580 ‐8.463

p ‐‐‐‐‐‐‐  0.424 0.942 0.113 0.002 0.009 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Flat, Non‐Match Pair Mean 0.191 0.202 0.211 0.210 0.231 0.223 0.251 0.262 0.244 0.276 0.299 0.302 0.336 0.370

StdErr 0.014 0.015 0.013 0.013 0.014 0.015 0.015 0.015 0.012 0.014 0.015 0.013 0.014 0.015

Median 0.191 0.203 0.211 0.210 0.231 0.223 0.250 0.263 0.244 0.276 0.299 0.303 0.336 0.370

z ‐‐‐‐‐‐‐  ‐0.623 ‐1.232 ‐1.290 ‐2.217 ‐1.794 ‐3.499 ‐4.070 ‐3.164 ‐5.153 ‐6.043 ‐6.613 ‐8.786 ‐10.596

p  ‐‐‐‐‐‐‐  0.534 0.218 0.197 0.027 0.073 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000

Flat to Rolled, Match Pair Mean 0.236 0.272 0.265 0.265 0.293 0.276 0.290 0.303 0.333 0.341 0.335 0.355 0.374 0.408

StdErr 0.014 0.016 0.015 0.012 0.014 0.011 0.013 0.013 0.013 0.014 0.013 0.011 0.013 0.014

Median 0.236 0.271 0.265 0.265 0.293 0.276 0.290 0.304 0.334 0.341 0.335 0.355 0.374 0.408

z  ‐‐‐‐‐‐‐  ‐2.144  ‐1.687 ‐1.593 ‐3.269 ‐2.394 ‐3.252 ‐4.120 ‐5.364  ‐5.599  ‐5.583  ‐6.950 ‐8.443 ‐9.388

p ‐‐‐‐‐‐‐  0.032 0.092 0.111 0.001 0.017 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Flat to Rolled, Non‐Match Pair Mean 0.180 0.208 0.212 0.228 0.232 0.226 0.276 0.264 0.270 0.284 0.285 0.324 0.321 0.322

StdErr 0.014 0.014 0.013 0.013 0.014 0.013 0.013 0.015 0.013 0.014 0.014 0.015 0.013 0.013

Median 0.180 0.208 0.211 0.229 0.233 0.226 0.276 0.264 0.270 0.284 0.285 0.324 0.321 0.323

z ‐‐‐‐‐‐‐  ‐1.683 ‐1.889 ‐2.937 ‐3.175 ‐2.800 ‐6.020 ‐4.865 ‐5.185 ‐5.800 ‐5.940 ‐7.588 ‐8.332 ‐8.423

p  ‐‐‐‐‐‐‐  0.092 0.059 0.003 0.001 0.005 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Flat to Flat, Match Pair Mean 0.058 0.049 0.032 0.066 0.090 0.124 0.152 0.161 0.168 0.198 0.212 0.209 0.243 0.272

StdErr 0.010 0.011 0.007 0.008 0.009 0.011 0.012 0.011 0.010 0.011 0.012 0.012 0.014 0.014

Median 0.058 0.048 0.033 0.066 0.090 0.124 0.153 0.161 0.168 0.198 0.211 0.209 0.244 0.271

z  ‐‐‐‐‐‐‐  0.753 2.530 ‐0.794 ‐2.915 ‐5.322 ‐6.285 ‐7.909 ‐8.362  ‐11.558  ‐12.035  ‐10.333 ‐12.580 ‐13.343

p ‐‐‐‐‐‐‐  0.451 0.011 0.427 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Flat to Flat, Non‐Match Pair Mean 0.189 0.166 0.205 0.206 0.203 0.228 0.224 0.254 0.266 0.270 0.267 0.290 0.359 0.346

StdErr 0.013 0.016 0.015 0.015 0.013 0.015 0.016 0.014 0.015 0.015 0.016 0.016 0.018 0.018

Median 0.189 0.166 0.205 0.205 0.203 0.228 0.224 0.254 0.266 0.270 0.266 0.290 0.360 0.346

z ‐‐‐‐‐‐‐  1.395 ‐0.984 ‐1.044 ‐0.849 ‐2.073 ‐2.046 ‐3.862 ‐4.609 ‐4.479 ‐4.257 ‐5.507 ‐9.363 ‐8.282

p  ‐‐‐‐‐‐‐  0.163 0.325 0.297 0.396 0.038 0.041 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Slap to Slap, Match Pair Mean 0.030 0.030 0.032 0.043 0.054 0.086 0.111 0.132 0.146 0.153 0.186 0.190 0.191 0.209

StdErr 0.007 0.007 0.007 0.008 0.009 0.010 0.011 0.012 0.010 0.011 0.011 0.012 0.011 0.012

Median 0.029 0.030 0.033 0.043 0.054 0.086 0.111 0.133 0.146 0.153 0.185 0.190 0.191 0.209

z  ‐‐‐‐‐‐‐  ‐0.025  ‐0.322 ‐1.349 ‐2.696 ‐5.134 ‐6.276 ‐7.544 ‐10.102  ‐9.558  ‐13.338  ‐13.373 ‐13.197 ‐13.782

p ‐‐‐‐‐‐‐  0.980 0.748 0.177 0.007 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Slap to Slap, Non‐Match Pair Mean 0.195 0.192 0.185 0.213 0.212 0.199 0.205 0.202 0.237 0.208 0.265 0.249 0.249 0.293

StdErr 0.013 0.013 0.013 0.013 0.013 0.013 0.012 0.013 0.013 0.012 0.014 0.013 0.013 0.013

Median 0.195 0.191 0.185 0.214 0.213 0.199 0.205 0.201 0.238 0.208 0.265 0.250 0.249 0.293

z ‐‐‐‐‐‐‐  0.173 0.584 ‐1.112 ‐1.001 ‐0.206 ‐0.572 ‐0.388 ‐2.420 ‐0.728 ‐4.245 ‐2.946 ‐2.958 ‐5.252

p  ‐‐‐‐‐‐‐  0.862 0.560 0.266 0.317 0.837 0.567 0.698 0.016 0.467 0.000 0.003 0.003 0.000
NISTIR 7778 ‐ Effects of JPEG 2000 Image Compression on 1000 ppi Fingerprint Imagery

PAGE 72 OF 72

Table 32 ‐ Distribution parameters of bootstrap replications and hypothesis tests of differences in degradation between

lossless baseline and compressed images (Digital Live Capture)

1:1 2:1 5:1 7:1 10:1 12:1 15:1 17:1 20:1 22:1 26:1 30:1 34:1 38:1

Digital Live Capture

Rolled to Rolled, Match Pair Mean 0.057 0.048 0.056 0.077 0.098 0.156 0.165 0.208 0.220 0.257 0.281 0.303 0.308 0.319

StdErr 0.009 0.007 0.008 0.009 0.009 0.011 0.010 0.010 0.010 0.011 0.011 0.011 0.012 0.011

Median 0.058 0.048 0.056 0.078 0.099 0.156 0.165 0.209 0.220 0.258 0.281 0.304 0.308 0.319

z  ‐‐‐‐‐‐‐  0.982 0.107 ‐1.737 ‐3.453 ‐7.597 ‐8.337 ‐11.986 ‐12.584  ‐15.285  ‐16.558  ‐17.220 ‐17.896 ‐18.014

p ‐‐‐‐‐‐‐  0.326 0.915 0.082 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Rolled, Non‐Match Pair Mean 0.199 0.204 0.212 0.223 0.256 0.262 0.274 0.289 0.329 0.324 0.345 0.354 0.369 0.363

StdErr 0.012 0.011 0.012 0.011 0.011 0.011 0.010 0.010 0.013 0.012 0.013 0.011 0.012 0.012

Median 0.199 0.204 0.211 0.223 0.256 0.263 0.274 0.289 0.329 0.324 0.345 0.354 0.369 0.364

z ‐‐‐‐‐‐‐  ‐0.351 ‐0.873 ‐1.887 ‐3.969 ‐4.911 ‐5.752 ‐6.410 ‐8.495 ‐8.687 ‐9.252 ‐9.020 ‐10.156 ‐10.296

p  ‐‐‐‐‐‐‐  0.725 0.383 0.059 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rolled to Flat, Match Pair Mean 0.235 0.218 0.237 0.215 0.228 0.233 0.248 0.258 0.240 0.249 0.253 0.269 0.258 0.265

StdErr 0.015 0.015 0.014 0.015 0.014 0.014 0.015 0.013 0.015 0.012 0.014 0.014 0.013 0.015

Median 0.234 0.218 0.236 0.215 0.229 0.233 0.248 0.258 0.240 0.249 0.253 0.269 0.258 0.265

z  ‐‐‐‐‐‐‐  0.975  ‐0.122 1.264 0.368 0.133 ‐0.854 ‐1.498 ‐0.432  ‐0.949  ‐1.056  ‐2.141 ‐1.459 ‐1.809

p ‐‐‐‐‐‐‐  0.330 0.903 0.206 0.713 0.895 0.393 0.134 0.666 0.343 0.291 0.032 0.145 0.070

Rolled to Flat, Non‐Match Pair Mean 0.241 0.226 0.245 0.241 0.228 0.239 0.248 0.250 0.263 0.271 0.262 0.273 0.276 0.277

StdErr 0.015 0.013 0.016 0.015 0.014 0.012 0.015 0.014 0.014 0.014 0.014 0.014 0.014 0.014

Median 0.241 0.226 0.245 0.241 0.228 0.239 0.248 0.249 0.263 0.271 0.261 0.273 0.276 0.276

z ‐‐‐‐‐‐  0.962 ‐0.251 ‐0.020 0.837 0.152 ‐0.410 ‐0.541 ‐1.371 ‐1.788 ‐1.235 ‐1.968 ‐1.909 ‐1.984

p  ‐‐‐‐‐‐  0.336 0.802 0.984 0.403 0.879 0.682 0.588 0.170 0.074 0.217 0.049 0.056 0.047

Flat to Rolled, Match Pair Mean 0.211 0.214 0.230 0.225 0.238 0.217 0.232 0.242 0.229 0.236 0.249 0.260 0.221 0.251

StdErr 0.014 0.013 0.013 0.014 0.014 0.013 0.012 0.016 0.014 0.016 0.017 0.016 0.012 0.015

Median 0.210 0.214 0.229 0.225 0.238 0.216 0.233 0.241 0.229 0.235 0.249 0.260 0.221 0.251

z  ‐‐‐‐‐‐  ‐0.172  ‐1.231 ‐0.789 ‐1.655 ‐0.353 ‐1.360 ‐1.851 ‐1.242  ‐1.426  ‐2.083  ‐2.845 ‐0.669 ‐2.241

p ‐‐‐‐‐‐  0.864 0.218 0.430 0.098 0.724 0.174 0.064 0.214 0.154 0.037 0.004 0.504 0.025

Flat to Rolled, Non‐Match Pair Mean 0.236 0.216 0.243 0.235 0.251 0.244 0.221 0.249 0.245 0.229 0.255 0.231 0.253 0.276

StdErr 0.014 0.014 0.016 0.016 0.016 0.014 0.015 0.015 0.014 0.015 0.014 0.013 0.016 0.015

Median 0.236 0.216 0.243 0.235 0.251 0.244 0.221 0.248 0.245 0.229 0.255 0.231 0.253 0.275

z ‐‐‐‐‐‐  1.305 ‐0.432 0.055 ‐0.893 ‐0.482 0.978 ‐0.766 ‐0.639 0.430 ‐1.231 0.318 ‐1.004 ‐2.387

p  ‐‐‐‐‐‐  0.192 0.666 0.956 0.372 0.630 0.328 0.444 0.523 0.667 0.218 0.750 0.315 0.017

Flat to Flat, Match Pair Mean 0.036 0.039 0.042 0.046 0.057 0.056 0.068 0.087 0.092 0.082 0.109 0.111 0.121 0.111

StdErr 0.007 0.006 0.008 0.008 0.009 0.009 0.008 0.010 0.009 0.010 0.010 0.010 0.011 0.011

Median 0.036 0.039 0.041 0.046 0.058 0.056 0.069 0.088 0.091 0.081 0.109 0.111 0.121 0.110

z  ‐‐‐‐‐‐  ‐0.372  ‐1.042 ‐1.176 ‐2.315 ‐2.334 ‐3.888 ‐5.403 ‐5.889  ‐4.693  ‐6.695  ‐6.986 ‐7.553 ‐6.325

p ‐‐‐‐‐‐  0.710 0.298 0.240 0.021 0.020 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Flat to Flat, Non‐Match Pair Mean 0.114 0.118 0.107 0.119 0.128 0.145 0.134 0.150 0.143 0.150 0.163 0.164 0.178 0.174

StdErr 0.011 0.011 0.010 0.011 0.010 0.012 0.011 0.012 0.012 0.012 0.012 0.011 0.012 0.012

Median 0.114 0.118 0.108 0.119 0.129 0.145 0.134 0.150 0.143 0.150 0.163 0.164 0.179 0.174

z ‐‐‐‐‐‐  ‐0.335 0.628 ‐0.484 ‐1.295 ‐2.930 ‐2.125 ‐3.170 ‐2.867 ‐3.285 ‐4.596 ‐4.777 ‐5.413 ‐5.119

p  ‐‐‐‐‐‐  0.738 0.530 0.628 0.195 0.003 0.034 0.002 0.004 0.001 0.000 0.000 0.000 0.000

Slap to Slap, Match Pair Mean 0.010 0.006 0.007 0.015 0.037 0.062 0.104 0.134 0.159 0.169 0.179 0.186 0.205 0.214

StdErr 0.003 0.003 0.003 0.004 0.006 0.008 0.009 0.009 0.009 0.009 0.008 0.008 0.009 0.008

Median 0.010 0.006 0.008 0.015 0.038 0.063 0.104 0.134 0.159 0.169 0.179 0.186 0.205 0.214

z  ‐‐‐‐‐‐  1.028 0.659 ‐0.980 ‐3.674 ‐6.775 ‐10.188 ‐12.885 ‐16.797  ‐17.164  ‐19.494  ‐20.243 ‐21.205 ‐23.585

p ‐‐‐‐‐‐  0.304 0.510 0.327 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Slap to Slap, Non‐Match Pair Mean 0.052 0.049 0.047 0.055 0.090 0.105 0.160 0.165 0.185 0.188 0.202 0.211 0.226 0.232

StdErr 0.008 0.007 0.007 0.007 0.009 0.009 0.009 0.009 0.008 0.008 0.008 0.008 0.007 0.009

Median 0.053 0.049 0.048 0.055 0.090 0.105 0.160 0.165 0.185 0.188 0.203 0.211 0.226 0.233

z ‐‐‐‐‐‐  0.367 0.501 ‐0.234 ‐3.413 ‐4.710 ‐9.279 ‐9.762 ‐12.102 ‐11.890 ‐12.964 ‐14.121 ‐16.853 ‐16.904

p  ‐‐‐‐‐‐  0.714 0.616 0.815 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000