IREX I

Performance of Iris Recognition Algorithms on

Standard Images

NIST Interagency Report 7629

P. Grother, E. Tabassi, G. W. Quinn, W. Salamon

Information Access Division

National Institute of Standards and Technology

October 30, 2009
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

ACKNOWLEDGMENTS

The authors would like to thank the Department of Homeland Security’s U. S. VISIT Office for

supporting this work. In addition, the authors are grateful to the Department of Homeland Security’s Science and Technology Directorate for supporting the ongoing standardization aspects

of this activity. We extend our thanks to the U.S. Department of Defense and the University of

Bath for providing iris imagery to the project.

Finally, thanks go IREX participants for their contributions toward the ISO/IEC 19794-6 standard

and the development of the IREX test specification1.

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.

1The formal CONOPS and API specification is available at http://iris.nist.gov/irex/irex api.pdf.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

EXECUTIVE SUMMARY

The Iris Exchange (IREX) was initiated by NIST in late 2007 to support interoperable exchange of iris imagery in high performance biometric applications. The first activity in the program, the IREX I evaluation, was conducted in cooperation

with the iris recognition industry to develop and test standard image formats, and to demonstrate that iris recognition algorithms can maintain their accuracy and interoperability with compact images. Standard formats are needed in

federated applications in which iris data is exchanged between interoperating systems. Compact size is a current and

vital requirement for applications in which imagery is passed across bandwidth-limited networks, or stored on identity

credentials.

IREX I was initiated to give quantitative support to the revision of the ISO/IEC 19794-6 and ANSI/NIST TYPE 17 standards,

and to form a multi-provider marketplace around those standards. As the largest independently administered test of iris

recognition technology to date, IREX I includes a formal evaluation of the state-of-the-art of iris recognition algorithms

from the following providers:

Cambridge University Cogent Systems Crossmatch Technologies Honeywell Iritech

L1 Identity Solutions LG Neurotechnology Retica Systems Sagem

Recognition algorithms from these organizations were evaluated in a three stage process. First, algorithms were applied

to convert raw images from contemporary iris cameras to the standardized iris images (i.e. IREX records) depicted here: Cropped image

Cropped and

masked image

KIND 3

KIND 7

Parent image from camera

Unsegmented polar

Reconstructed

rectilinear

KIND 1

KIND 7

KIND 48

KIND 16

Preparation of these records requires various detection, localization, cropping, sampling and masking operations. These

operations are non-trivial. They precede the second stage of processing in which features are extracted from standard

images to form a template. The IREX records are not iris templates; instead they are specialized interoperable images

designed for efficient storage. Templates contain proprietary “black box” feature representations. Their content is nonstandard, non-interoperable and not suitable for cross-agency exchange of iris data. The last stage, recognition, involves

matching of templates to produce comparison scores. The role of standardized images is depicted as follows.

Camera ISO Standard Image 

For Interoperable 

Exchange

1:N Identification, or

1:1 Verification

Non-interoperable, 

black-box, proprietary 

feature template

Database, or ID card

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

The primary impacts of IREX are listed below. These are followed, in the next section, by an extensive, technical, summary

of the results.

. IREX has advanced iris recognition toward the level of technical maturity and interoperability of fingerprint biometrics and has affirmed the potential for using iris biometrics as a second modality for large-scale identity management applications. This result will support storage of iris biometrics on identity credentials such as the United

States Government’s PIV cards in support of Homeland Security Presidential Directive (HSPD) 12. In addition it

will directly support the interoperability goals of Homeland Security Presidential Directives 6, 11 and 24.

. IREX quantified the core algorithmic capability of nineteen recent iris recognition software implementations from

ten organizations. This represents an order of magnitude expansion in the number of providers over the last half

decade.

. IREX required participating organizations to implement the image formats proposed for the ISO standard. The

result is that each provider now has off-the-shelf, or readily portable, software to support creation, validation, and

recognition of standard images.

. IREX I complements considerable activity in the area of iris camera development. This has occurred particularly in

the stand-off capture (where iris images are acquired at a few meters) and mobile device arenas. These, and other,

cameras are technically capable of producing images in conformance to formal standards. IREX recommends that

users should require cameras to do so.

. Standard iris image records with size of approximately thirty kilobytes can be produced for large-scale identification applications. This represents a factor of ten reduction in size over the images captured using contemporary

cameras.

. Standard iris image records with sizes around three kilobytes can be produced that are suitable for one-to-one

authentication applications. This factor of one hundred reduction in size over the images captured using contemporary cameras makes the images suitable for storage on “smart card” credentials.

. There is an industry-wide accuracy versus speed tradespace: Large-scale identity management applications benefit

from fast algorithms; Forensic and unconstrained-capture applications should leverage newer, more computationally expensive, iris recognition algorithms.

The authors are available to discuss and brief this report.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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TECHNICAL SUMMARY

The significant results of the test are listed by subject-matter area below. These should be weighed in light of the caveats

presented on page 13. Vendor comments on their IREX algorithms are included in the accompanying appendices2. These

are accompanied, separately, by free vendor comments on IREX itself.

IMPACT ON THE MARKETPLACE

. In parallel with the revision of the ISO/IEC 19794-6 iris image interchange standard, the IREX study

attracted ten organizations into implementing the standard. This entailed significant effort on the

part of the providers with respect to the production of syntactically correct code and with respect to

algorithmic functionality. The result is that each provider now has off-the-shelf, or readily portable,

software to support creation, validation, and recognition of standard images.

Sec. 4

. IREX quantified the core algorithmic capability of nineteen recent3iris recognition software implementations from ten organizations. This represents an order of magnitude expansion in the

number of providers over the last half decade. There are at least two other commercial providers

whose algorithms were not openly submitted to IREX. The availability of standards-compliant implementations from these providers is not known.

Sec. 4

. The compact interoperable formats tested here are amenable to lossless compression to as little

as 20 kilobytes. Lossless compression preserves imaging detail and ensures that iris recognition

accuracy for large scale one-to-many applications is not compromised.

Sec. 8.7

. Used with lossy compression, compact interoperable images occupy as little as two kilobytes. This

makes them suitable for storage on ISO/IEC 7816 integrated circuit “smart card” identification tokens. In comparison to the other biometric interchange records currently used on such credentials, the IREX record is somewhat larger than standard fingerprint minutia templates (approx. 300

bytes4), but smaller than standard fingerprint images (typically 6 to 10 kilobytes5) and e-Passport

face images (from about 15 to 20 kilobytes6).

Sec. 8

. Compressed iris image sizes are similar to the template sizes measured for many algorithms submitted to the IREX evaluation. Such images can be matched without loss of recognition accuracy.

There are no standards for iris recognition templates, and they are not interoperable. Their size

advantage is small. Standard iris images should be exchanged instead.

Sec. 7.5

. The ISO/IEC 19794-6 standard is application-neutral. Standard images are suitable for iris recognition applications embedding large-scale one-to-many identification searches (watchlist, deduplication, fraud detection), one-to-many token-less verification claims, and one-to-one verification claims with a credential.

. By executing a wide ranging study and reporting detailed results, IREX is expected to influence

technical development of iris recognition algorithms in unforeseen ways.

INTEROPERABLE IMAGE FORMATS

2The appendices include: optional provider-supplied text describing each specific recognition algorithm; comments from the participants

on the IREX activity; and detailed technical information for each specific IREX implementation. The appendices may be downloaded from

http://iris.nist.gov/irex/irex appendices.pdf.

3The implementations were sent to NIST in mid February 2009.

4A INCITS 378 minutia template from a flat index-finger impression containing 38 minutiae encoded in 6 byte format.

5A typical ISO/IEC 19794-4 single finger record, acquired at 500 pixels per inch and WSQ compressed at 15:1.

6A color ISO/IEC 19794-5 token face image, interocular distance 90 pixels, 15:1 compression and chrominance sub-sampling.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

. For applications without size, transport, or communications-related throughput constraints the

uncropped uncompressed KIND 1

7

rectilinear record may be used. Such images, of size 640 x 480

pixels, can be losslessly compressed using the ISO/IEC 15948 Portable Network Graphics compression algorithm (PNG) by about a factor of two. The resulting records occupy a median size of 150

kilobytes.

Sec. 8.7

. If the acquisition process can include a coarse iris detection operation, then the centered crop-only

KIND 3 record almost always gives fewer false rejection errors than its un-cropped, uncompressed,

and unconstrained KIND 1 parent. With lossless compression, KIND 3 instances require 50-80 kilobytes of storage. Instances may be further compressed using the lossy JPEG2000 algorithm. The

crop-only KIND 3 image format should be retained in the ISO/IEC 19794-6 standard.

Sec. 8.2

. The cropped-and-masked KIND 7 image format proposed for the ISO/IEC 19794-6 standard should

be retained and advanced as the primary format for the exchange of compact iris images smaller

than 3KB. At larger sizes or lower compression ratios, the KIND 3 format should be preferred: it is

more easily and safely instantiated. The false rejection performance for some implementations exceeds that of the KIND 1 parent. The cropped-and-masked KIND 7 format is particularly amenable

to lossless compression. This allows iris records to be produced in the 20-40 kilobyte range. This

format should usually only be used in conjunction with the JPEG2000 and PNG compression algorithms.

Sec. 8.2

. The KIND 16 unsegmented polar format proposed for the ISO/IEC 19794-6 standard should be rejected. The recognition error rates associated with the format are much larger than those attainable

with rectilinear KIND 3 and KIND 7 records. This is true natively, when a single provider prepares

and matches the records, and in the interoperable case also, when different providers do so.

Sec. 8.2

. For some images, lossless compression will not be able to achieve a specific target size, and JPEG2000

should be applied at a specific targeted bit rate. For images below about 20KB, lossy JPEG2000

compression will usually be needed.

Sec. 8

. Using false rejection error as a metric, the cropping operation used in preparation of KIND 3 and

KIND 7 records should extend to no closer than 0.6 iris radii from the iris in the horizontal direction,

and 0.2 radii in the vertical direction.

Sec. 8.5

. Iris cameras should not internally apply compression to iris images, unless they are manufactured

for a dedicated, profiled, application in which standardized compressed iris images are produced.

An exception is lossless compression.

. There is a large academic literature addressing the iris localization problem, and a rich diversity of

algorithms can be employed to effect the detection and localization steps necessary to instantiate

KIND 7 and KIND 3 records. Detection and localization are non-trivial operations and are influential

on recognition accuracy. Users should evaluate implementations accordingly.

Sec. 1

RECOGNITION ACCURACY

. False rejection performance depends on the following (in decreasing order of importance): the

recognition algorithm, the particular image dataset, the standard image format, and on the amount

of compression applied. The observed error rate variations span at least an order of magnitude.

Sec. 7.3.1

7For a visual description of the image KINDS evaluated in IREX, refer to Figure 3 on page 19.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

. False match rate (FMR) calibration curves are computed. These confirm the low false match rates

published for iris recognition algorithms at specific operational thresholds. By comparing images

from two occupationally and geographically separated populations, of combined size O(104), the

FMR calibration is based on O(109) comparisons. While this population size extends prior independent studies, larger operational corpora should be leveraged to refine FMR performance estimates

and give statistical significance to FMR measurements on national-scale identification searches.

Sec. 7.4

. The stability of the impostor distribution is measured and reported. At a specific threshold, the

false match rate depends on the dataset. This applies across all formats and compression conditions, including uncompressed and unprocessed iris images. In addition, for most iris recognition

algorithms, false match rates vary under compression. When severe compression is applied to

damage the iris texture, some algorithms maintain low FMR; others do not.

Sec. 7.4

INTEROPERABLE ACCURACY

. Given a standard for iris images, there are two separate tasks for iris recognition: Generation of the

standard image, and matching of standard images (matching is implemented via by proprietary

templates). These tasks will generally be executed by different providers’ algorithms. The implementations that most accurately match standard IREX records are not generally the implementations that prepare the most matchable IREX records. That is, the error rates associated with the

initial detection of the sclera-iris and iris-pupil boundaries are distinct from, but smaller than, the

error rates associated with the end-stage fine-grained localization, feature extraction and matching.

Sec. 8.6

. The interoperability of standardized iris images in IREX is better than that reported for standard

fingerprint minutiae templates: There, the best accuracy was observed when the same provider

generated and matched the enrollment and verification templates. In IREX, this native-mode bias

is small. Instead the iris recognition algorithm (fine-grained localization, feature extraction, and

matching) is most influential on outcome. Fingerprint minutia interoperability is degraded by idiosyncratic (i.e. algorithm-specific) minutia detection and selection[8, 25, 23]. For iris, the standard

interchange medium is image data; for fingerprint minutiae, it is (x, y, θ) point data and the relative

interoperability of the two is a product of the difficulty of consistently and uniformly instantiating

the semantic content of the respective standards.

Sec. 8.6

COMPUTATIONAL EFFICIENCY

. The IREX test plan explicitly stated that algorithm timing estimates would be reported. Further,

IREX encouraged the submission of slow-but-accurate vs. fast-but-less-accurate implementations

within a generous timing budget. The result, across the nineteen IREX algorithms, is that there are

two orders of magnitude for the time needed to prepare an IREX record and to generate a template

from an IREX record, and three orders of magnitude for the time needed to execute a one-to-one

match.

Sec. 7.6

. The more computationally expensive algorithms give fewer recognition errors. This accuracy benefit applies on both the segmentation side, and the matching side. While the results demonstrate

the existence of an industry-wide accuracy vs. time tradespace, not all providers demonstrate such

a tradeoff between their primary and secondary SDKs.

Sec. 7.6.3

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Recommended Target Record Size 

Role Format Compressor 2KB 4KB 8KB 16KB 32KB 64KB 128KB 256KB 307KB 

All KIND 1 Uncompressed 

All KIND 3 Uncompressed 

All KIND 7 Uncompressed 

All KIND 3 PNG Lossless 

All KIND 7 PNG Lossless 

1:N KIND 3 JPEG 2000 Lossy 

1:N KIND 7 JPEG 2000 Lossy 

1:1 KIND 3 JPEG 2000 Lossy 

1:1 KIND 7 JPEG 2000 Lossy 

Figure 1: Application-specific recommendations on compression and format. The horizontal axis shows target file size in kilobytes

on a logarithmic scale.

. Most prior published tests have ignored the tradeoff between computational expense and accuracy. Here more computationally expensive algorithms yield better accuracy. This indicates that

difficult-to-match samples are amenable to more expensive algorithms. While IREX allowed computationally intensive algorithms, providers were given prior notice that this report would report

execution speed in addition to accuracy. NIST biometric testing campaigns have historically emphasized matching accuracy.

Sec. 7.6.4

. The speed of the fastest algorithms is in line with that reported in the academic literature. Sec. 7.6

. All of the implementations are fast enough for use in one-to-one applications. However, in one-tomany identification applications with even moderate enrolled population sizes, the viability of the

slowest IREX implementations may rest on the availability of fast search (e.g., dataset partioning)

algorithms. The degree to which the algorithms in the IREX SDKs can be expedited is not known.

Sec. 7.6.2

. The fastest implementations are more than twice as fast as the slowest: They can compute and

match pairs of templates from both eyes in less time than it takes the slowest algorithms to compute

and match templates from a single eye.

Sec. 7.6.1

. The more accurate implementations may be useful for forensic iris identification where computational expense is usually inconsequential.Sec. 7.6.1

. Important caveats apply to the measurement of computation time, and to the operational relevance

of timing estimates.

Sec. 7.6.4

SELECTION OF COMPRESSION ALGORITHMS

. The IREX study has supported refinement of the ISO/IEC 19794-6 international iris interchange standard. Particularly the IREX study supported exclusion of polar formats from the ISO/IEC 19794-6

standard.

Sec. 8.2

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

. The IREX study has confirmed the findings of previous studies, namely that compression gives low,

graduated, increases in false rejection errors. Prior work, published in the academic literature, has

often considered single non-commercial algorithms running on smaller datasets.

Sec. 8.1.1

. Lossy compression algorithm such as JPEG and JPEG2000 unrecoverably damage iris images. This

has an adverse effect on false non-match error rates, and, for some iris recognition algorithms, on

false match rates too. This latter aspect contraindicates application of lossy compression to images

used in one-to-many searches.

Sec. 8.1.3

. Compression should be applied at the minimum level needed to attain a storage or bandwidth

requirement. The primary operational target variable is the size of the compressed image. This

may be set directly using the JPEG2000 algorithm. Default guidance is given in Figure 1. However,

implementers should quantify compression damage in terms of bits per pixel, and this will depend

on iris radius - large irises should be compressed more lightly.

Sec. 8.6

. The ISO/IEC 10918 JPEG compression algorithm should be deprecated. The presence of Discrete

Cosine Transform blocking artifacts produces elevated false match rates. This recommendation

applies particularly to compact iris images, but also to the cases where JPEG encoding is being used

solely as a convenient container. It is recommended that the lossless PNG compressor be used in

such cases because it is too easy to invoke JPEG with adverse parameters.

Sec. 8.1

EFFECTS OF DILATION, OCCLUSION, CENTER DISPLACEMENT

. Higher amounts of pupillary dilation increase image-specific false non-match and false-match

rates. The effect diminishes when the enrollment images are JPEG2000 compressed.

Sec. 8.9.2

. Images stored in KIND 16 format with constricted pupils produce higher image-specific false match

rates. Perhaps high amounts of constriction make the features more difficult to localize.

Sec. 8.9.2

. The difference between the dilations present in two iris images affects their comparison score. In

particular, large disparities in dilation elevate false rejection error rates. False match rates are not

changed. The magnitude of the effect is comparable to that of eyelid occlusion.

Sec. 8.9.2

. The effect of dilation change on recognition accuracy diminishes when the enrollment images are

JPEG2000 compressed.

Sec. 8.9.2

. Changes in dilation tend to be smaller for intra-person comparisons than for inter-person comparisons. Under controlled illumination conditions, the amount of dilation may serve as an ancillary

discriminating factor.

Sec. 8.9.2

. Large amounts of eyelid occlusion increased the probability of a non-match for most algorithms,

and increased the probability of a false match for some algorithms.

Sec. 8.9.1

. Some IREX algorithms exhibit a small adverse dependence on the displacement of the pupil and

limbus centers. Specifically, genuine matching scores are elevated.

Sec. 8.9.3

IMAGE QUALITY ASSESSMENT

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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. Three SDKs reported iris image quality scores on the standard range of [0-100]. A quality score,

computed during the production of a KIND 3 record, is considered effective if they are quantitatively indicative of matching performance. Quality scores are considered interoperable if they are

effective and uniformly interpretable. Two of three quality assessment algorithms, D1 and G1, are

effective at assigning higher quality values to images with lower image-specific false match and

false non-match rates. SDK D1 exhibits a larger stratification of the median quality scores assigned

to four accuracy-categorized partitions of the ICE dataset.

Sec. 8.8.1

. When images are excluded from testing on the basis of poor image quality assessments by SDKs

G1 and D1, the false non-match rate of almost all matching algorithms improves. The best gain

in accuracy is achieved for SDKs A1, I1, I2, G2, H2, H1. Exclusion of images judged to have low

quality by SDK A1 does not appreciably improve observed false non-match rates.

Sec. 8.8.2

. Quality scores are not interoperable: The distributions of the D1 and G1 quality scores are different

and relate to different image error rates. This lack of interoperability implies a need for calibration.

Sec. 8.8.2

. The IREX test plan did not require image quality assessment. While there is an increasing concensus

that image quality estimates have greatest utility if they they are indicative of recognition accuracy,

the IREX test plan did not mandate a semantic meaning for image quality. The new ISO/IEC 29794-

6 iris image quality standard is intended to improve this situation. Academic and commercial

research, and IREX activities will support this project.

Sec. 8.8.2

EXPANDED TEST METHODS AND METRICS

. Detection error tradeoff characteristics are included that present lines joining points of fixed threshold. These reveal changes in both FNMR and FMR at a fixed threshold. These expose dependencies

of FMR on dataset and compression parameters.

Sec. 7.3

. Within-dataset and cross-dataset impostor comparisons were conducted. The latter guarantee the

integrity of the impostor status of all image pairs. Matching of samples from different cameras is

an implied aspect of applications based on standardized images.

Sec. 7.4

. Conditional false non-match rates were defined and used to quantify errors induced by a change

to a dataset (e.g., compression). This offers a more precise approach to failure analysis when the

covariate is under the control of the experimenter.

Sec. 8.2

. Template sizes were reported. Sec. 7.5

. Processing times were reported for IREX record preparation, template generation and one-to-one

comparison. The computational cost vs. accuracy tradespace was documented.

Sec. 7.6

. Image-specific false match and false non-match error rates were defined by inheriting concepts

from the biometric zoo. These metrics support failure mode analyses by allowing association of

a covariate (e.g., dilation) with a matching error rate without having to consider the covariate of

a comparison image. Image-specific error rates are useful in detection of ground truth errors in

datasets.

Sec. 6.4

. Image-specific error rates were used to create four partitions of the ICE dataset: These are termed

CLEAR ICE, BLUE GOATS, BLUE WOLVES, and BLACK ICE. The latter consists of those images that

have pathological error rates on all SDKs. On request NIST will release the image partitions to

interested parties.

Sec. 8.10

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

. The IREX study defined a “C” API to support the various investigations. This API was implemented

successfully by all the IREX participants. It is published8, freely available, and easily re-usable by

other testing programs. It could be used with its full IREX record functionality, or in a more generic

raster-template-match mode.

NEXT STEPS AND SUPPORT FOR DEVELOPERS

. NIST will consider requests for additional quantitative feedback in support of algorithm development. As part of this process NIST will invite IREX participants to inspect some problematic images.

. NIST invites comment on what further work is needed in support of standardization of iris image

interoperability. Further, NIST solicits input on how the IREX umbrella program might be extended

to support iris image interoperability. Comments and inquiries are welcome via IREX@NIST.GOV.

. NIST will initiate a second activity under the IREX umbrella. The project, the Iris Quality Evaluation and Calibration (IQEC), focuses on evaluation and calibration of iris quality scores. IQEC is a

large scale evaluation of iris quality scores and will commence in Fall 2009. IQEC aims to evaluate

the effectiveness of image quality assessment algorithms IQAA in predicting recognition accuracy

of particular comparison algorithms (from the supplier of the IQAA), and of others’ algorithms.

Given the IREX result that quality scores are not immediately interoperable, IQEC will establish a

calibration procedure of IQAAs.

8See http://iris.nist.gov/irex/irex api.pdf

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

CONTENTS

Acknowledgments 1

Disclaimer 1

Caveats 13

Release Notes 14

Errata and Clarifications 14

Terms and Definitions 16

Chronology 17

1 Introduction 18

1.1 Application scenarios and scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.2 Relation to other nist activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2 Prior studies 22

3 Compression algorithms 23

4 Participation 24

5 Datasets 24

5.1 The ops dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.1.1 The all-failures partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.1.2 Operational relevance of the ops dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.2 The bath dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.3 The ice dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.4 On the role of technology testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

6 Metrics 30

6.1 Treatment of failure to enroll and failure to acquire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6.2 Thresholds for zero fmr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

6.3 Decidability measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

6.4 Image-specific error rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

7 Performance on full-size uncompressed images 34

7.1 Failure to enroll . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

7.2 Effect of iris radius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

7.3 det characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

7.3.1 Relative difficulty of datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

7.3.2 Relative performance of algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

7.3.3 The effect of trading fte for fnmr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

7.4 False match rate calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

7.5 Template sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

7.6 Timing results and accuracy-time tradeoffs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.6.1 Irex record and template generation times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.6.2 Matching times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

7.6.3 Speed vs. accuracy tradeoffs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

7.6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

11
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

7.6.5 can irex algorithms be expedited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

8 Performance of compact formats 55

8.1 Effect of compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

8.1.1 The genuine distribution under compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

8.1.2 Change in the genuine distribution under compression . . . . . . . . . . . . . . . . . . . . . . . . . . 60

8.1.3 The impostor distribution under compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

8.1.4 The effect of compression on fmr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

8.2 Sufficiency of the standard formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

8.2.1 Core sufficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

8.3 Conformance testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

8.4 Variation of image records between sdks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

8.5 The effect of cropping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

8.6 Interoperability of the standard formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

8.6.1 Test method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

8.6.2 Interoperability results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

8.6.3 Comparing kinds 3 and 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

8.7 Effect of lossless compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

8.8 Predictive power of image quality scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

8.8.1 Relationship of quality to image error rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

8.8.2 Error vs. reject curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

8.9 Effect of geometric properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

8.9.1 Eyelid occlusion of the iris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

8.9.2 Dilation of the iris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

8.9.3 Displacement of the iris and pupil centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

8.10 Image error rate estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

8.10.1 Dependence on comparison algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

9 Considerations for overall performance 113

10 Proposed changes to the standards 114

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

12
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

CAVEATS

As with all biometric evaluations, the results of this test must be carefully interpreted before any predictive conclusions

can be made. Users should factor the following into policy, planning and operational decisions.

1. IREX I did not address evaluation or standardization of cameras, interfaces, and complete systems. It does not establish operational requirements, nor does it consider transmission protocols, and security issues such as algorithm

vulnerabilities. These issues, which must be addressed operationally, may impact design tradeoffs based on IREX.

2. The absolute error rates quoted herein were measured by using the provided implementations on three large fixed

corpora of operational and non-operational iris images. As with all offline biometric tests, the relevance of the

results to operational reality must be considered in light of the fact that post-capture samples are used. Error

rates observed in real-world applications are almost always strongly dependent on acquisition related factors.

Generically these include

. The degree to which the design compels, induces, or incentivizes the user to use the camera in a mode intended by its designers,

. Cooperativeness of the user population (an uncooperative subject may be evade acquisition and be very hard

to image, a non-cooperative user may similarly not look at, or properly present to, the camera);

. Environment (e.g., low ambient light levels may impede detection);

. The number of verification attempts allowed (typically more attempts lead to lower false rejection, and higher

false acceptance);

. The number of images used, and the fusion policy (if several images from a sequence are matched, accuracy

can be improved);

. Number of biometric objects (fingers, irides) used, and the fusion policy (two gives better accuracy than one)

. Demographics (e.g., children and older adult populations may not present as quickly or as easily)

. Habituation (Users who regularly interact with system often yield lower rejection rates);

3. The sensor, and the enrollment policy affect error rates. For example, iris cameras almost always compute quantitative quality criteria in an auto-capture loop either in the camera’s firmware, or sometimes in a client-side application, or both. This may produce some failure-to-enroll occurrences, but will improve downstream matching

error rates.

4. With respect to iris recognition specifically, the accuracy and speed of operational transactions will generally depend on a number of factors, including the following.

. The template generation and matching algorithms are strongly influential on error rates.

. The number of eyes imaged

. The number of images available for matching

. The quality of the enrollment procedure particularly whether a verification was done at time of original enrollment

. The communications channel and interface, and the compression of the records it implies.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

13
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

RELEASE NOTES

. The IREX evaluation was conducted in accordance with the IREX API and CONOPS which was developed over

the period October 2007 through September 2008. It was developed in consultation with the public and received

comments from seven different organizations. It imported content on the IREX image formats from documents

submitted to SC 37 in support of ISO/IEC 19794-6 . It is archived at http://iris.nist.gov/irex.

. This document is accompanied by the IREX APPENDICES which present exhaustive results for each submitted iris

recognition algorithm. These may be downloaded at http://iris.nist.gov/irex/irex appendices.pdf.

. The IREX trial has been conducted in broad conformance to the ISO/IEC 19795 - Biometric Performance Testing

and Reporting - Part 4: Performance Interoperability Testing standard.

. Throughout this report the submitted iris recognition algorithms are identified by a letter and a numeral of the

form Nx. The letter identifies the company or university that submitted the algorithm. The numeral x takes the

value 1 for the primary algorithm and 2 for secondary a.

. The use of these codes is intended to conserve space in its many tables. For reference, the letters are associated

with the providers’ names in a running footnote.

. A glossary of terms and definitions is given on page 16.

. Much of the tabulated content in this report was produced automatically. This involved the use of scripting

tools to generate directly typesettable LATEX content. This improves timeliness, flexibility and maintainability, and

reduces transcription errors.

. This PDF file is large. While it is likely to be of better quality in print than on-screen, it may print slowly.

. Readers are asked to direct any correspondence regarding this report to the IREX@NIST.GOV.

aWhile the intent in allowing submission of two algorithms was to explore the tradespace between speed and accuracy, IREX participants were

free to vary the algorithmic functionality in any way they felt appropriate. The IREX SUPPLEMENTAL includes information volunteered by the IREX

participants on their choices.

ERRATA AND CLARIFICATIONS

. 1. October 28, 2009: The green heatmaps appearing in section 7.2 (on the effect of iris radius) were incorrect. The heatmaps show

the count of genuine comparisons broken out by radius of the enrollment and verification samples. The heatmaps erroneously

showed a function of genuine scores. This applies to versions of the IREX report and its appendices issued on or before October

6, 2009. The iris radii themselves have not changed.

. 2. October 28, 2009: The red-yellow heatmaps appearing in section 7.2 (on the effect of iris radius) were incorrect. The heatmaps

show the false non-match rate broken out by radius of the enrollment and verification samples. The heatmap computation

sometimes incorrectly applied the iris radius of a sample that was not the enrollment sample. This applies to versions of the

IREX report and appendices issued on or before October 6, 2009.

. 3. October 28, 2009: A line has been added to section 7.2 (on the effect of iris radius) to indicate that the iris radii are those

estimated by the I1 implementation, as recorded in the header of its KIND 3 records.

. 4. October 30, 2009: The failure to acquire counts in Table 6 for the BATH images were double their true value. However, the

FTE rates were and are correct. This applies to versions of the IREX report and appendices issued on or before October 6, 2009.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

14
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

VERSION CONTROL

Study IREX I

Report generated Fri Oct 30 14:30:21 2009

Report name tex/irex report.tex

Report last modified Fri Oct 30 14:07:34 2009

Report MD5 checksum e54a635a43123e27f08cd118452b9565

NIST contact irex@nist.gov

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

15
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

TERMS AND DEFINITIONS

Table 1 gives IREX-specific definitions to various words and acronyms found in this report.

No. Term Definition

Organizations

1 ANSI American National Standards Institute

2 DHS U. S. Department of Homeland Security

3 DoD U. S. Department of Defense

4 ISO International Organization for Standardization

5 IEC International Electrotechnical Commission

6 INCITS International Committee for Information Technology Standards

7 NIST National Institute of Standards and Technology

8 M1 The standards body that formulates comments toward SC 37 biometrics standards

9 SC 17 Subcommittee responsible for development of identification card standards

10 SC 37 Subcommittee responsible for development of biometrics standards

Programs

11 IREX Iris Exchange - NIST’s umbrella program for supporting iris interoperability

12 MINEX Minutiae Exchange - NIST’s umbrella program supporting minutia interoperability

Standards

13 INCITS 379:2004 U.S. standard for iris images

14 ISO/IEC 19794-6:2005 International variant of the INCITS 379 format, the focus of this report

Data elements

15 Standard template Standardized templates do not exist for iris recognition. IREX is concerned with interoperability based on standard images

16 Proprietary template Usually unpublished feature representation of matchable iris data - comparable only

with a template from the same vendor and product line

17 Enrollment template Synonym for reference template

18 Reference template Template, logically from the enrollment or first-encounter sample

19 Verification template Template generated from a subsequent sample of a subject or from an un-enrolled unknown or impostor sample

20 BDB Biometric Data Block (See SC37’s Harmonized Vocabulary[3])

Function and process terms

21 SDK Software Development Kit

22 API Application Programming Interface

23 Matcher In IREX a matcher is logically a function that compares two IREX records and produces a

dis-similarity score. Physically it compares two templates

24 Generator Software function that accepts an image and produces a standard record

25 Native mode Comparison by SDK X of IREX records prepared by SDK X

26 Interoperable mode Comparison by SDK X of IREX records prepared by SDKs Y and Z

27 Genuine Comparison of data from the same person

28 Impostor Comparison of data from different individuals

29 Verification One-to-one comparison

30 Authentication Synonym for verification

31 Localization Image processing operations to locate the iris or pupil boundaries

32 Segmentation Synonym for localization

Metrics

33 FAR False accept rate (i.e. transactional outcome)

34 FRR False reject rate (i.e. transactional outcome)

35 FMR False match rate (i.e. 1:1 single sample comparison outcome )

36 FNMR False non-match rate (i.e. 1:1 single sample comparison outcome )

37 DET Detection Error Tradeoff characteristic

Table 1: Glossary of IREX related terms

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

16
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

CHRONOLOGY OF IRIS STANDARDIZATION

Table 2 lists the key dates in the evolution of IREX. The table includes items related to the standards.

No. Period Event

1 July 9, 2009 SC 37 Working Group 3 Meeting

2 July 3, 2009 Release of first public draft of this report

3 May, 2009 ISO/IEC 29794-6 - Iris Image Quality is approved

4 June 12, 2009 IREX Phase III report is submitted for release

5 March 30, 2009 NIST comments toward revision of ISO/IEC 19794-6

6 February 2, 2009 Release of first committee draft of ISO/IEC 19794-6 iris image interchange standard, document JTC 1 SC37 N3031

7 February 17, 2009 Deadline for delivery of Phase II SDKs to NIST

8 January 22, 2009 SC 37 Working Group 3 Meeting

9 October 20, 2008 Phase I evaluation begins

10 ... Acceptance testing of Phase I SDKs

11 September 17, 2008 Deadline for delivery of Phase I SDKs to NIST

12 August, 2008 Finalization of IREX CONOPS and API specification

13 November 16, 2008 Release of initial IREX CONOPS and API for comment

14 March 24, 2008 NIST comments toward revision of ISO/IEC 19794-6

15 February 1, 2006 NIST Special Publication 800-76-1 is released

16 September 15, 2005 Publication of ISO/IEC 19794-6 Biometric Data Interchange Format - Iris image

data

17 August 27, 2004 Homeland Security Presidential Directive 12 is signed

18 March 8, 2004 INCITS 379 finalized

Table 2: IREX chronology and related events.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

17
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

1. INTRODUCTION

NIST’s iris interoperability program, IREX, was initiated to support an expanded marketplace of iris-based applications

based on standardized interoperable imagery. The work is primarily conducted in support of the ISO/IEC 19794-6

standard, now under revision. It secondarily supports the upcoming revision of the ANSI/NIST ITL 1-2007 Type 17

standard[44].

Support for the ISO standard: The primary IREX motivation is to support a more robust, interoperable, useful and implementable ISO/IEC 19794-6 standard. The standard defines image formats for exchange of iris images. It allows migration

from the single-provider template-based applications depicted in Figure 2 toward open, multi-provider, applications

based on the standardized image formats presented in Figure 3.

IREX was initiated to give a quantitative basis for the inclusion and exclusion of image formats in the ISO standard. This

work supports the SC 37 Working Group 3 (WG3) and M1.3 committees by specifically embedding conformance, performance and interoperability tests as part of the standards’ development process. IREX was structured as an applicationindependent assessment of the core algorithmic performance of the localization and recognition components.

Toward compact iris images: The second motivation in executing IREX I was the establishment of a standardized accurate, interoperable and compact iris image format suitable for large-scale identity management applications. The IREX

study was intended to avert the situation that arose with the ISO/IEC 19794-6 published in 2005. That standard defined

a non-concentric polar format for compact storage of iris data. It was adopted operationally, and while interoperability problems were never formally demonstrated, two technical contributions to WG3 [Germany, N2059; Great Britain,

N2124] asserted that accuracy of the polar format was critically sensitive to the consistency of the localization, and subject

to sampling problems[53]. The two documents advocated removal of the polar format. A GB contribution[18] usefully

suggested the polar format’s size (around 2 kilobytes) can be achieved via cropping and compression of the rectilinear

format. In addition, the US suggested a new polar variant termed the unsegmented polar format[36] with similarly low

storage potential.

NIST is particularly interested in establishing a set of specifications for efficient transmission of iris imagery across a

network, and for storage on an ISO/IEC 7816 crypto-token. Toward similar ends, iris compression studies have been

conducted [34, 19, 54]. While the studies reported promising results, they explored only the case in which enrollment

and verification data are processed by a lone supplier’s localization and matching algorithms. The exception here is [54]

Non-interoperable Iris Biometric

DETECTION +

SEGMENTATION

Black Box

Proprietary Template

FEATURE 

EXTRACTION

IF IMAGE CAN BE 

RETAINED, TEMPLATE 

LOCK-IN CAN BE 

AVOIDED?

ENROLMENT

DATABASE

Figure 2: Non-interoperable biometrics: If an application does not retain the sample images, the system can only function with the

matching algorithms of one provider, because the templates are non-standardized.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

18
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Cropped image

Cropped and

masked image

KIND 3

KIND 7

Parent image from camera

Unsegmented polar

Reconstructed

rectilinear

KIND 1

KIND 7

KIND 48

KIND 16

Figure 3: The standard image formats tested in IREX. Preparation of the KIND 3 record requires detection of the iris, and a crop

operation to center the iris. The KIND 7 record requires detection of the iris-eyelid and iris-sclera boundaries and a pixel-replacement

masking operation. Finally instantiation of the KIND 16 record requires location of concentric circles inside the pupil and outside the

iris, followed by an rectilinear-to-polar mapping.

which showed similar compression sensitivities for two different matching algorithms. The ISO/IEC 19794-6 standard

was published in 2005. It is almost identical to its precursor, the INCITS 379 standard published in the United States

in 2004. As application-independent standards, neither document establishes normative requirements on compression.

Instead the 2005 ISO/IEC 19794-6 standard’s clause A.1.6 gives the following informative guidance “ a compression factor

of 6:1 or less is recommended”. This is an order of magnitude smaller than compression ratios published in the last two

years[19, 54].

IREX I was intended to support the ISO/IEC 19794-6 by requiring participants to produce conformant instances of the

rectilinear format, and by evaluating the compact formats proposed by the GB and UK national bodies via submission

of technical specifications[19, 18, 36] toward the revision of the original ISO/IEC 19794-6:2005 Iris image interchange

standard. The revision is ongoing in Working Group 3 of the ISO committee on biometrics, SC 37. NIST supported the

effort by defining a syntactic data record based on the 2005 standard and the technical contributions.

Examples of the images are shown in Figure 3 and their properties are summarized in Table 3.

1.1. APPLICATION SCENARIOS AND SCOPE

Many systems operate under biometric data size constraints in their communications infrastructure, their storage requirements, and in the computational cost associated with larger data. In network-based applications the transmission

times are linearly related to the data size. Similarly, in card-based applications the to-card and from-card transfer times

influence the selection of modality (minutia, iris etc.) and the number of instances (two fingers, two irises etc). For IREX

two notional operational scenarios are considered. These cover compression of one or both samples in a comparison.

. Identity credential: A compressed standard iris image is stored on, for example, a ISO/IEC 7816 smart card, and is

compared during authentication against a newly collected uncompressed sample9. This scenario is representative

of cooperative physical or logical access control situations in which the first sample is collected and prepared in an

attended formal enrollment session, and the authentication sample exists only for the duration of the attempt. In

9The term uncompressed in IREX is used to indicate that both that the image is represented as a pixel raster and that it has never before been

compressed.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

19
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Aspect Cropped Rectilinear Cropped + Masked Rectilinear Unsegmented Polar

Identifier KIND 3 KIND 7 KIND 16

Definition The iris is cropped and centered. The iris is cropped and centered,

and the eyelids and sclera are

masked with a uniform pixel

value.

The region between concentric

circles inside the pupil and outside the iris is unwrapped (rect

→ polar)

Defining citation [19, 18] [19, 18] [36]

Standards complianceAs a rectilinear instance it is compliant to ISO/IEC 19794-6:2005

but that standard gave no size

parameter guidance. Allowed in

ISO/IEC 19794-6 revision project

(estimated completion 2010). Allowed in ANSI/NIST ITL 2007

Type 17[44].

As a rectilinear instance it is compliant to ISO/IEC 19794-6:2005

but that standard gave no size

parameter guidance. Allowed in

ISO/IEC 19794-6 revision project

(estimated completion 2010).

A non-concentric polar version was present in ISO/IEC

19794-6:2005. Withdrawn from

ISO/IEC 19794-6 revision project

(est. 2010).

Required localizationCoarse iris detection Fine detection of sclera-iris

boundary and eyelid-iris boundaries.

Coarse detection of pupil and

iris.

Not intended or allowed (but not enforced)Image enhancement Image enhancement Eyelash removal[67], eyelid

masking

Table 3: Summary properties of the image formats tested in IREX.

IREX this scenario is addressed by compression of one sample (the enrollment sample) in one of the three compact

formats, and execution of matching comparisons against uncompressed KIND 1 records.

. Central matching facility: Compressed standard samples are submitted to a central dataset. These are the firstencounter “enrollment” samples. Subsequently, compressed samples are transmitted to the central facility and are

matched against the enrollments. This scenario would be typical in open-universe one-to-many applications such

as visa fraud detection and watchlists. The need for compression is implied by operational network bandwidth

constraints. In this scenario both images might be compressed in one of the standard formats.

Figure 4 depicts a network-centric application in which iris image data is moved over network links (shown as

green arrows) whose bandwidth may vary (as indicated by the width of the arrows).

. Baseline: To examine the effect of compression, a baseline is established by matching uncompressed unprocessed

KIND 1 records against themselves.

With the applications defined, the formal IREX I scope was stated as follows.

. To quantify the performance and interoperability of rectilinear images, the UK-proposed ROI masked rectilinear

images[18, 19], and the US-proposed unsegmented polar images[36].

. To measure the effect of JPEG and JPEG2000 compression on accuracy.

. To quantify the performance and interoperability of iris localization algorithms.

. To time the various operations.

. To formulate record structures and other content toward the revision of ISO/IEC 19794-6.

. TO check that suppliers can produce records conformant to the ISO/IEC 19794-6 standard.

The primary outputs of the test are statements of performance including:

. failure-to-segment rates for various compression levels;

. false non-match and false match error rates for various compression levels and operating thresholds;

. interoperable error rates for comparison of image records prepared by different providers’ algorithms;

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Central 

server and 

First encounter (enrolment) Second encounter (identification)

Field 

unit 1

Communications bridge

Field 

unit 2

Field 

unit 3

Field 

unit 4

K1

K7

K3

K3,K7 Handheld

Identification

Device

Handheld

Identification

Device

Handheld

Identification

Device

K1,K3,K7 K1,K3,K7

Communications bridge

Forward 

provisioning 

unit

K1,K3,K7 TEMPLATE

Forward 

provisioning 

unit

K1,K3,K7

Forward 

provisioning 

unit

K1,K3,K7

TEMPLATE

TEMPLATE

server and 

enrolment 

database

Communications bridge

unit 4

Field 

unit N

K1, K3, K7

Operations center, 

server or desktop 

capture unit

Operations 

center, 

handheld 

unit

Operations 

center, 

PACS unit

Communications bridge

K1,K3,K7

TEMPLATE

Figure 4: Example of a network-centric application in which standardized iris data is stored centrally and in the field. Note that

compact iris images, in KIND 3 and KIND 7 formats, are passed across narrow bandwidth connections. The left-to-right data flow represents the temporal sequence of enrollment then identification. The storage of templates in the handheld units is a defined, localized

use of non-interoperable data. In principle the units could be equipped with iris recognition software from different providers.

. time taken to prepare the various standard images;

. time taken to extract features from the various standard records;

. time taken to match feature-based templates.

As depicted in Figure 3, the IREX evaluation required conversion of:

. raw raster images into ISO/IEC 19794-6 rectilinear images;

. ISO/IEC 19794-6 rectilinear images into cropped iris-centered rectilinear images;

. ISO/IEC 19794-6 rectilinear images into ROI-masked rectilinear images;

. ISO/IEC 19794-6 rectilinear images into unsegmented polar images.

The following were specifically not within the current scope of this evaluation:

. predictions of operational performance;

. sensor usability or security evaluation (IREX was conducted with offline imagery);

. off-angle imagery (other than that incidentally present in the test corpora);

. conformance to already published standards (e.g., ISO/IEC 19794-6:2005).

The work was carried out with close conformance to ISO/IEC 19795-4:2008 Biometric Performance Testing and Reporting Part 4: Interoperability Performance Testing.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

21
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

1.2. RELATION TO OTHER NIST ACTIVITIES

Relation to PIV: The results of IREX may have implications for projects such as the US Government’s Personal Identity

Verification (PIV) program10 which was initiated by Homeland Security Presidential Directive 1211. This mandated the

establishment of a common identification standard for U.S. government employees and contractors. It required interoperable use of identity credentials to control physical and logical access to federal government facilities and systems. In

response, NIST released FIPS 20112 in February 2005, which includes the definition of an identity credential. It specified

the inclusion of data from two fingerprints as a third authentication factor. The format for this information was finalized in February 2006, when NIST Special Publication 800-76 specified the MINEX profile of the INCITS 378 standard for

encoding and formatting of fingerprint minutiae[25].

NIST is considering including specifications for iris biometrics in an upcoming revision of SP 800-76-1. This would

extend a second interoperable biometric authentication mechanism for U.S. government agencies.

Relation to ICE and MBGC: The IREX activities are distinct from NIST’s prior Iris Challenge Evaluations (ICE)[50] and

ongoing Multiple Biometric Grand Challenge activities which have more basic research goals.

2. PRIOR STUDIES

IREX follows a number of studies on the effects of compression on iris recognition accuracy. These are detailed below.

Such studies seem necessary for all biometric modalities, and as as noted by Daugman[19], this work has been done for

fingerprints as far back as 1993 for 197pixels per centimeter WSQ certification, and more recently for JPEG2000 applied to

394ppcm scans.

University of Bath In 2008 Rakshit and Monro [54] applied the Monro[47, 46], Tan[39, 40] and Masek[42, 41] iris recognition algorithms to 2156 images of 308 different eyes contained in the extended CASIA dataset[12]. They used both

JPEG and JPEG2000 (Kakadu) compressors at bits rates from 1.0 to as low as 0.1 bits per pixel. They reported DET

characteristics with FMR as low 0.0001. Additionally they gave the dependence of “FMR at the first false rejection”

on bits-per-pixel and advanced this as their preferred metric. Compression was applied to both the enrollment and

verification images.

University of Cambridge In 2007 Daugman and Downing[19, 18]13 applied their iris recognition algorithms to 1425

images of 124 people from the ICE 05 dataset[50]. All images were cropped to 320x320, centered, and compressed

with JPEG (IJG14) and with the JASPER implementation of JPEG2000 15. They executed 12214 genuine and 1002386

impostor comparisons, and reported performance using DET characteristics with FMR as low as 0.00001, and with

decidability measures (see section 6.3). Compression was applied to both the enrollment and verification images.

U.S. Naval Academy In a pair of papers[34, 33] Ives et al. applied the Masek[42, 41] recognition algorithm to 756 images

of 108 eyes present in the CASIA[12] dataset, and to 20 images of both eyes of 50 people in the University of Bath

dataset[45]. The CASIA images were resized to 320x280. The CASIA images were compressed at ratios from 5:1

to 50:1. The BATH images, which had previously been compressed using JPEG2000 at 16:1 in their full 1280x960

10See http://csrc.nist.gov/piv-program/

11The text of HSPD 12 is here: http://www.whitehouse.gov/news/releases/2004/08/20040827-8.html

12See Federal Information Processing Standards Publication 201, Personal Identity Verification for Federal Employees and Contractors and related documents here: http://csrc.nist.gov/piv-program

13The authors are participants in IREX .

14The Independent JPEG Group’s implementation is freely available from http://www.ijg.org/, downloaded June 22, 2009.

15The source code is available from http://www.ece.uvic.ca/mdadams/jasper/, downloaded June 22, 2009.  ̃

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

native format, were compressed from 20:1 to 50:1. They used both JPEG and JPEG2000 (Jasper) implementations,

in both lossy and lossless mode. They conducted 2268 genuine and 283122 impostor comparisons with the CASIA

images and, respectively, 19000 and 1980000 comparisons with the BATH set. The study reported first and second

order statistics for the Hamming distance distributions and stated performance in terms of equal error rate and the

true accept rate at the threshold that gave the lowest combined error count. The results show elevation of genuine

scores but insensitivity of impostor scores under compression. For CASIA, this results in elevation of equal error

rate, but this is not observed for BATH even at 50:1 compression.

International Biometric Group As part of their ITIRT study of camera interoperability[32], IBG applied JPEG2000 compression to subset of the collected images and executed approximately 4600 comparisons with 24 enrolled samples.

They observed changes in more than 99% of the Iridian recognition engine’s Hamming distances (HD) under compression, noted that HDs tended to decrease, and attributed this to loss in quality, changes in image localization,

and format conversion. However within the 6:1 lossy compression limit established by the INCITS 379 there was

little impact. The investigation did not describe the effects separately for genuine from impostor comparisons.

Carinthia Technology Institute In 2007 Matschitsch, Tschinder, and Uhl reported[43] on the performance of the Masek[42,

41] algorithm as applied to seven CASIA 1.0[12] images from 20 persons. The study considered five compression

algorithms JPEG, JPEG 2000, SPIHT, PRVQ and a fractal method. The authors advocated peak signal-to-noise ratio

(PSNR) as a measure of compression damage. The study reported the dependence of genuine and impostor scores

on the number of bits per pixel, and concluded that FMR is unaffected by compression.

3. COMPRESSION ALGORITHMS

IREX made use of three compression algorithms. These were applied to images by NIST in a compression-decompression

fashion so that the IREX SDKs were never exposed to a compressed data stream and were therefore not required to supply

or call compression libraries. This removed any chance of compression-related interoperability problems.

Three compression algorithms were used. The first is lossless and has zero effect on accuracy. The remaining two are

lossy algorithms and will effect accuracy.

PNG The PNG specification was finalized as a W3C Recommendation on 10 November 2003. This second edition was

formally standardized as ISO/IEC 15948:2003. PNG is an extensible file format for the lossless, portable, wellcompressed storage of raster images. It provides a patent-free mechanism to store greyscale (or color) iris images

with pixel depths on [1, 16] bits per component. PNG is allowed in the ISO/IEC 19794-6 revision. IREX used version

1.2.10 of libpng16.

JPEG The venerable JPEG algorithm is widely used for non-biometric purposes. It is formally standardized as ISO/IEC

10918-1 and ITU-T Recommendation T.81. IREX used version 6b of the Independent JPEG Group’s implementation.

It is freely available17 and very widely used.

JPEG 2000 The JPEG2000 standard is formally standardized as ISO/IEC 15444. It has many advanced features and is

well suited for lossy compression of biometric data18. For IREX we used the JASPER implementation in its default

configuration.

16See http://libpng.org.

17From http://www.ijg.org/, downloaded June 22, 2009.

18See http://www.jpeg.org/jpeg2000/.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Organization Letter KIND 1 KIND 3 KIND 7 KIND 16 Elliptical models Scalar image OS

Name Code (required) (required) (optional) (optional) iris + pupil quality

Sagem A 2 2 2 2 2 2 Win.

Cogent B 2 2 2 2 0 0 Win.

Cross Match C 2 2 2 0 0 0 Linux

Cambridge D 2 2 2 0 0 2 Linux

L1 ID E 2 2 2 0 0 0 Win.

Retica F 1 1 1 1 0 0 Win.

LG G 2 2 2 2 0 2 Win.

Honeywell H 2 2 2 2 2 0 Linux

Iritech I 2 2 2 2 2 0 Linux

Neurotechnology J 2 2 2 2 2 0 Win.

No. Suppliers 10 10 10 10 7 4 3

No. SDKs 19 19 19 13 8 6

Table 4: IREX provider participation and functionality.

Dataset ID Origin Number of subjects Number of images Camera Citation

OPS An operational set 8160 32640 Pier 2.319

ICE Extract of Notre Dame 2005-

2006 ICE images

240 60000 LG IrisAccess 220020

BATH University of Bath UK 800 Dedicated [45]

Table 5: Summary of the IREX datasets.

4. PARTICIPATION

Participation in IREX was open worldwide, to individuals, universities, non-profits, technology providers and technology integrators. The deadline for a declaration of intent to participate was July 7, 2008. Ten organizations elected to

participate, as shown in Table 4. There was no fee to participate. There were no qualification criteria for entry other

than the ability to send an implementation conformant to the API specifications. There was no requirement to have any

membership in any standards body. There was no requirement to sell or otherwise reproduce any of the IREX technology

to other parties.

. The IREX participants are identified by their full name in Table 4, and by a letter code and an abbreviated name in

the running footer of each page.

. The test was conducted in three phases. The first was an iterative validation and correctness phase intended to

assure both NIST and the participant that the SDKs were operating correctly. The second was intended as the

definitive IREX test. It was supplanted with the third and final phase when certain unrepresentative failures and

conformance problems were resolved.

5. DATASETS

The IREX study employed data from three collections of iris images. These are summarized in Table 5 and described in

the following subsections.

5.1. THE OPS DATASET

The operational dataset consists of two captures of the left and right irises of 8160 individuals. This gives a total of 32640

distinct images. The images were collected using the PIER 2.3 camera from Securimetrics, now a division of L1 Identity

Solutions. The files were extracted from a large multimodal dataset, according to a fixed criterion. This was applied by

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

the provider of the data. The authors did not have any role in the selection process. The selection criteria were such that

a person was included in the IREX partition if the following logical expression was TRUE.

RULE 1 and (RULE 2 or RULE 3 or (RULE 4 and RULE 5))

Where

RULE 1 the subjects ten-print fingerprints were matched by an operational AFIS system21 at some threshold

RULE 2 the subject’s pair of left eyes matched with iris recognition algorithm X at a score below threshold τ1

RULE 3 the subject’s pair of right eyes matched with iris recognition algorithm X at a score below threshold τ1

RULE 4 the subject’s pair of left eyes matched with iris recognition algorithm X at a score below threshold τ2

RULE 5 the subject’s pair of right eyes matched with iris recognition algorithm X at a score below threshold τ2

It is known that τ2 > τ1. The provider of the OPS dataset stated that using X with the τ1 and τ2 threshold produced “zero

FMR” in O(1010) comparisons. The authors assume that

. this applied to person-pairings, i.e. a second (L,R) pair was bound to a first (L,R) pair with zero false pairings;

. ground truth was defined by the AFIS implementation; and

. single images may still come from different persons because of RULES 2 and 3.

The algorithm X was supplied by an IREX participant, Y. The identity of Y is not disclosed in this report. The image

localization, feature representation and matching algorithms used in X are unknown. The nature of the IREX algorithms

from provider Y (and all other providers) are also not known (IREX is a black box test). While the known use of an

iris recognition algorithm in the construction of a test dataset is not best-practice, it is allowed by the ISO/IEC 19795

standards provided the practice is disclosed. Nevertheless, this use raises the follow questions:

. Did the use of X-selected data bias IREX toward Y? The answer is that no dominant bias is apparent. This answer is

provided with no explanation beyond empirical observation that the performance measurements attributed here

to Y are never amongst the best.

. Did the use of X-selected data bias IREX toward providers using similar algorithms and feature sets to those used

in Y? The answer is unknown because the authors have no knowledge of any of the SDK feature representations.

However, if we assume that some IREX implementations have an algorithmic lineage similar to that of X (via

licensing, for example), and that failures are not matcher independent, then yes a bias may be introduced.

. Did the involvement of Y with the provider of the OPS dataset improve Y’s capability? This in unknown to the

authors because we know nothing of the relationship between Y and the OPS provider.

In summary, the application of the X algorithm to the selection process means that recognition accuracy is likely to be

high - more specifically that the iris left and right eye pairs are matchable (at some threshold). Thus we anticipate that any

L-R fusion procedure should give error rates of zero for iris recognition algorithms of similar capability to X. Critically,

however, this does not hold for single images, and non-zero matching error rates should be expected and are, in fact,

observed. Given these factors, the authors used the dataset because:

. The images are likely to be more representative of enrollment samples in which care had been taken to produce

a pristine and matchable image. This makes the images suitable for the compression aspects of IREX because an

identity credential would normally be populated with such images.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

. The purpose of the IREX test is not prediction of operational performance - the IREX goals are to give quantitative

support to the ISO/IEC 19794-6 standard, and to the implementers and adopters thereof. The dataset is suitable for

demonstrating changes in performance (e.g., under compression, or across algorithms).

. The population size, at 8160, is a factor of 34 larger than the ICE dataset, and while this is invaluable to capture the

natural variation between persons in order to better characterize false match performance, it remains too small to

support robust quantitative estimation of false match performance in national-scale 1:N applications.

. The ICE dataset has its own detracting properties (see section 5.3).

. Images from the ICE dataset have been disseminated publicly and thus IREX providers perhaps anticipated and

tuned to that kind of image. The IREX test plan hinted that these images might be used by including a code for the

LG2200 camera in the published IREX API document. These disadvantages motivated use of the OPS dataset.

5.1.1. THE ALL-FAILURES PARTITION

Given its provenance, the OPS dataset might be considered easy: many of the images will never be involved in a failed

comparison. This affords an opportunity to produce a smaller dataset in which errors are concentrated. This subset was

constructed to reduce the computational cost of the measurements of cross-supplier interoperability given in section 8.6.

The data construction procedure was as follows. Given N = 16320 genuine comparisons, find the set Bi for SDK i of

KIND 1 vs. KIND 1 comparisons that are falsely non-matched at the default threshold 22 of FMR = 10−4. The resulting

dataset is then the union B = B1 ∪ B2... ∪ BN .

This is comprised of 1335 genuine image pairs from 1144 subjects. Unless otherwise stated, these are used by taking the

first image of the pair and comparing with all members of the OPS dataset including the second mated member of the

pair.

5.1.2. OPERATIONAL RELEVANCE OF THE OPS DATASET

The fact that the IREX set of the operational OPS dataset has already been matched (at some threshold) means the images

are clean - false rejection will be less frequent than if no matcher had been used. That said, if the original OPS collection

policy had embedded a matching phase23 then localization failures on the resulting corpus would be much rarer and

performance is likely to be better than for a collection that did no such thing.

5.2. THE BATH DATASET

NIST was provided with images of individuals collected by the University of Bath in the United Kingdom. The images

were collected[45] using a computer vision camera (not a commercial iris product) at a high resolution such that the

uncompressed greyscale eight bit raster images had size 1280 x 640 pixels across the peri-ocular region. The main dataset

is comprised of 29525 images from 800 individuals. This does not include the images held in directories labeled NonIdeal

which were ignored throughout.

All of the raw images were downsampled to 640 x 480 via 2 x 2 neighborhood averaging. This made the images conformant to the IREX test plan specification. The images were then passed to the I1 SDK to prepare KIND 3 instances. The

record headers included iris diameter estimates, the histogram of which is given in Figure 5. The distribution is clearly

bimodal. Images with an iris diameter in excess of 340 pixels were omitted from the IREX sample. The effect of this

operation reduced the number of images to 23055 and the number of subjects to 664. This is what is used for all IREX

analyses.

22The default threshold is that computed using all OPS vs. OPS and OPS vs. ICE comparisons of uncompressed KIND 1 images.

23For example, consider an enrollment process that includes capture of three images of an eye. If these samples are immediately matched to produce

comparison scores, s12, s13, s23, then the sample with the lowest sum (m1 = s12 + s13, m2 = s12 + s23, and m3 = s13 + s23) could be retained.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

200 250 300 350 400 450 500

0.00

0.01

0.02

0.03

0.04

0.05

Iris Diameter

Density

OPS

ICE

Bath

Figure 5: Histograms of the diameters of the irises in the images of the three IREX datasets. BATH images with iris diameter in excess

of 340 pixels were not used in IREX.

Without the 340 pixel cap, some SDKs gave very high template generation failure rates because (we assume) the iris

detection software was not configured to find large irises, or because insufficient margin surrounded the iris. Note that

even if a template was produced, it may embed an incorrect segmentation which would usually lead to false non-match

errors.

For the set actually used in IREX larger error rates were observed for atypically sized irises (see section 7.2).

5.3. THE ICE DATASET

This set of data was provided to the authors by the MBGC program[24]. Although the data was sequestered at the time

IREX commenced, a representative disjoint set of images had been released under the ICE 05 development program.

The ICE corpus used in IREX consists of a left and right iris images collected from a university population over six

semesters running from 2004 to 2006. The images were formally described[49] thus:

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

The ICE 2006 images were acquired using an LG EOU 2200 iris scanner. The LG EOU 2200 is a complete acquisition system and has automatic image quality control checks. By agreement between U. of Notre Dame and

Iridian, a modified version of the acquisition software was provided. The modified software allowed all images

from the sensor to be saved under certain conditions, as explained below.

The iris images are 480x640 in resolution, see Figure 2 [suppressed]. For most “good” iris images, the diameter

of the iris in the image exceeds 200 pixels. The images are stored with 8 bits of intensity, but every third intensity

level is unused. This is the result of a contrast stretching automatically applied within the LG EOU 2200 system.

In our acquisitions, the subject was seated in front of the system. The system provides 32 recorded voice prompts

to aid the subject to position their eye at the appropriate distance from the sensor. The system takes images in

“shots” of three, with each image corresponding to illumination of one of the three infrared (IR) light emitting

diodes (LED)s used to illuminate the iris.

For a given subject at a given iris acquisition session, two “shots” of three images each are taken for each eye,

for a total of 12 images. The system provides a feedback sound when an acceptable shot of images is taken. An

acceptable shot has one or more images that pass the LG EOU 2200’s built-in quality checks, but all three images

are saved. If none of the three images pass the built-in quality checks, then none of the three images are saved.

At least one third of the iris images do pass the Iridian quality control checks, and up to two thirds do not pass.

A manual quality control step at Notre Dame was performed to remove images in which, for example, the eye

was not visible at all due to the subject having turned their head.

The use of these images proved controversial in the ICE 2006 evaluation because the suppression of the camera’s quality

control apparatus caused operationally non-representative images (e.g., eyes closed, non-axial gaze, blur) to be present

in the dataset. The presence of degraded images adversely affected iris recognition accuracy, and while larger error rates

give better statistical significance to FNMR estimates, the test results are have less relevance to operational reality.

The authors’ view is that whether or not to use ICE images depends on the purpose:

. If the purpose of the test is to determine the most capable fully automated “lights-out” processor of iris images

then inclusion of such data is worthwhile and defensible. This might be more representative of applications that

include a lightly constrained capture application.

. If the purpose is to assess the effect of compression on such images, or to assess the viability of algorithms on the

core iris feature extraction and matching problem, then results from such images should be discounted.

. If, on the other hand, the aim is prediction or representation of operational performance then the images are suitable

only to the degree that they quantitatively represent the geometric and photometric properties of those in the

intended application. For contemporary mainstream identity management applications, that representativeness is

very poor because of the subversion of the quality control apparatus, the presence of interlacing artifacts24, the use

of contrast stretching[17] and, not least, because the LG 2200 has been obsolete for several years25.

5.4. ON THE ROLE OF TECHNOLOGY TESTING

The following topics are advanced for consideration when reviewing the scope of the IREX results.

. Operational relevance: The predictive value of offline tests for operational performance is limited to cases where

the actual operation is adequately reflected by the images themselves. Such is the case for latent fingerprint images

24The ICE images do exhibit interlacing artifacts - it is not known whether these arose in the camera itself or in ancillary sampling equipment.

The magnitude of interlacing artifacts is presentation (and hence subject) dependent. It can reduce the vertical resolution by two times, and lead to

segmentation difficulties.

25Note, however, that legacy databases may exist.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

which are collected and used in an offline test exactly as they might be in an operational AFIS search. But a technology test is poorly suited for estimating the operational aspects of a cooperative access control application in which

a user interactively presents one, or conditionally, two eyes (or fingers) to the system which, in turn, responds with

prompts, feedback, and interim and final pass-fail decisions. The conventional approach to testing such a system

is to run a scenario test26 in which the intended application is modeled with a sufficiently large population of live

subjects who attempt to use the systems in the intended manner. This was the practice in the DHS-funded ITIRT

study[32].

. Comparative testing: Offline tests are, however, very well suited for comparing the algorithmic performance

and properties of the core technologies embedded in systems. Offline tests proceed by running provider software

on sequestered archived biometric data. This allows massive datasets and populations to be used - this affords

statistical significance. More importantly it provides for a level playing field in which competing implementations

are evaluated in an identical manner. Critically the tests are repeatable, as required by the scientific method.

. Blind testing: Tests like IREX, which work on sequestered sets of biometric samples, often proceed without any

prior dissemination of training data to the test providers. This places developers in the invidious position of having

to ship test SDKs which must be capable of processing images without having been specifically built, trained or

parametrized to do so. This is a frequent refrain heard from prospective NIST test participants (for iris, and other

modalities) and indeed at least one IREX provider addresses this issue in the respective IREX APPENDIX. The blind

nature of testing undermines the representativeness of the test results to operational reality where, it is assumed,

the software could be tailored in the design phase or in actual operation.

The counterargument, however, is that offline tests conducted in this manner constitute a forcing function for

development of algorithms that are tolerant to known image variations. This aspect, universal interoperability and

camera-matcher independence, is explicitly the aim of a standard interoperable iris image format, and the IREX

test is well motivated in this respect. That said, the authors consider that the industry would be better served if

standard reference datasets were available to developers.

. Provider bias: A further question on bias occurs because the manufacturer of the PIER camera, L1, used for

the collection of the OPS dataset is also an IREX participant27. Similarly the manufacturer of the camera used for

collection of the ICE images, LG Iris, is also an IREX participant.

In each case the camera manufacturer is theoretically afforded an advantage in that they would likely have had an

opportunity to gain experience in developing algorithms for these images. However a set of LG 2200 images, from

ICE 05, were released by NIST and in principle available to all IREX developers.

Note also the discussion, in section 5.1, on possible bias introduced for a specific provider (and a specific class of

algorithms) by use of the OPS dataset.

. Comparison to other modalities: There is considerable interest in what modalities offer the best recognition

performance - “what is the best biometric”? This is usually taken to mean which biometric is most discriminating

between people (as needed in one-to-many applications, and in very low FMR one-to-one cases). Probably the

best method for comparing modalities on this aspect is to apply contemporary algorithms from each modality

to operationally collected or representative images or signals. The IREX-style technology tests are well suited to

assessing performance on large populations.

26While the international standard for scenario testing ISO/IEC 19795-2 does not (and cannot) require retention of captured images, it would be

clearly worthwhile in the case described.

27More specifically the camera is made by Securimetrics a division of L1 Identity solutions, and the IREX software was submitted by the Biometric

Research division of L1.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

29
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

6. METRICS

The direct and proper way to quantify accuracy and interoperability is in terms of false non-match and false match error

rates, FNMR and FMR. The quantities are computed empirically. If d denotes a matcher dissimilarity score obtained by

comparing two samples from the same person, and M(τ ) is the number of such scores above threshold, τ ,

M(τ ) = X

d∈G

H(d − τ ) (1)

where G denotes the set of all genuine comparison scores. and H(x) is the step function defined here as

H(x) = (

0 x ≤ 0

1 x > 0

(2)

The inequality placement is unconventional (for the Heaviside step function) and is used so that scores equal to the

threshold correspond to acceptance. FNMR is then the fraction of genuine comparisons for which the score is above the

operating threshold:

FNMR(τ ) = M(τ )

M(−∞)

(3)

where M(−∞) is just the number of genuine comparisons considered. Likewise, when d denotes a score obtained by

comparing samples from different persons, and N(τ ) is the number of such scores below threshold, τ ,

N(τ ) = X

d∈I

1 − H(d − τ ) (4)

where I denotes the set of all impostor scores. FMR is then the fraction of impostor comparisons resulting in a score less

that or equal to the operating threshold:

FMR(τ ) = N(τ )

N(∞)

(5)

where N(∞) is the number of impostor comparisons conducted. In 1:N negative identification applications (e.g., watchlist, duplicate detection) FMR measures the rate at which a search sample is incorrectly associated with an enrolled

sample. In 1:1 positive authentication applications FMR is regarded as a measure of security, i.e. the fraction of illegitimate matching attempts that result in success. In any case, these error rates must be understood as being matching error

rates, not transactional rates. The ISO/IEC SC 37 Working Group 5 has established different terms for these rates: FMR and

FNMR refer to comparisons of single samples, while FAR and FRR apply to the outcome of a human-system transaction

in which a user might, for example, make multiple attempts with multiple eye presentations.

6.1. TREATMENT OF FAILURE TO ENROLL AND FAILURE TO ACQUIRE

The previous section defined error rates in terms of the logical comparison of samples, without considering the intermediate templates. The failure of an implementation to produce a template (or an intermediate image) from an input parent

image may be elective (e.g., an image is assessed to have unusable quality) or otherwise (e.g., the software crashes). In

any case, a testing laboratory, seeking to establish a level playing field in a comparative test, cannot ignore such events

because downstream matching accuracy can be improved if they are used to exclude the worst images from the matching phase. The ISO/IEC 19795 standard, Biometric Performance Testing and Reporting - Part 1: Principles and Framework

addresses this issue by observing, “Comparison of systems having different failure to enroll rates may require use of

generalized false reject and false accept rates which combine enrollment, sample acquisition and matching errors”. It

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

30
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Figure 6: For preparation of KIND 7 records from ICE images, one IREX SDK fails to produce templates from verification images at

rate FTE = 0.0085 and fails to produce templates from enrollment images at rate FTE = 0.0145. The figure shows: As the middle solid

line, the DET that includes the effects of template generation failure; As the bottom dashed line, the DET obtained for matching of the

properly generated templates only. The top dotted line is the result of apply equations 6 and 7 to bottom line with the scalar FTE and

FTE values. The green links join points of constant threshold. A non-vertical link indicates a change in FMR. All results apply to native

operation, and the effects of FTE are included.

continues by including a requirement to report how error rates are generalized in comparative testing.

Consider a full comparison of all samples in an enrollment set with all those in a verification set. If the fraction of

missing enrollment instances is the failure-to-enroll rate, FTE, and the fraction of missing verification instances is the

failure-to-acquire rate, FTA, the generalized Type I and II performance metrics are

GFAR(τ ) = (1 − FTE)(1 − FTA)FMR(τ ) (6)

GFRR(τ ) = FTA + (1 − FTA) [FTE + (1 − FTE)FNMR(τ )] (7)

The second formula says that an individual is falsely rejected if either the verification sample resulted in an FTE or, if it

didn’t, that either the enrollment sample failed to enrolenroll or, that the two samples produced a false non-match. If,

for example, an IREX algorithm gave FTE = FTA = 0.012, then for an operating point (FMR , FNMR) = (0.0001, 0.01), the

generalized rates become (GFAR, GFRR) = (0.000098, 0.0336). This is shown in Figure 6. The failure to produce 1.2% of

the templates adds almost double that to the false rejection rate. This is because when f = FTE = FTE is small, equation

7 is approximately GFRR = 2f+FNMR reflecting the absence of enrollment and verification samples.

The formulae assume that samples are used in equal numbers of genuine comparisons. For OPS this is correct, but

for the ICE and BATH datasets where subjects have differing numbers of images, samples are used in varying number

of comparisons and the formulae do not hold. This is illustrated in Figure 6 which shows three DET characteristics.

The center, solid, plot treats comparisons for which one or both templates were not produced as producing a high score

which guarantees rejections). The lower, dashed, plot ignores such events; and the top dotted line is the result of applying

equations 6 and 7. The green links join points of fixed threshold. The conclusions are that false acceptance varies little,

but that false rejection varies considerably, and that the formulae overestimate the genuine error rates.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

31
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Given this discussion, IREX, unless stated otherwise, accounts for this tradeoff by regarding any comparison involving

missing enrollment or verification templates as one that would produce a comparison score of, essentially, ∞. This

maintains existing practice in NIST tests.

6.2. THRESHOLDS FOR ZERO FMR

The attraction of iris recognition, as articulated in the literature, is that the impostor distribution is stable enough and

sufficiently well characterized that a threshold may be set to give known, and very low, false match rates. While it is the

case with all biometric algorithms and modalities that the operating threshold can be set to give FMR values arbitrarily

close to zero, there are three practical consequences of doing so:

1. Elevated FNMR , such that for some modalities and applications, the false rejection rates may be so high that the

threshold is utterly untenable. For some applications a high FNMR may be tenable - for example in a watchlist

application[1] in which it is unacceptable to falsely detain someone, a 60% FNMR may still be attractive if the FMR

can be maintained near zero. In contrast a physical access control system configured to give zero FMR might produce unacceptable FNMR and this may result in the decommissioning of the system. In iris recognition specifically,

FNMR may be elevated if the sensing technology is insufficient to faithfully capture and digitize the iris. This has

been demonstrated to occur

. when short wavelength light (e.g., visible) is absorbed by the melanin pigmentation of dark eyes such that the

iris structure is not revealed,

. when subjects are imaged far from the focal plane,

. when subjects’ gaze is not on the optical axis, or

. when the iris is occluded by glasses or patterned contact lenses.

Commercial iris recognition cameras are specifically designed to mitigate all of these effects.

2. If the impostor distribution changes with respect to, for example, environmental changes or changes in the imaging

system, the FMR may change.

3. Extreme value effects: Given long enough operation, the system sooner or later will report a false match. This

depends on just how low (or high, for fingerprints) that the threshold is set. The problem exists in one-to-many

systems in which new persons are continually enrolled without change of threshold. The threshold should generally be set depending on the size of the enrolled population, a step normally intended to render a fixed selectivity,

i.e. a constant number of false matches (typically zero)[16].

The calibration results in section 7.4 are intended to assist users in setting thresholds for very low FMR .

The practice of setting FMR to 0.0001 is not intended as any indication of a suitable operating point. As with other

biometric modalities the threshold should be set according to application requirements.

6.3. DECIDABILITY MEASURES

A measure of the separation of the genuine and impostor distributions is the d-prime measure

d

0 = q

μI − μG

σ

2

I +σ

2

G

2

(8)

defined in terms of the mean μ and variance σ

2 of the impostor (I) and genuine (G) distributions. This is appropriate

when the distributions are Normal but is less relevant otherwise because it does not capture the functional form at the

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

32
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

overlap of the tails. Nevertheless, we use the following related quantity to quantify how compression causes genuine

scores to degrade and move closer to the impostor distribution.

d

0

(C) = μI − dG(C)

σI

(9)

where dG(C) represents a genuine dissimilarity score involving an image lossy compressed with parameter C. This

quantity is the number of standard deviations the genuine score lies from the impostor distribution.

6.4. IMAGE-SPECIFIC ERROR RATES

It is known that different users exhibit different levels of recognizability in biometric recognition systems. Some people

are easy to recognize, while others can impersonate or be impersonated. The literature makes the analogy between the

various Type I and Type II error rate heterogeneities and a biometric zoo.

The issue of performance variability among different users was first addressed by Campbell et al.[11]. Later, Doddington

et al.[21] developed a statistical framework to identify four categories of speakers based on the recognition error of each

speaker. Specifically, they introduced:

. Sheep - users who are recognized easily

. Goats - users who are particularly difficult to recognize

. Lambs - users who are particularly easy to imitate

. Wolves - who are users that are particularly successful in imitating others.

Others [62, 64, 59, 29] have investigated the existence of a biometric menagerie in face and fingerprint recognition systems. More recently Yager and Dunstone[66] introduced four new groups of animals. Recognizing the user-dependent

performance variability, Poh et al.[52] ranked users based on the strength of their performance and used that information

to do fusion on a per-user basis.

This non-uniform performance is of interest to the designers of biometric recognition systems. The difficult-to-recognize

users are responsible for the major share of biometric errors. Goats contribute to FNMR but this poor performance in

genuine comparisons does not necessarily elevate FMR. Goats are particularly problematic in access control systems

where reliable, convenient, verification of users is the main interest (ı.e. low FNMR is desirable). Wolves and lambs

adversely affect the security of biometric systems by contributing to the FMR. Their biometric samples tend to match

impostors, or be matched by impostors.

Similarly, different images of the same subject could exhibit different levels of matchability. Image performance variation

is often ascribed to the capture device (e.g., different physical imaging properties of sensors), the environment (e.g., low

light) or the user (e.g., squinting), and the thrust of research is therefore to make recognition algorithms more tolerant

of such variations. Stated another way, algorithms that bound or constrain FNMR and FMR are more reliable and secure.

To reduce false non-matches and improve reliability, it is a common policy to allow multiple acquisitions of the same

biometric at the time of authentication (e.g., to re-acquire after a moistening of the finger). Dealing with false match

occurrences, however, is a more difficult problem. In operational verification systems, false matches are likely to be

undetected; in identification systems they lead to spurious entries on candidate lists and these elevate workload.

To examine performance variation among different images, we define the following image-specific error rates

. Image false match rate, iFMR - the proportion of comparisons for which an image produces false matches (i.e.

non-match comparisons at or below the operating threshold).

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

33
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

. Image false non-match rate, iFNMR - the proportion of comparisons for which an image produces a false nonmatch (i.e. genuine comparisons above the operating threshold).

Specifically, if we define s

ij

kl to be the comparison score of the k-th image of subject i with the l-th image of subject j then

the set of impostor scores of the k-th image of subject i is

I(i, k) = { s

ij

kl , i 6= j , j = 1 . . . J , l = 1 . . . Nj} (10)

for comparison against all Nj images of all J persons in an enrolled set. The image false match rate is then defined as

iFMR (τ, i, k) =

P

s∈I(i,k)

1 − H(s − τ )

P

s∈I(i,k)

1

(11)

where H(s) is the step function of equation 2. If the threshold is set to τ in the conventional manner (i.e over some large

cross comparison set) to give a global FMR of f, then the general case is that iFMR 6= f.

For the image false non-match rate, we use the set of non-self genuine scores of the k-th image of subject i

G(i, k) = { s

ii

kl, l = 1 . . . Ni

, k 6= l } (12)

to compute

iFNMR (τ, i, k) =

P

s∈G(i,k) H(s − τ )

P

s∈G(i,k)

1

(13)

where H(x) is again the step function of equation 2.

Unless otherwise stated iFMR and iFNMR are computed for each comparison algorithm by substituting comparison scores

of the algorithm in equations 11 and 13 above using the following datasets.

. The threshold can be set to any value. Here it is set over all OPS - to - OPS and OPS - to - ICE impostor comparisons

to achieve FMR = 0.001.

. iFNMR is computed over the ICE dataset. It cannot be computed over the OPS dataset because only one genuine

comparison is available per subject-eye.

. iFMR is computed for each ICE image by comparing it with 16320 OPS images.

Further we compute an aggregate iFMR as the arithmetic mean of image false match rates over comparison algorithms.

Similarly the aggregate iFNMR is the arithmetic mean of image false non-match rates over the same algorithms.

7. PERFORMANCE ON FULL-SIZE UNCOMPRESSED IMAGES

This section gives the quantitative results for “traditional” rectilinear iris imagery, i.e. 640x480 greyscale images without

application of compression. Such images are packaged as KIND 1 instances. This is advanced as a baseline ahead of the

corresponding results for the cropped, cropped and masked, and polar IREX compact formats.

7.1. FAILURE TO ENROLL

In the IREX context a failure to enroll refers to the failure of a SDK function invocation to produce the anticipated output.

Two cases apply: The failure to convert a 640x480 input image to an IREX image record is counted as FTE. The failure to

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

34
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

SDK OPS ICE BATH

k1 k3 k7 k16 k1 k3 k7 k16 k1 k3 k7 k16

A1

0.000

15

0

0.000

0

0

0.000

0

0

0.000

0

0

0.002

146

0

0.000

4

4

0.000

9

9

0.000

0

0

0.000

0

0

0.001

30

30

0.001

30

30

0.000

0

0

A2

0.000

15

0

0.000

0

0

0.000

0

0

0.000

0

0

0.002

146

0

0.000

4

4

0.000

9

9

0.000

0

0

0.000

0

0

0.001

30

30

0.001

30

30

0.000

0

0

B1

0.000

0

0

0.000

0

0

0.000

0

0

0.000

1

0

0.000

0

0

0.000

1

0

0.000

1

0

0.000

4

0

0.000

0

0

0.000

0

0

0.000

0

0

0.000

8

0

B2

0.000

0

0

0.000

0

0

0.000

0

0

0.000

1

0

0.000

0

0

0.000

1

0

0.000

1

0

0.000

4

0

0.000

0

0

0.000

0

0

0.000

0

0

0.000

8

0

C1

0.001

30

0

0.013

426

16

0.003

89

16

-

0.001

95

0

0.003

191

81

0.002

173

81

-

0.013

310

0

0.224

5163

291

0.049

1128

291

-

C2

0.001

43

0

0.013

420

4

0.002

73

2

-

0.002

127

0

0.004

295

43

0.003

214

37

-

0.011

263

0

0.225

5186

237

0.048

1111

218

-

D1

0.000

15

0

0.000

12

0

0.001

18

0

-

0.006

425

0

0.005

346

0

0.003

206

0

-

0.018

406

0

0.019

441

0

0.016

379

0

-

D2

0.000

16

0

0.001

18

0

0.001

37

0

-

0.008

602

0

0.007

505

0

0.007

477

0

-

0.019

446

0

0.020

471

0

0.019

440

0

-

E1

0.001

30

0

0.001

30

7

0.001

33

0

-

0.012

851

0

0.012

883

72

0.012

836

0

-

0.029

658

0

0.045

1043

721

0.030

690

0

-

E2

0.001

30

0

0.001

30

30

0.001

33

33

-

0.012

851

0

0.012

883

883

0.012

836

836

-

0.029

658

0

0.045

1043

1043

0.030

690

690

-

F1

0.000

12

0

0.000

16

5

0.000

11

5

0.001

20

5

0.003

228

0

0.003

242

33

0.003

181

33

0.015

1087

33

0.044

1003

0

0.051

1183

996

0.044

1008

996

0.046

1063

995

G1

0.001

22

0

0.001

22

22

0.002

76

76

0.003

104

104

0.005

392

0

0.005

364

364

0.007

508

508

0.010

742

742

0.043

1000

0

0.044

1009

1009

0.053

1219

1219

0.102

2353

2353

G2

0.001

28

0

0.001

28

28

0.002

70

70

0.003

107

107

0.003

248

0

0.003

248

248

0.005

348

348

0.009

627

627

0.024

556

0

0.025

585

585

0.034

778

778

0.092

2109

2109

H1

0.002

53

0

0.002

56

53

0.002

79

55

0.002

60

53

0.000

11

0

0.000

35

24

0.001

42

24

0.000

13

11

0.005

117

0

0.012

273

249

0.013

288

252

0.005

122

117

H2

0.002

53

0

0.002

56

53

0.002

79

55

0.002

59

53

0.000

11

0

0.000

35

24

0.001

42

24

0.000

13

11

0.005

117

0

0.012

273

249

0.013

288

252

0.005

122

117

I1

0.000

0

0

0.000

0

0

0.000

0

0

0.000

0

0

0.000

4

0

0.000

4

4

0.000

4

4

0.000

4

4

0.000

0

0

0.000

0

0

0.000

0

0

0.000

0

0

I2

0.000

0

0

0.000

0

0

0.000

0

0

0.000

0

0

0.000

5

0

0.000

6

6

0.000

6

6

0.000

6

6

0.000

0

0

0.000

0

0

0.000

0

0

0.000

0

0

J1

0.001

28

0

0.000

13

13

0.000

13

13

0.000

13

0

0.002

167

0

0.000

21

21

0.000

21

21

0.000

21

0

0.016

364

0

0.016

364

364

0.016

364

364

0.016

364

0

J2

0.001

28

0

0.001

17

8

0.000

15

8

0.001

21

0

0.002

167

0

0.000

24

17

0.000

30

17

0.001

46

0

0.016

364

0

0.022

497

351

0.021

479

351

0.027

613

1

Table 6: The numbers and proportions of failed template and IREX record creation attempts. In each cell there are three quantities:

First is the fraction of templates missing, second is the number of templates missing, and third is the number of IREX records that were

not produced. By definition, this last value is less than or equal to the second. All rates refer to native operation, and are summations

over the enrolment and verification image sets. Colors: Cells are shaded dark red when the topline FTE is above 1.0%, light red above

0.5%, light green below 0.1% and dark green with exactly zero errors.

convert an IREX record to a template is also an FTE. A failure to acquire (FTA) is defined identically and only differs from

FTE in that FTE refers on enrollment samples and FTE refers to verification samples. Note that even if an SDK produces an

output the content may nevertheless be incorrect, e.g., the resulting image is of the endocanthion and not the iris. Such

semantic faults will not count as FTE because we can’t automatically detect such events. Instead they will most often

lead to false rejection errors.

In any case, the occurrence of FTE events is shown in Table 6 which reports failure for all SDKs producing and operating

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

35
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

on all KINDS of images from all three datasets. All input images were uncompressed. The notable results are below.

. Across all datasets the I1 and I2 SDKs give essentially zero failures to enroll. The only failures are limited to ICE

images for which the eye is closed or, in one case, not present at all. Additionally the B1 and B2 SDKs are almost

perfect with A1 and A2 failing only on very few ICE images.

. Elsewhere the BATH dataset presents more problems than ICE which, in turn, is much harder than the OPS set. The

failures in the BATH dataset are for prosaic reasons of size as documented extensively in section 7.2. The images in

the BATH set used here have iris diameters less than or equal to 340 pixels.

. Failures are often concentrated in specific stages of processing: For example, with ICE images the E2 SDK fails to

make some KIND 3 records. In contrast, the failures for the D2 SDK occur with ICE imagery during the production

of templates from the KIND 3 records. In some cases errors occur in both phases (e.g., SDK J2 making KIND 3 from

ICE images). This result is explained by the fact that both steps involve significant image analysis operations.

7.2. EFFECT OF IRIS RADIUS

The question of whether performance is affected by iris size motivated the analyses of Figures 7 and 8 for the BATH and

OPS datasets respectively. The radius values used in this analysis are those reported by the I1 SDK in the headers of its

KIND 3 records. Note that the parent BATH dataset contains many images of large size. NIST downsampled all 1280x640

parents to the 640x480 norm. As described in section 5.2, all IREX analyses are restricted to those images for which the

final iris radius R ≤ 170 pixels. The histograms of iris size for these images are shown in Figure 5. The notable results

are as follows. (Larger figures for all SDKs and all datasets appear in the appendices of the IREX SUPPLEMENT28.)

. From the 2D comparison-count maps in the bottom right hand corner, the IREX partition of the BATH dataset

contains a greater occurrence of atypical iris sizes than is present in the OPS dataset. Particularly the BATH dataset

has a significant number of cases where the enrollment and verification iris pairs are either both large or both small.

. The heat maps plot FNMR at a fixed FMR of 0.001 for KIND 1 vs. KIND 1 comparisons. Importantly, they treat

template generation failures as false non-matches. The higher presence of red in Figure 7 vs. Figure 8 shows that

there are fewer errors overall on the OPS dataset than on the BATH set.

. For the OPS images, the false rejections tend to occur when the radius of the iris is large. While it is possible

(and likely for fixed focal-length cameras) that different sizes are symptomatic of other problems (e.g., out of focus

images) a majority of the SDKs are immune to this.

. In the BATH dataset, several SDKs almost always fail on images with small iris radii. A larger number fail for the

case when the two iris radii differ substantially (i.e. small vs. large).

These results have implications for the iris image standard. While 640x480 pixels is the de facto standard image size, the

current ISO/IEC 19794-6 only guides on iris size in a best practice annex. This is likely to be remedied in the revised ISO

standard, per IREX input. Performance-related image attributes will be further refined in the new ISO/IEC 29794-6 Iris

Image Quality standard.

28http://iris.nist.gov/irex/irex appendices.pdf.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

36
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

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(l) log10 1 + COUNT(R1, R2)

Figure 7: For each primary SDK the figure shows the dependency of false non-match rate on iris radius for the BATH dataset. Each

cell is the FNMR at FMR = 0.001 for enrollment samples on the y-axis and verification samples on the x-axis. The radii are quantized

into three-pixel bins. The radii run 102 ≤ r ≤ 171 pixels. Cells are uncolored when the dataset did not contain any comparisons

with those radii. Elsewhere, particularly away from the diagonal and in the corners, the number of comparisons is sometimes small

such that there is considerable error in the FNMR estimates. The number of the comparisons, on a log scale, is shown at bottom right.

Larger images for each SDK on all three datasets appear in the respective appendices.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

37
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

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Figure 8: For each primary SDK the figure shows the dependency of false non-match rate on iris radius for the OPS dataset. Each cell

is the FNMR at FMR = 0.001 for enrollment samples on the y-axis and verification samples on the x-axis. The radii are quantized into

three-pixel bins. The radii run 96 ≤ r ≤ 186 pixels. Cells are uncolored when the dataset did not contain any comparisons with those

radii. Elsewhere, particularly away from the diagonal and in the corners, the number of comparisons is sometimes small such that

there is considerable error in the FNMR estimates. The number of the comparisons, on a log scale, is shown at bottom right. Larger

images for each SDK on all three datasets appear in the respective appendices.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

38
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

7.3. DET CHARACTERISTICS

As is typical in offline testing [4], this report computes the DET for all scores emitted by a tested algorithm29. This allows

a survey over all operating points, τ , and contrasts with the operational situation in which the system is configured with

a fixed operating threshold, against which a decision is rendered. The DET is a plot of FNMR(τ ) against FMR(τ )

30 and,

as the primary output of a biometric performance test, is vital in establishing the tradeoff between TYPE I and TYPE II

errors.

7.3.1. RELATIVE DIFFICULTY OF DATASETS

For the purposes of comparing the three IREX datasets, Figure 9 shows, the DET characteristics for six primary SDKs

31

Note: The plots ignore comparisons where templates were missing (e.g., from FTE events), showing matching errors

only. The intent is to show relative ordering of FNMR and slope. The notable observations are as follows:

. The ordering of the DET traces varies across SDKs. That is the implementations differ in which dataset gives the

best accuracy. So while A1 finds OPS easier than ICE, which in turn is easier than BATH, B1 prefers BATH to ICE, and

D1 prefers both of these to OPS. ICE is never best.

. At a fixed threshold, the observed FNMR values vary across the three datasets by as little as a factor of two (B1,H1,J1)

to as much as an order of magnitude (C1).

. Similarly, at a fixed threshold, the observed FMR values vary across the three datasets by as little as a factor of two

(A1) to as much as an order of magnitude (J1).

. The SDKs are much more consistent in the slopes of the DET characteristics. The slope is related to the separability

of the genuine and impostor distributions, with a flatter DET being more desirable. The ordering for most SDKs,

from flattest to steepest, is OPS, BATH, and ICE. The I1 and H1 SDKs prefer ICE to BATH.

7.3.2. RELATIVE PERFORMANCE OF ALGORITHMS

The DET characteristics of Figures 10, 11 and 12 allow comparison of the core algorithmic accuracy of the IREX algorithms.

They are computed from comparison of uncompressed 640x480 images from the OPS, BATH and ICE datasets as described

in section 5. While discussion in section 7.3.1 showed that different providers’ algorithms work best on different datasets,

the comparative DET plots in this section show that the relative ordering of algorithms is mostly consistent. For the main

observations that follow, note that the DETs include the effects of FTE and FTE - see the discussion in section 7.3.3.

. On the OPS images, all SDKs produce reasonably flat DET characteristics. For most SDKs there is little variation in

FNMR across the five decades of FMR plotted. On this dataset, the I1 and I2 SDKs are almost identical and give

fewer than half the false non-matches of the next closest SDKs (B1 and A1).

. For all datasets, the false non-match rates of the SDKs vary over more than an order of magnitude. The DET

characteristics tend not to cross, and they are almost always flat enough that a change in FMR operating point

cannot compensate for choice of recognition algorithm. That is, a relaxation of the false acceptance criterion does

not lower false rejection sufficiently to beat other algorithms.

29The IREX API defined comparison scores to have a floating point type. Although two of the participants, B and J, produced integer values, these

are subject to identical analyses.

30DET characteristics sometimes plot Normal deviates, i.e. a plot in which the FNMR and FMR are (non-linearly) transformed by the inverse CDF of

N(0, 1). This is abandoned here because the score densities are often not Normal.

31All DET plots are included in the IREX SUPPLEMENT at http://iris.nist.gov/irex/irex appendices.pdf.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

39
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

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OPS k1 lossless vs. k1 lossless

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BATH k1 lossless vs. k1 lossless

(a) FNMR A1

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

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1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

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FMR

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ICE k1 lossless vs. k1 lossless

BATH k1 lossless vs. k1 lossless

(d) FNMR E1

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

0.0015 0.0020 0.0030 0.0040 0.0055 0.0070 0.0090

FMR

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+

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+

1.133

+

1.148

+

1.162

+

1.179

+

1.195

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

0.0015 0.0020 0.0030 0.0040 0.0055 0.0070 0.0090

FMR

FNMR

1.107

+

+

1.120

+

+

1.133

+

+

1.148

+

+

1.162

+

+

1.179

+

+

1.195

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

0.0015 0.0020 0.0030 0.0040 0.0055 0.0070 0.0090

FMR

FNMR

1.107

+

+

1.120

+

+

1.133

+

+

1.148

+

+

1.162

+

+

1.179

+

+

1.195

+

+

OPS k1 lossless vs. k1 lossless

ICE k1 lossless vs. k1 lossless

BATH k1 lossless vs. k1 lossless

(e) FNMR I1

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

0.0055 0.0065 0.0075 0.0085 0.0095 0.0110 0.0125

FMR

FNMR

3191

+

3196

+

3201

+

3206

+

3211

+

3216

+

3222

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

0.0055 0.0065 0.0075 0.0085 0.0095 0.0110 0.0125

FMR

FNMR

3191

+

+

3196

+

+

3201

+

+

3206

+ +

3211

+

+

3216

+

+

3222

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02 5e−02

0.0055 0.0065 0.0075 0.0085 0.0095 0.0110 0.0125

FMR

FNMR

3191

+

3196

+

+

3201

+

+

3206

+

+

3211

+

+

3216

+

+

3222

+

+

OPS k1 lossless vs. k1 lossless

ICE k1 lossless vs. k1 lossless

BATH k1 lossless vs. k1 lossless

(f) FNMR J1

Figure 9: DET characteristics for six implementations on three IREX datasets. All comparisons are with uncompressed KIND 1 vs.

KIND 1 images. The lines join points corresponding to the a fixed threshold. All results apply to native operation. The vertical scales

are different. Non-vertical links indicate a change in FMR when the dataset changes. The effect of failure to produce templates (i.e.

FTE and FTA) is ignored, because the intent is to show relative ordering of FNMR and slope. This means the plots are not suitable for

comparative testing of algorithms (see section 7.3.3).

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

40
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 Figure 10: The DET characteristics for all IREX algorithms on the OPS dataset for uncompressed i.e. KIND 1 images. As documented in Table 6 of section 7.1, each SDK failed to produce templates from a fraction of the images and the effects of that are included in this plot. For the OPS dataset this fraction was often zero.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

41
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 Figure 11: The DET characteristics for all IREX algorithms on the BATH dataset for uncompressed i.e. KIND 1 images. As documented in Table 6 of section 7.1, each SDK failed to produce templates from a fraction of the images and the effects of that are included in this plot.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

42
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 Figure 12: The DET characteristics for all IREX algorithms on the ICE dataset for uncompressed i.e. KIND 1 images. As documented in Table 6 of section 7.1, each SDK failed to produce templates from a fraction of the images and the effects of that are included in this plot.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

43
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

. A notable exception applies to the ICE and BATH datasets: Figures 11 and 12 show that the I1 and I2 SDKs give very

low error rates at high false match rates of 0.001, but this begins to degrade below about FMR = 10−4such that the

B1 and B2 SDKs give the best accuracy below FMR = 10−5. On the BATH dataset, primary and secondary SDKs from

each provider are usually very close.

. On the ICE dataset, the FNMR estimates appear more tightly clustered but this is an artifact of the log-scale. The

FNMR values are uniformly higher, with the best FNMR values being an order of magnitude higher than those in the

OPS dataset. This reflects the “clean” nature of the OPS dataset32 and the operationally non-representative nature

of the ICE images. Regarding the portability of these results to operational deployments readers should see the

discussion on page 13 and in section 5.4.

7.3.3. THE EFFECT OF TRADING FTE FOR FNMR

Section 6.1 introduced the failure to enroll and failure to acquire measurements and showed that they are influential on

accuracy. Figure 13 shows two sets of DET characteristics for recognition of uncompressed KIND 1 ICE images. The first

set of plots penalizes an SDK for failing to process enrollment and verification images. The second set does not. The

relative accuracy depends on the template generation failure measurements reported in Table 6. For the A1, A2, B1, B2,

I1 and I2 SDKs, the DET characteristics are identical because they always produce a template i.e. FTE = FTE = 0. For other

SDKs these values are non-zero. For example, the E1 and E2 SDKs fail to make templates from 1.2% of the ICE images.

This failure is often elective, i.e. the image processing algorithms of the SDKs determine that the input image is, in some

internally defined sense, irregular or unsuitable (e.g., the image was blurred, or the eye was closed). Operationally this

quality control function is common and valuable because failure to produce a template may trigger re-acquisition of a

new (and hopefully better) image. However, in some applications where the subject cannot be imaged again, a failure is

unrecoverable.

The efficacy of rejecting poor images can be seen by studying the results for SDKs E1 and E2 in Figure 13. With FTE

and FTE removed from the error rates in the lower graph, they become the best performing algorithms. This is evidence

that these SDKs are making good decisions when they declares FTE and FTE events i.e. that the failures are effective at

reducing false non-match errors. This would not be the case if the failures were random.

This is clearly unfair for comparative testing because the zero-failure SDKs would potentially benefit from excluding those

images and the providers of those SDKs deserve some credit for enrolling the image and trying to match them. However,

note that comparative testing should also include the tradeoffs between accuracy and speed discussed in section 7.6, and

in this respect the E SDKs are somewhat faster at template generation than the A and B implementations.

The overall interpretation of the results depends on the intended application: If FTE and FTE events are operationally

present and tolerably infrequent then the algorithms may be viable; if on the other hand FTE and FTE events are unrecoverable then alternative algorithms seem necessary.

The same discussion above applies to the BATH dataset, although the causes of the FTE events are different. Note that

FTE rates of the OPS dataset are very low.

7.4. FALSE MATCH RATE CALIBRATION

The practice of using fixed FMR and threshold values elsewhere in this report is undertaken for the purpose of analysis

only, and is not intended as any recommendation on operational FMR or threshold policies. However, this section supports threshold selection by giving the incidence of false matches as a function of threshold. This follows the ITIRT[32]

study which exhaustively tabulated the false match rates as a function of threshold for the Iridian product used.

32The OPS dataset is easier to recognize because an iris recognition system was used in selection of the images - see section 5.1.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

44
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

(a) Including the effect of FTE

(b) Excluding the effect of FTE

Figure 13: The DET characteristics for all IREX algorithms on the ICE dataset for uncompressed (i.e. KIND 1 images). Above are

the plots that include the effects of FTE and FTE and below is the result of excluding missing templates from the FNMR and FMR

computations. Each SDK failed to produce templates from a fraction of the images. For the ICE images used here, this fraction was

often quite large (see Table 6).

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

45
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 Figure 14: The plot of FMR vs. threshold for SDK D2 and various KINDS and sets of comparisons. Each curve is an empirical cumulative distribution function of a set of impostor scores with log-linear axes. Note that these curves are computed from images of O(10000) people for OPS, O(1000) people for BATH, and O(100) people for ICE. In addition, systematic variation beyond that observed here may occur. Threshold values only have meaning for specific algorithms; they are not interoperable across SDKs and providers.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

46
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 Figure 15: The plot of FMR vs. threshold for SDK A1 and various KINDS and sets of comparisons. Each curve is an empirical cumulative distribution function of a set of impostor scores with log-linear axes. Note that these curves are computed from images of O(10000) people for OPS, O(1000) people for BATH, and O(100) people for ICE. In addition, systematic variation beyond that observed here may occur. Threshold values only have meaning for specific algorithms; they are not interoperable across SDKs and providers.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

47
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

The dependency of FMR on threshold has been the subject of much academic publication at least for one class of iris algorithms [15]. Indeed the claim of a known impostor distribution is an extremely attractive and valuable property for all

biometrics and particularly for one-to-many applications where false match suppression is of paramount importance for

large populations. Figures 14 and 15 gives this dependence for SDKs D2 and A133 as applied to various combinations of

intra-dataset and inter-dataset, and compression image, comparisons. The corresponding graphs for other SDKs appear

in the respective appendices.

The curves include use of inter-dataset comparisons (ICE vs. OPS ) which afford very high assurance of ground truth

integrity. This arises because the subject populations are very well separated geographically and occupationally, and the

likelihood of co-membership is considered to be identically zero.

Note that while the number of impostor comparisons at 1.2x109is still short of the 1.7 x 109 performed in ITIRT[32], the

number of persons present is somewhat larger (8160 + 240 = 8400 vs. 1224 in ITIRT). Both of these numbers are far short

of the 632500 irises used in executing the approximately 2 x 1011 impostor comparisons reported for the UAE data[16].

For these graphs the notable observations are as follows.

. For these sets, log FMR (τ ) is usually a slightly sub-linear function of threshold (i.e. τ

a

, a < 1) reflecting only an

approximately exponential form in the left tail of the impostor distribution. Exceptions to this are the H1 and H2

algorithms which exhibit a more nonlinear dependence.

. With intra-dataset comparisons, ground truth integrity is generally subject to unresolved consolidation errors.

These appear in both the BATH - to - BATH and ICE - to - ICE FMR distribution functions as horizontal lines at low

FMR because the genuine pairs that contaminate the impostor distributions are most often rejected only at much

lower thresholds.

. Across datasets, formats, and compression levels there is often an order of magnitude variation in the observed

false match rates. Specifically if the threshold that gives a FMR around 0.0001 is considered, there is a vertical

spread of the various plots. For SDK A1, this variation is low, at about a factor of three. For others it can approach

a factor of 20. This goes to the stability of the impostor distribution which is an important metric in its own right

(see the discussion later, in section 9).

. Table 7 shows the correspondence of the impostor empirical cumulative distribution function computed here for

KIND 1 ICE vs. KIND 1 OPS comparisons with values published by Daugman. Comparisons are provided for a)

empirical estimates for the score-normalized algorithm[16], and b) theoretical predictions of false match occurrence

for the original algorithm[15]. For D2, this proximity is noteworthy in that neither dataset existed at the time the

(a) Proc. IEEE[16]

Threshold HD Published D1

0.282 3.5 10−93.8 10−8

0.297 5.6 10−81.9 10−7

0.312 5 10−79.0 10−7

0.317 1 10−61.6 10−6

(b) IEEE Trans. CSVT[15]

Threshold HD Published D2

0.32 3.8 10−84.2 10−8

0.33 2.5 10−72.9 10−7

0.34 1.4 10−62.1 10−6

0.35 7.5 10−61.4 10−5

Table 7: Selected IREX estimates of FMR alongside previously published estimates. Cambridge indicates in their comments in the D1

and D2 appendices of the IREX SUPPLEMENT that score normalization is used for D2 but not for D1. The IREX estimates are for the

1,165,868,160. OPS - ICE impostor comparisons.

33See the IREX SUPPLEMENTAL appendices for the graphs for all other SDKs. The PDF of the appendices may be downloaded from

http://iris.nist.gov/irex/irex appendices.pdf.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

48
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Daugman paper was written. The full FMR (τ ) plots, for SDKs D2 and A1, are presented in Figures 14 and 15 (the

remainder are in the supplemental appendices).

. The D2 implementation’s OPS - to - OPS FMR curves in Figure 14 are all significantly above the OPS - to - ICE plots.34

This observation, that inter-dataset false matches are inherently less likely than intra-dataset, would occur if some

aspect of the imaging environment imparted a degree of similarity to iris images within a dataset to which the

feature extraction algorithms is not invariant. The imaging environment could include aspects intrinsic to the

sensor (illuminants, for example) or extrinsic (manner of presentation, or ambient illumination, for example). The

authors are not aware of published studies of this hypothesis. In any case, cross-database, cross-camera searches

are a defining aspect of federated applications in which images are collected using cameras in a multi-vendor

marketplace.

. For other implementations whose native score scale might suggest Daugman-like algorithmic function, the impostor distributions are also higher than expected. IREX and other NIST tests know nothing of the internals of

algorithms submitted (i.e. they’re black boxes), and thus the CDF plot cannot be held to be unusual or unexpected.

We report these curves to assist practitioners in setting thresholds.

7.5. TEMPLATE SIZES

The template sizes are tabulated in Figure 16. While some observations are made below, see section 7.6 for additional

discussion.

. There are two orders of magnitude between the largest and smallest template sizes: For SDK E2 the size of the

verification template is 45080 bytes; for G1 the enrollment template is 512 bytes.

. Five SDKs from three providers (A, E, G) implemented asymmetric templates. The IREX API allowed role-specific

templates where the template generation function is informed of the kind of template to be produced. In all those

cases the verification template is larger than the enrollment template. This property is desirable for persistent

enrollment datasets on cost-of-storage grounds.

. One provider submitted SDKs (A1 and A2) with variable length templates. While many iris recognition algorithms

represent the iris in a fixed-dimensional vector space[47, 15] the templates allowed in IREX are fully proprietary

and may include any kind of information, including the original image. Despite the extensive discussion of iriscodes

in the academic literature there is no standard representation and no prospect of making one. In any case, NIST has

no knowledge of the template contents.

. If templates are concatenated and compressed using the bzip2 lossless compressor some size benefits are realized

for all SDKs. The size reductions range from around 12% through to 70% for the OPS dataset, and from 19% to

70% for the ICE templates. Many individual templates cannot be compressed in this manner (i.e. the size increases

under compression).

. Some IREX templates are larger than the images as compressed under IREX . If an operation elected to compress

images to 3KB, then the templates from A1, A2, B1, B2, E2, F1, H2, I1, and I2 would be larger than the image. If the

image size was 2KB, J1 and J2 would be added to this list. This situation is atypical in biometrics. It occurs because

the Daubechies wavelet representations used in compressed JPEG2000 streams are efficient texture-preserving, and

34In Figures 15 and 14 the ICE - to - ICE and BATH - to - BATH comparisons appear to be contaminated with residual ground truth errors, and are

therefore not useful for examining the impostor distribution tails.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

49
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 k03.A1 k07.A1 k16.A1 k03.A2 k07.A2 k16.A2 k03.B1 k07.B1 k16.B1 k03.B2 k07.B2 k16.B2 k03.C1 k07.C1 k16.C1 k03.C2 k07.C2 k16.C2 k03.D1 k07.D1 k16.D1 k03.D2 k07.D2 k16.D2 k03.E1 k07.E1 k16.E1 k03.E2 k07.E2 k16.E2 k03.F1 k07.F1

k16.F1

k03.G1k07.G1k16.G1k03.G2k07.G2k16.G2

k03.H1k07.H1k16.H1k03.H2k07.H2k16.H2

k03.I1k07.I1k16.I1k03.I2k07.I2k16.I2

k03.J1k07.J1k16.J1k03.J2k07.J2k16.J2

1e+04

2e+04

5e+04

1e+05

2e+05

k3 k7 k16

(a) Size of IREX record

E.A1

V.A1

E.A2

V.A2

E.B1

V.B1

E.B2

V.B2

E.C1

V.C1

E.C2

V.C2

E.D1

V.D1

E.D2

V.D2

E.E1

V.E1

E.E2

V.E2

E.F1

V.F1

E.G1

V.G1

E.G2

V.G2

E.H1

V.H1

E.H2

V.H2

E.I1

V.I1

E.I2

V.I2

E.J1

V.J1

E.J2

V.J2

500

1000

2000

5000

10000

20000

50000

Enrol Verif

(b) Size of template

Figure 16: Top: The distribution of the IREX record size (header + image), arranged by SDK and image KIND. The boxplot whiskers

extend to the highest and lowest observed values except that failed, zero length, records are not included. The units are in bytes and

a log-scale is used. Bottom: The template size arranged by SDK and the ROLE of the template (enrollment, then verification). The

boxplot whiskers extend to the value in which 99.9% of Normal deviates would reside. In both cases, the vertical grey lines delimit

the SDKs for ease of viewing.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

50
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Linux SDKs

C1 C2 D1 D2 H1 H2 I1 I2

k1 166 238 21 17 2274 9086 1569 1557

k3 163 235 21 17 2276 9086 1571 1575

k7 166 239 21 17 2274 9086 1571 1550

k16 - - - - 2273 9083 1569 1564

Windows SDKs

A1 A2 B1 B2 E1 E2 F1 G1 G2 J1 J2

k1 1032 211 553 102 259 5115 233 51 53 14 20

k3 1051 211 540 101 259 5112 234 51 53 15 21

k7 1069 210 541 101 259 5276 234 51 53 15 21

k16 1111 211 540 101 - - - 51 53 15 21

Table 8: For LINUX SDKs (left) and Win32 SDKs (right) the time needed to execute a single template comparison by SDK and KIND. The

units are microseconds (μs). The values are computed over a single timing of C = N(N − 1)/2 comparisons. With N = 600 + 300 =

900, C = 404550. The LINUX SDKs run on Intel Xeon E5405 blades running at 1995MHz, with a 1333MHz bus and 2 x 6MB of cache,

benchmarked at SpecInt of 17.6. The win32 SDKs run on Intel Xeon E5310 blades running at 1595MHz, with a 1066MHz bus and 2 x

4MB of cache, benchmarked at SpecInt of 12.6.

reversible, representations of the original iris raster. Daugman noted[19, 18] the utility of JPEG2000 for iris recognition may rest on commonalities between its wavelet representation and that used in the recognition algorithm35

.

7.6. TIMING RESULTS AND ACCURACY-TIME TRADEOFFS

This section gives results for the times of the elemental operations provided in the IREX API specification. These are

. Conversion of a raw 640x480 greyscale raster into the KIND 3, KIND 7 and KIND 16 records. This includes iris detection and initial localization, and in the case of unsegmented polar records, the rectilinear to polar interpolation.

. Conversion of an IREX record into a proprietary template. This requires final fine segmentation and feature extraction. For the unsegmented polar record this does not include polar to rectilinear conversion because that was

performed by the NIST test harness which produces the KIND 48 instance passed to the template generator.

. One-to-one comparison of two proprietary templates.

7.6.1. IREX RECORD AND TEMPLATE GENERATION TIMES

The boxplots of Figure 17 show the times needed to generate IREX records and to produce templates from them. The

plots include data for the various KINDS and all SDKs. The notable results on computational efficiency are as follows.

. The time taken to convert a parent 640x480 iris image into a IREX record varies by two orders of magnitude over

the SDKs tested. For G1 and G2 the times are below 20 milliseconds (ms), except for KIND 7 which requires around

60 ms. For J2 the times are around 1200 ms on the same hardware.

. For some SDKs the time needed to generate the various KINDS is essentially the same. This applies to B1, B2, D1,

D2, E1, E2, F1, I1, I2 and J2. For the others, the KIND 3 record is usually the least expensive to produce, followed by

KIND 7 and KIND 16. The increase in expense never exceeds a factor of about three (A1).

. The time needed to produce a template from an IREX record also varies by two orders of magnitude. For the KIND

7 record the time needed for D2 is 13 ms, while for I2, the figure is 1088 ms.

. The variation in template generation time across SDK is much larger than across the KIND of image. The template

generation time usually depends on the KIND of the input record, the exceptions being for H1, H2, G1 and G236.

In almost all other cases the KIND 1 record is the most expensive, reflecting the cost of the iris detection in a large

raster. In the case of J1, the time for KIND 1 is a factor of forty larger than for the other KINDS.

35Because the IREX test never passed compressed encoded data to the template generators it precluded the direct use of the JPEG2000 or JPEG stream.

This possibility has been mooted because the wavelet representation of JPEG2000 is not dissimilar to the Gabor wavelets used in many published iris

feature extractors. Given lack of research and unclear performance benefits NIST considers that this was not a significant limitation of the IREX design.

36The G1 and G2 implementations run under windows where the timing resolution is close to that of the measured time. The gives poor uncertainty

estimates.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

51
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 k03.A1 k07.A1 k16.A1 k03.A2 k07.A2 k16.A2 k03.B1 k07.B1 k16.B1 k03.B2 k07.B2 k16.B2 k03.C1 k07.C1 k16.C1 k03.C2 k07.C2 k16.C2 k03.D1 k07.D1 k16.D1 k03.D2 k07.D2 k16.D2 k03.E1 k07.E1 k16.E1 k03.E2 k07.E2 k16.E2 k03.F1 k07.F1

k16.F1

k03.G1k07.G1k16.G1k03.G2k07.G2k16.G2

k03.H1k07.H1k16.H1k03.H2k07.H2k16.H2

k03.I1k07.I1k16.I1k03.I2k07.I2k16.I2

k03.J1k07.J1k16.J1k03.J2k07.J2k16.J2

10

20

50

100

200

500

1000

k3 k7 k16

(a) Time to make IREX record (milliseconds)

k01.A1k03.A1k07.A1k16.A1k01.A2k03.A2k07.A2k16.A2k01.B1k03.B1k07.B1k16.B1k01.B2k03.B2k07.B2k16.B2

k01.C1k03.C1k07.C1k16.C1k01.C2k03.C2k07.C2k16.C2k01.D1k03.D1k07.D1k16.D1k01.D2k03.D2k07.D2k16.D2

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Figure 17: At top, the distributions of the time needed to generate a standard IREX iris image record arranged by SDK and KIND.

Below is the corresponding time to convert an IREX record into a proprietary template. In both cases the units are milliseconds (ms),

and log-scales are used. The times are measured over 600 OPS and 300 ICE images. The vertical grey lines delimit the SDKs for ease of

viewing. The timing resolution depends on the operating system. On windows platforms, for SDKs ∈ {A, B, E, F, G, J} the resolution

is 15ms. On the linux platform, for SDKs ∈ {C, D, H, I} the resolution is 1μs. The faster SDKs operate near this resolution, and only the

median values of the boxplots are meaningful. The boxplot whiskers extend to the value in which 99.9% of Normal deviates would

reside.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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. For the KIND 3, KIND 7 and KIND 16 instances, where the iris location is known, the template generation times

are usually similar. While the notched boxplots do exhibit significant differences, the inter-quartile ranges often

overlap. This indicates that cost of preparation is not innately derogatory to particular standard formats.

7.6.2. MATCHING TIMES

Table 8 shows the time needed to execute one-to-one template comparisons. All times are given in microseconds (μs)

and are averages over 405550 comparisons. The notable observations are as follows.

. Across SDKs the time needed to compare two templates to produce a comparison score varies by three orders

of magnitude. The times are as short as 14 microseconds for J1 to 9 milliseconds for H237. While, for many

implementations the comparison operation is fast (because the template represents the iris in a vector space[47]

for which arithmetic operations can be as inexpensive as bitwise XOR [15]), the very large range observed here

reflects a diversity in the algorithmic approaches.

. There is considerable range in speeds across the SDKs from a single IREX participant. SDK E2 is 20 times slower

than E1. Similarly both A1 and A2, and B1 and B2, differ by a factor of five. This, too, is indicative of the use of

different algorithms, representations or parametrization.

7.6.3. SPEED VS. ACCURACY TRADEOFFS

Given the wide ranges in processing times observed in the last subsections, the question arises whether accuracy can be

traded for speed. This is addressed directly in the plots of Figure 18, each of which is a scatter plot of duration against

accuracy, with each point corresponding to one SDK. In each case the accuracy is quantified by FNMR at a fixed global

threshold giving FMR = 0.00001 computed over uncompressed and uncropped ICE and OPS images. The FNMR value is

the simple average of those observed over the OPS and ICE corpora. The three durations are:

. The time taken to prepare KIND 3 and KIND 7 IREX records;

. The time taken to prepare templates from unprocessed 640x480 KIND 1 images;

. The time taken to compare two proprietary templates.

A fourth duration was added: This is a composite value representative of a physical access control application in which

any of the N enrolled users cooperatively presents to the system without use of an identity credential. The duration of

a first attempt can be modeled as the summation of the capture time, TC , the template generation time, TI , and N times

the matching time, TM. The whole transaction takes time

T = TC + (TI + NTM)(1 + FNMR(τ )) (14)

where the possibility of false rejection at a first attempt is included by the FNMR term, and where it is assumed that

the initial capture phase includes acquisition of at least one further image (e.g., via a two-eye camera). This result of

applying this model with the measured FNMR values is shown in the last plot of Figure 18. The notable observations are:

. The inverse relation between accuracy and speed is even more evident than for the single-aspect time vs. accuracy

plots. The four most accurate algorithms would take T ≥ 4 seconds.

. The N = 2000 multiplier penalizes the expensive matchers, and such that the total time would be operationally untenable. Some providers have made comments on this aspect in their respective annexes of the IREX SUPPLEMENT.

37This range meant that a full cross-comparison of the IREX datasets would require from two hours to two months on a single CPU depending on

the SDK. The latter figure is comparable with the time between outages in data-centers. The final IREX computations were parallelized over 64 CPUS.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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Figure 18: Duration of the IREX functions versus FNMR at FMR = 0.00001 where the FNMR value is the simple average over the OPS

and ICE dataset. At top left is the IREX record creation time measured in milliseconds; at top right is the template generation time also

in milliseconds; at bottom left is the one-to-one comparison time in microseconds (μs); and finally at bottom right is the time for the

hypothetical access control system of equation 14 with N = 2000 and TC = 2.5 secs. All times are on log-scales.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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7.6.4. DISCUSSION

Some important caveats apply to the timing analysis:

. The IREX test plan established very liberal constraints on the expense of the algorithms38 because recognition under

compression was considered to be a harder and newer problem worthy of additional computation. Note that the

IREX test plan did indicate that timing results would be reported.

. The IREX test plan only defined an API for one-to-one verification. One-to-many testing was considered unnecessary because the accuracy effects associated with formats and compression would be clearly evident in verification

mode. One-to-many implementations may generally be expedited via several methods (e.g., incomplete distance

computation, or fast search[27]).

. The IREX test plan did not prohibit threading and at least one SDK utilized this possibility. This can lead to considerable over-estimation of the core (i.e. single thread) speed of the algorithms. The IREX test plan should probably

have prohibited threading since all cpu’s and cores were invoked simultaneously.

. One provider’s SDKs required compilation with 64 bit compilation flags. Use of 64 bit executables does not necessarily expedite processing.

. One provider asserted that the template comparison operation would be faster if their template was used in consecutive comparisons. This condition held for the timing test but, for other reasons, was generally not true for the

accuracy computations.

. Fast implementations have been reported[27]. These are intended give better-than-linear dependence on the size

of the enrolled population in a one-to-many search.

7.6.5. CAN IREX ALGORITHMS BE EXPEDITED

While there is likely some opportunity for expediting the more expensive algorithms tested in IREX, prospective users of

these algorithms should differentiate the following two classes of approach.

. A - Computational optimization: While this class of techniques is most simply achieved using more computationally

powerful hardware, virtually all pieces of software can be optimized via compiler optimizations (loop unrolling,

etc.), compiler changes (icc vs. gcc), recoding (assembly language vs. C vs. Java), and use of dedicated libraries

(e.g., FFTW).

. B - Algorithmic optimization: This class includes alteration of the algorithms themselves with the possible consequence that different outputs are obtained for the same input. Examples here would be to reduce the extent of the

search space, or to use Hough transforms vs. active contours.

It is important to note class A techniques do not change the actual algorithm and that class B techniques either change

the algorithm completely or give heuristic approximations to it. In the latter case, the IREX accuracy results may no

longer apply and performance could degrade).

8. PERFORMANCE OF COMPACT FORMATS

The prior section covered results for the traditional 640x480 iris image without compression. This section addresses the

main focus of the IREX study - the performance of the compact image formats.

38Template comparison in 20ms, template and record creation in under 2500ms.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

(a) KIND 3

(b) KIND 7

(c) KIND 48

Figure 19: An ICE image in each of the three IREX formats. From left to right, the sizes are uncompressed, 4KB, 3KB and 2KB. In

each set, the top row uses JPEG2000 compression with JPEG below. The display/printing process may make it difficult to discern the

increasing compression and sampling artifacts in the last three columns. Beneath each plot is a horizontal scanline across the center

of each image. The KIND 3 images were produced by I1. The KIND 7 images were produced by D1. Note the tighter vertical cropping

applied by D1.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

56
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 800.k3.J2K 1000.k3.J2K 1200.k3.J2K 1400.k3.J2K 1600.k3.J2K 1800.k3.J2K 2000.k3.J2K 2200.k3.J2K 2400.k3.J2K 2600.k3.J2K 2800.k3.J2K 3000.k3.J2K 4000.k3.J2K 6000.k3.J2K 800.k7.J2K 1000.k7.J2K 1200.k7.J2K 1400.k7.J2K 1600.k7.J2K 1800.k7.J2K 2000.k7.J2K 2200.k7.J2K 2400.k7.J2K 2600.k7.J2K 2800.k7.J2K 3000.k7.J2K 4000.k7.J2K 6000.k7.J2K 800.k16.J2K 1000.k16.J2K 1200.k16.J2K 1400.k16.J2K 1600.k16.J2K 1800.k16.J2K 2000.k16.J2K 2200.k16.J2K 2400.k16.J2K 2600.k16.J2K 2800.k16.J2K 3000.k16.J2K 4000.k16.J2K 6000.k16.J2K 800.k3.JPG 1000.k3.JPG 1200.k3.JPG 1400.k3.JPG 1600.k3.JPG 1800.k3.JPG

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1.41.21.00.80.60.40.2

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Figure 20: The distribution of I1 native genuine comparison scores by size of the compressed image, KIND and the compression algorithm. The images are from the OPS dataset. The

right axis scale gives the corresponding value for d

0 = (s −

μI )/

q

0.5(σ

2

I + σ

2

G) for genuine score s. The boxplots only include comparison scores if the uncompressed version of the

same image was matched below the FMR = 0.001 threshold. Above the boxplots are FNMR values at FMR = 10−3. The three blue lines correspond, from the top, to FMR of 10{−2,−3,−4}.The lower grey line refers to the median score obtained from comparison of uncompressed KIND 3 images. Any comparison for which either template had not been generated is excluded.

Note that the iris record size on the horizontal axis is not evenly spaced above 3000 bytes.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

57
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 800.k3.J2K 1000.k3.J2K 1200.k3.J2K 1400.k3.J2K 1600.k3.J2K 1800.k3.J2K 2000.k3.J2K 2200.k3.J2K 2400.k3.J2K 2600.k3.J2K 2800.k3.J2K 3000.k3.J2K 4000.k3.J2K 6000.k3.J2K 800.k7.J2K 1000.k7.J2K 1200.k7.J2K 1400.k7.J2K 1600.k7.J2K 1800.k7.J2K 2000.k7.J2K 2200.k7.J2K 2400.k7.J2K 2600.k7.J2K 2800.k7.J2K 3000.k7.J2K 4000.k7.J2K 6000.k7.J2K 800.k16.J2K 1000.k16.J2K 1200.k16.J2K 1400.k16.J2K 1600.k16.J2K 1800.k16.J2K 2000.k16.J2K 2200.k16.J2K 2400.k16.J2K 2600.k16.J2K 2800.k16.J2K 3000.k16.J2K 4000.k16.J2K 6000.k16.J2K 800.k3.JPG 1000.k3.JPG 1200.k3.JPG 1400.k3.JPG 1600.k3.JPG 1800.k3.JPG

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Figure 21: The distribution of the increase in I1 native genuine comparison scores between the uncompressed “parent” and the compressed image, arranged by size, KIND and the

compression algorithm. The images are from the OPS dataset. Any comparison involving a failed template is excluded. Note that the iris record size on the horizontal axis is not evenlyspaced above 3000 bytes.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

8.1. EFFECT OF COMPRESSION

The application of lossy compression algorithms alters the pixel values in the iris region and in the limbic and pupillary

boundary regions. When compression is low, these changes are inconsequential, but when compression is applied with

sufficient severity the changes appear as visible artifacts and cause recognition errors. This is evident in Figure 19.

8.1.1. THE GENUINE DISTRIBUTION UNDER COMPRESSION

Figure 20 shows the effect of JPEG and JPEG2000 compression on the genuine matching scores of the I1 SDK. Four thousand

OPS images, in KIND 3, KIND 7, and KIND 16 format, are compressed to attain codestream sizes from 6000 bytes down

to 800 bytes. These are compared with a second capture in uncompressed KIND 1 format. The results are displayed as

groups of six boxplots: one for each of the three formats under JPEG2000 compression and an analogous three for JPEG.

Each boxplot summarizes the distribution of the genuine scores. High scores lead to false rejection. The figure

. plots genuine score on the left axis as the primary response variable,

. plots a separability measure on the right axis,

. shows the median uncompressed score as a grey horizontal baseline,

. shows, as horizontal lines from top to bottom, thresholds corresponding to global FMR rates of 10−4, 10−3 and 10−2

- scores above a horizontal line would be falsely rejected, and

. gives FNMR values in the top row.

Identical figures for all IREX SDKs are shown in the appendices of the IREX SUPPLEMENT39. The notable results are as

follows.

. The genuine scores degrade much more rapidly with JPEG compression. This applies across all recognition algorithms and all formats. This contraindicates use of JPEG.

. The cropped and masked KIND 7 record can be compressed to about half the size of the KIND 3 record for the same

increase in genuine comparison score.

. The boxplots themselves don’t show the fraction of the genuine scores above the threshold lines. Those numbers

are presented at the top of each plot. For KIND 16 some SDKs, particularly A1, G1 and J1, give fewer false rejections

with JPEG than with JPEG2000. This arises because most of the size reduction for these SDKs comes from radial and

circumferential sampling such that the compression itself is very light. Note also that any DCT tiling artifacts will

not appear as such after reverse polar transformation.

. The number of scores in the tails is not directly dependent on the change in the scores. This arises because the

upward drift of the scores is related to the gradual escalating compression damage to the iris texture, whereas

outright false non-matches are the result of a failed localization of the relevant pupil and limbic boundaries.

. The KIND 3 and KIND 7 records tend to give fewer false rejections - than KIND 16. Comparing KIND 3 and KIND 7,

some SDKs demonstrate lower FNMR on the former. This topic is addressed more fully in later subsections.

39Available at http://iris.nist.gov/irex/irex appendices.pdf

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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8.1.2. CHANGE IN THE GENUINE DISTRIBUTION UNDER COMPRESSION

Figure 21 presents the change in genuine score under compression. It uses the same annotated boxplot format. Across all

SDKs the key observations from the change boxplots are as follows.

. As more compression is applied, genuine scores rise asymptotically from zero. From uncompressed to 6000 bytes,

the median change in genuine score is close to zero. In addition, the inter-quartile range depicted by the colored

boxes is also very small. The IREX study has not established a functional model for the change in score ∆d(c) at

compression ratio c, but the figure shows that any model will have smaller residuals if it incorporates the uncompressed score d(1) vs. a fixed intercept.

. More important than the change in genuine score, however, is that it is long-tailed. The change in some cases is

sufficient to cause false rejection and is evidence of a failed localization. This is evident in the second and third

flights of boxplots in Figure 20 where the tails of the KIND 16 extend much higher than KIND 7 and include error

even with the lightest compression (i.e. at 6000 bytes).

8.1.3. THE IMPOSTOR DISTRIBUTION UNDER COMPRESSION

Figures 22 and 23 are the impostor analogues of the prior two figures. They show, for the I1 SDK , the distributions

of the impostor scores, and the change in impostor scores, under compression. Identical figures for all IREX SDKs are

shown the appendices of the IREX SUPPLEMENT40. To show any unwelcome reduction in scores under compression (i.e.

increased false match rate) the plots include horizontal lines which give the FMR = 10−4, 10−3 and 10−2 quantiles of the

uncompressed impostor distributions. The key results across SDKs are as follows.

. Under JPEG compression some SDKs (I1, I2, D1, E1) give an increased FMR - the impostor distribution moves to

the left. While this usually only happens at sizes below 2000 bytes it contraindicates use of JPEG compression,

particularly in one-to-many applications.

. Under JPEG compression, some SDKs (B1, F1, G1, H1) show movement of the impostor distribution to the right

(higher). This is benign. It is indicative that the feature based representation of the iris is not sensitive to the DCT

tiling artifacts produced under JPEG compression.

. Under JPEG2000 compression, the impostor distributions are more stable. There is a slight increase in variance

of the impostor scores under compression. Some SDKs (I1, G1, H1) exhibit a decrease in impostor scores which

manifests itself as higher FMR in the DETs of the next section.

. The figures do not show that any of the compact formats is better or worse with respect to the properties of the

impostor distribution. This aspect is discussed in later section.

8.1.4. THE EFFECT OF COMPRESSION ON FMR

The boxplots of the previous section were essentially qualitative in their presentation of the effect of compression. In

addition, by conducting only 4000 impostor comparisons, the data did not support quantification of the effect of compression on the relevant metric, FMR. In this section a number of different figures and tables give more precise statements

of the effect.

Figure 24 shows DET characteristics for four selected SDKs matching instances of the four standard IREX records. The

compact formats, KIND 3, KIND 7, and KIND 16, were prepared by the I1 SDK and compressed to 3000 bytes using JPEG2000.

40Available at http://iris.nist.gov/irex/irex appendices.pdf

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

60
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 800.k3.J2K 1000.k3.J2K 1200.k3.J2K 1400.k3.J2K 1600.k3.J2K 1800.k3.J2K 2000.k3.J2K 2200.k3.J2K 2400.k3.J2K 2600.k3.J2K 2800.k3.J2K 3000.k3.J2K 4000.k3.J2K 6000.k3.J2K 800.k7.J2K 1000.k7.J2K 1200.k7.J2K 1400.k7.J2K 1600.k7.J2K 1800.k7.J2K 2000.k7.J2K 2200.k7.J2K 2400.k7.J2K 2600.k7.J2K 2800.k7.J2K 3000.k7.J2K 4000.k7.J2K 6000.k7.J2K 800.k16.J2K 1000.k16.J2K 1200.k16.J2K 1400.k16.J2K 1600.k16.J2K 1800.k16.J2K 2000.k16.J2K 2200.k16.J2K 2400.k16.J2K 2600.k16.J2K 2800.k16.J2K 3000.k16.J2K 4000.k16.J2K 6000.k16.J2K 800.k3.JPG 1000.k3.JPG 1200.k3.JPG 1400.k3.JPG 1600.k3.JPG 1800.k3.JPG

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1.401.351.301.251.201.151.101.05

Impostor Score

k3k7k16

 2.103 1.396 0.690−0.016−0.723−1.429−2.136−2.842

0.004

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Figure 22: The distribution of I1 native impostor comparison scores by size of the compressed image, KIND and the compression algorithm. The right axis scale gives the correspondingvalue for d

0 = (s −

μI )/

q

0.5(σ

2

I + σ

2

G) for impostor score s. The three blue lines correspond, from the top, to FMR of 10{−2,−3,−4}. The lower grey line refers to the median score

obtained from comparison of uncompressed KIND 3 images. Any comparison involving a failed template is excluded. Above the boxplots are FMR values at the threshold that gives FMR= 10−3 on uncompressed images. These figures are computed from only 4000 comparisons so the FMR values and the tails of the impostor distribution are poorly characterized. Note thatthe iris record size on the horizontal axis is not evenly spaced above 3000 bytes.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

61
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 800.k3.J2K 1000.k3.J2K 1200.k3.J2K 1400.k3.J2K 1600.k3.J2K 1800.k3.J2K 2000.k3.J2K 2200.k3.J2K 2400.k3.J2K 2600.k3.J2K 2800.k3.J2K 3000.k3.J2K 4000.k3.J2K 6000.k3.J2K 800.k7.J2K 1000.k7.J2K 1200.k7.J2K 1400.k7.J2K 1600.k7.J2K 1800.k7.J2K 2000.k7.J2K 2200.k7.J2K 2400.k7.J2K 2600.k7.J2K 2800.k7.J2K 3000.k7.J2K 4000.k7.J2K 6000.k7.J2K 800.k16.J2K 1000.k16.J2K 1200.k16.J2K 1400.k16.J2K 1600.k16.J2K 1800.k16.J2K 2000.k16.J2K 2200.k16.J2K 2400.k16.J2K 2600.k16.J2K 2800.k16.J2K 3000.k16.J2K 4000.k16.J2K 6000.k16.J2K 800.k3.JPG 1000.k3.JPG 1200.k3.JPG 1400.k3.JPG 1600.k3.JPG 1800.k3.JPG

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−0.05−0.10−0.15

0.150.100.050.00

Change in Impostor Score

k3 k7 k16

Figure 23: The distribution of the increase in I1 native impostor comparison scores between the uncompressed “parent” and the compressed image,arranged by size, KIND and the compression algorithm. The images are from the OPS dataset. Any comparison involving a failed template is excluded.

Note that the iris record size on the horizontal axis is not evenly spaced above 3000 bytes.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

62
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

All KIND 1 records were used without compression. The I1 SDK was adopted as a reference generator because it is one

the implementations that never failed to produce IREX records, and because it gives good performance natively. The use

of a common reference allows comparison of the resilience of the various matching algorithms under compression. It

removes cropping margins as a confounding factor.

The Figure 24 DETs are computed for the ALL-FAILURES partition of the OPS dataset. As described in section 5.1.1, the

subset is the union of all difficult comparisons, and therefore gives artificially high FNMR values. For comparative testing

this is of no consequence. The notable results are as follows.

. For all SDKs and formats, FMR with compressed enrollment images differs from the baseline value obtained for

uncompressed images. For most SDKs FMR increases as compression is applied. A notable exception is B1 for

which FMR always decreases. The largest changes are observed for I1 and I2 where FMR changes by up to a factor

of 10.

. The smallest changes are observed for the KIND 7 format, followed (usually) by KIND 16 and finally KIND 3. This

order reflects the resilience of the format to compression: KIND 7 reserves more bits for encoding of the iris texture;

KIND 3 the fewest.

. The variation is related to the amount of compression. For the dotted lines, the numbers of bits per pixel is 0.2; for

the solid lines, bpp varies between 0.15 to 0.21 (the fifth and ninety-fifth percentiles) depending on the image size.

It is generally the case that the change in FMR is larger for the solid lines.

8.2. SUFFICIENCY OF THE STANDARD FORMATS

The original and main goal of IREX is to establish formats and compression parameters for compact iris images. A

necessary condition for a biometric data interchange format is that it should offer recognition performance comparable

with that available from unconstrained, non-standard formats. This requirement is more fundamental than whether

the format is interoperable. The word sufficiency refers to some statement of whether the data format is quantitatively

sufficient, i.e. fit-for-purpose. This section gives quantitative statements of sufficiency.

The candidate formats, KIND 3, KIND 7 and KIND 16, were described and depicted in Figure 3. Images in these formats

were subjected to PNG , JPEG and JPEG2000 compression. These algorithms were described in section 3. The viability

of various combinations is assessed in terms of the core recognition error rates measured when samples are compared.

Thus performance is stated by using DET characteristics and tabulating FNMR values at fixed FMR points. In addition

we tabulate FNMR and FMR values at fixed threshold. Note that the most recent publications on the compression of iris

data[19, 54] both quoted FNMR at FMR = 0.0001 or 0.001.

To establish the suitability of the three formats for compact representation of iris images, this section reports fundamental

matching error rates for images from three datasets. It reports results for the application scenarios given in section 1.1.

Sufficiency results are tabulated in Tables 9, 10 and 11. Each table reports, for each IREX SDK, four different performance

metrics. These are presented in three sub-tables (a), (b) and (c).

. Table(a): FNMR at the threshold, τb, fixed to give FMR = 0.001 over the approximately 109 KIND 1 vs. KIND 1 impostor

comparisons present in the OPS - to - OPS and OPS - to - ICE comparison sets.

. Table(a): FNMR at a threshold fixed to give FMR = 0.001 on the specific combination of formats and compression

sizes in use41.

41This bullet and the preceding one represent two different approaches to setting the threshold: The first sets a global threshold whatever the kind

and source of the data; the second tailors the threshold to the specific formatting of the data. If the iris recognition algorithm’s impostor distribution

was monolithic and invariant to format and compression, the threshold could be global.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

63
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.115 0.105 0.095 0.085 0.080 0.075 0.070 0.065 0.060 0.055

FMR

FNMR

0.742

+

0.763

+

0.787

+

0.808

+

0.83

+

0.851

+

0.874

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.115 0.105 0.095 0.085 0.080 0.075 0.070 0.065 0.060 0.055

FMR

FNMR

0.742

+

+

0.763

+

+

0.787

+ +

0.808

+

+

0.83

+

+

0.851

+

+

0.874

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.115 0.105 0.095 0.085 0.080 0.075 0.070 0.065 0.060 0.055

FMR

FNMR

0.742

+

+

0.763

+

+

0.787

+

+

0.808

+

+

0.83

+

+

0.851

+

+

0.874

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.115 0.105 0.095 0.085 0.080 0.075 0.070 0.065 0.060 0.055

FMR

FNMR

0.742

+

+

0.763

+

+

0.787

+

+

0.808

+

+

0.83

+

+

0.851

+

+

0.874

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.115 0.105 0.095 0.085 0.080 0.075 0.070 0.065 0.060 0.055

FMR

FNMR

0.742

+

+

0.763

+

+

0.787

+

+

0.808

+

+

0.83

+

+

0.851

+

+

0.874

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.115 0.105 0.095 0.085 0.080 0.075 0.070 0.065 0.060 0.055

FMR

FNMR

0.742

+

+

0.763

+

+

0.787

+

+

0.808

+

+

0.83

+

+

0.851

+

+

0.874

+

+

OPS k1 lossless vs. k1 losslessOPS k1 lossless vs. k3 J2K Comp 3000BOPS k1 lossless vs. k16 J2K Comp 3000BOPS k1 lossless vs. k7 J2K Comp 3000BOPS k1 lossless vs. k3 J2K BPP = 0.2OPS k1 lossless vs. k7 J2K BPP = 0.2

(a) A1 OPS

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.20 0.10 0.05

FMR

FNMR

943

+

948

+

955

+

961

+

969

+

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+

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+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

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FMR

FNMR

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+

+

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+

+

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+

+

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+

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+

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+

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+

+

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FMR

FNMR

+

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+

+

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+

+

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+

+

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+

+

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+

+

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+

+

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FMR

FNMR

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+

+

948

+

+

955

+

+

961

+

+

969

+

+

976

+

+

983

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.20 0.10 0.05

FMR

FNMR

+

948

+

+

955

+

+

961

+ +

969

+ +

976

+

+

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+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.20 0.10 0.05

FMR

FNMR

+

948

+

+

955

+

+

961

+

+

969

+

+

976

+

+

983

+

+

OPS k1 lossless vs. k1 losslessOPS k1 lossless vs. k3 J2K Comp 3000BOPS k1 lossless vs. k16 J2K Comp 3000BOPS k1 lossless vs. k7 J2K Comp 3000BOPS k1 lossless vs. k3 J2K BPP = 0.2OPS k1 lossless vs. k7 J2K BPP = 0.2

(b) B1 OPS

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.30 0.26 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08

FMR

FNMR

0.501

+

0.518

+

0.538

+

0.557

+

0.577

+

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+

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+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.30 0.26 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08

FMR

FNMR

0.501

+

+

0.518

+

+

0.538

+

+

0.557

+

+

0.577

+

+

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+

+

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+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.30 0.26 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08

FMR

FNMR

0.501

+

+

0.518

+

+

0.538

+

+

0.557

+

+

0.577

+

+

0.598

+

+

0.623

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.30 0.26 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08

FMR

FNMR 0.501

+

+

0.518

+

+

0.538

+

+

+0.557

+

0.577

+

+

0.598

+

+

0.623

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.30 0.26 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08

FMR

FNMR

0.501

+

+

0.518

+

+

0.538

+

+

+

0.557

+

+0.577

+

0.598

+

+

0.623

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.30 0.26 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08

FMR

FNMR

0.501

+

+

0.518

+

+

0.538

+

+

0.557

+

+

+

0.577

+

0.598

+

+

0.623

+

+

OPS k1 lossless vs. k1 losslessOPS k1 lossless vs. k3 J2K Comp 3000BOPS k1 lossless vs. k16 J2K Comp 3000BOPS k1 lossless vs. k7 J2K Comp 3000BOPS k1 lossless vs. k3 J2K BPP = 0.2OPS k1 lossless vs. k7 J2K BPP = 0.2

(c) E1 OPS

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.034 0.032 0.030 0.028 0.026 0.025 0.024 0.023 0.022 0.021 0.020

FMR

FNMR

1.092

+

1.105

+

1.120

+

1.133

+

1.148

+

1.162

+

1.179

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.034 0.032 0.030 0.028 0.026 0.025 0.024 0.023 0.022 0.021 0.020

FMR

FNMR

1.092

+

+

1.105

+

+

1.120

+

+

1.133

+

+

1.148

+

+

1.162

+

+

1.179

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.034 0.032 0.030 0.028 0.026 0.025 0.024 0.023 0.022 0.021 0.020

FMR

FNMR

1.092

+

+

1.105

+

+

1.120

+

+

1.133

+

+

1.148

+

+

1.162

+

+

1.179

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.034 0.032 0.030 0.028 0.026 0.025 0.024 0.023 0.022 0.021 0.020

FMR

FNMR

1.092

+

+

1.105

+

+

1.120

+

+

1.133

+

+

1.148

+

+

1.162

+ +

1.179

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.034 0.032 0.030 0.028 0.026 0.025 0.024 0.023 0.022 0.021 0.020

FMR

FNMR

1.092

++

1.105

+

+

1.120

+

+

1.133

+

+

1.148

+

+

1.162

+

+

1.179

+

+

1e−05 5e−05 1e−04 5e−04 1e−03 5e−03 1e−02

0.034 0.032 0.030 0.028 0.026 0.025 0.024 0.023 0.022 0.021 0.020

FMR

FNMR

1.092

+

+

1.105

+

+

1.120

+

+

1.133

+

+

1.148

+

+

1.162

+

+

1.179

+

+

OPS k1 lossless vs. k1 losslessOPS k1 lossless vs. k3 J2K Comp 3000BOPS k1 lossless vs. k16 J2K Comp 3000BOPS k1 lossless vs. k7 J2K Comp 3000BOPS k1 lossless vs. k3 J2K BPP = 0.2OPS k1 lossless vs. k7 J2K BPP = 0.2

(d) I1 OPS

Figure 24: DET characteristics for selected SDKs applied to records of the three KINDS compressed using JPEG2000 to 3000 bytes. The images are the pairs in the ALL- FAILURES partition of the OPS dataset: This leads to higher error rates than for the entire OPS dataset. Also shown is the DET for the same images without compression,

in KIND 1 format. All the KIND 3, KIND 7 and KIND 16 images were produced by the I1 SDK which is adopted here as a reference for the purpose of normalizing away

geometrical effects such as cropping margins and polar sampling rates. The grey lines link points of equal threshold. Note the FMR variation for some SDKs across

KINDS.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

64
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

(a) OPS FNMR

Kind and Compression Native false non-match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0047 0.0176 0.0040 0.0104 0.0331 0.0318 0.0215 0.0145 0.0075 0.0072 0.0319 0.0077 0.0074 0.0362 0.0356 0.0018 0.0017 0.0110 0.00472 k3 Uncomp k1 Uncomp 0.0039 0.0167 0.0042 0.0104 0.0443 0.0445 0.0210 0.0138 0.0071 0.0068 0.0314 0.0076 0.0073 0.0341 0.0341 0.0018 0.0017 0.0109 0.00453 k7 Uncomp k1 Uncomp 0.0042 0.0165 0.0041 0.0105 0.0342 0.0337 0.0224 0.0154 0.0111 0.0105 0.0325 0.0109 0.0097 0.0398 0.0382 0.0019 0.0018 0.0110 0.00454 k16 Uncomp k1 Uncomp 0.0050 0.0173 0.0642 0.1094 - - - - - - 0.0325 0.0140 0.0135 0.0457 0.0477 0.0019 0.0018 0.0110 0.00485 k3 J2K 2000B k1 Uncomp 0.0054 0.0187 0.0070 0.0122 0.0420 0.0461 0.0215 0.0151 0.0070 0.0067 0.0307 0.0142 0.0135 0.0404 0.0376 0.0091 0.0091 0.0355 0.03286 k3 J2K 2000B k1 Uncomp 0.0055 0.0197 0.0074 0.0116 - 0.0479 0.0215 0.0154 0.0070 0.0067 0.0319 0.0168 0.0159 0.0446 0.0436 0.0143 0.0143 0.0472 0.04377 k7 J2K 2000B k1 Uncomp 0.0041 0.0167 0.0046 0.0104 0.1402 0.1398 0.0222 0.0156 0.0113 0.0107 0.0312 0.0126 0.0118 0.0420 0.0398 0.0020 0.0022 0.0108 0.00548 k7 J2K 2000B k1 Uncomp 0.0042 0.0169 0.0046 0.0103 0.1411 0.1411 0.0222 0.0157 0.0112 0.0107 0.0314 0.0135 0.0127 0.0442 0.0434 0.0023 0.0024 0.0113 0.00589 k16 J2K 2000B k1 Uncomp 0.0140 0.0292 0.1802 0.2493 - - - - - - 0.1121 0.0570 0.0618 0.0549 0.0521 0.0137 0.0135 0.0545 0.056110 k16 J2K 2000B k1 Uncomp 0.0144 0.0315 0.1802 0.2317 - - - - - - 0.1134 0.0657 0.0701 0.0623 0.0638 0.0248 0.0245 0.0680 0.077611 k3 J2K 2000B k3 J2K 2000B 0.0093 0.0237 0.0050 0.0080 0.0537 0.0589 0.0203 0.0148 0.0070 0.0070 0.0309 0.0254 0.0244 0.0507 0.0416 0.0055 0.0051 0.0419 0.045012 k7 J2K 2000B k7 J2K 2000B 0.0035 0.0164 0.0039 0.0056 0.2322 0.2290 0.0229 0.0167 0.0142 0.0135 0.0309 0.0159 0.0156 0.0481 0.0403 0.0020 0.0023 0.0115 0.006113 k16 J2K 2000B k16 J2K 2000B 0.0386 0.0555 0.2725 0.4031 - - - - - - 0.1826 0.1173 0.1250 0.0841 0.0653 0.0064 0.0063 0.0594 0.073914 k3 J2K 2000B k7 J2K 2000B 0.0051 0.0192 0.0042 0.0063 0.1491 0.1484 0.0222 0.0161 0.0104 0.0099 0.0317 0.0218 0.0209 0.0484 0.0405 0.0048 0.0051 0.0271 0.024115 k3 J2K 2000B k16 J2K 2000B 0.0244 0.0420 0.1526 0.2471 - - - - - - 0.1107 0.0757 0.0806 0.0705 0.0540 0.0064 0.0064 0.0538 0.063216 k7 J2K 2000B k16 J2K 2000B 0.0173 0.0330 0.1539 0.2286 - - - - - - 0.1126 0.0839 0.0863 0.0668 0.0540 0.0071 0.0076 0.0436 0.0483

(b) OPS cFNMR

Kind and Compression Conditional native false non-match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00002 k3 Uncomp k1 Uncomp 0.0001 0.0001 0.0004 0.0002 0.0139 0.0151 0.0004 0.0004 0.0001 0.0001 0.0007 0.0001 0.0000 0.0010 0.0014 0.0001 0.0000 0.0006 0.00053 k7 Uncomp k1 Uncomp 0.0002 0.0002 0.0004 0.0004 0.0040 0.0043 0.0016 0.0016 0.0047 0.0043 0.0012 0.0048 0.0036 0.0071 0.0058 0.0001 0.0001 0.0006 0.00044 k16 Uncomp k1 Uncomp 0.0011 0.0013 0.0604 0.1002 - - - - - - 0.0018 0.0071 0.0074 0.0112 0.0137 0.0001 0.0001 0.0005 0.00105 k3 J2K 2000B k1 Uncomp 0.0017 0.0031 0.0030 0.0025 0.0141 0.0187 0.0013 0.0016 0.0005 0.0004 0.0029 0.0073 0.0071 0.0083 0.0061 0.0074 0.0074 0.0260 0.02886 k7 J2K 2000B k1 Uncomp 0.0002 0.0009 0.0006 0.0005 0.1148 0.1151 0.0017 0.0019 0.0048 0.0044 0.0025 0.0061 0.0056 0.0097 0.0079 0.0003 0.0006 0.0007 0.00147 k16 J2K 2000B k1 Uncomp 0.0100 0.0133 0.1769 0.2417 - - - - - - 0.0866 0.0506 0.0556 0.0213 0.0187 0.0120 0.0118 0.0447 0.05258 k3 J2K 2000B k3 J2K 2000B 0.0062 0.0093 0.0015 0.0020 0.0279 0.0337 0.0014 0.0024 0.0012 0.0013 0.0061 0.0191 0.0183 0.0211 0.0126 0.0038 0.0035 0.0331 0.04199 k7 J2K 2000B k7 J2K 2000B 0.0004 0.0012 0.0005 0.0004 0.2110 0.2088 0.0029 0.0037 0.0087 0.0083 0.0044 0.0098 0.0098 0.0177 0.0111 0.0004 0.0007 0.0020 0.002910 k16 J2K 2000B k16 J2K 2000B 0.0351 0.0411 0.2698 0.3978 - - - - - - 0.1608 0.1113 0.1195 0.0519 0.0338 0.0050 0.0049 0.0512 0.070711 k3 J2K 2000B k7 J2K 2000B 0.0023 0.0042 0.0008 0.0007 0.1258 0.1259 0.0024 0.0032 0.0048 0.0046 0.0061 0.0154 0.0151 0.0183 0.0114 0.0032 0.0035 0.0181 0.020912 k3 J2K 2000B k16 J2K 2000B 0.0211 0.0271 0.1493 0.2396 - - - - - - 0.0872 0.0695 0.0746 0.0395 0.0240 0.0047 0.0049 0.0454 0.060013 k7 J2K 2000B k16 J2K 2000B 0.0139 0.0178 0.1504 0.2207 - - - - - - 0.0884 0.0776 0.0802 0.0356 0.0236 0.0054 0.0059 0.0348 0.0449

(c) OPS FMR

Kind and Compression Native false match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0014 0.0012 0.0012 0.0010 0.0009 0.0009 0.0006 0.0016 0.0005 0.0006 0.0005 0.0012 0.0012 0.0009 0.0013 0.0012 0.0012 0.0014 0.00152 k3 J2K 2000B k1 Uncomp 0.0013 0.0020 0.0013 0.0006 - 0.0016 0.0012 0.0025 0.0007 0.0007 0.0018 0.0023 0.0023 0.0022 0.0045 0.0049 0.0048 0.0026 0.00263 k7 J2K 2000B k1 Uncomp 0.0016 0.0017 0.0011 0.0005 0.0016 0.0016 0.0008 0.0019 0.0006 0.0006 0.0011 0.0016 0.0017 0.0019 0.0036 0.0028 0.0028 0.0028 0.00284 k16 J2K 2000B k1 Uncomp 0.0012 0.0020 0.0009 0.0005 - - - - - - 0.0017 0.0022 0.0022 0.0022 0.0046 0.0052 0.0052 0.0022 0.0023

Table 9: Accuracy for OPS records stored in the KINDS identified in the rows. The compression applied to the enrollment and verification samples is identified

in columns three and five. The remaining column headers identify the SDKs. All comparisons are native. Top: FNMR at the threshold for which FMR = 0.001 onuncompressed KIND 1 comparisons except in rows with red text where FNMR is given at the threshold for which FMR = 0.001 on the kind of images identified in therow. Cells appear dark green when the error rate is less than the baseline on row 1, light green when it is less than the baseline times 1.1, light red when it exceeds threetimes the baseline, and dark red when the factor is more than ten times. Center: Conditional FNMR at the threshold for which FMR = 0.001 on uncompressed KIND 1

comparisons. FNMR is conditional on the same parent KIND 1 image being correctly matched at the same threshold. Cells appear dark green when less than or equalto 0.005, light green when the cFNMR is less than or equal to 0.01, light red when error rate is above 0.03, and dark red above 0.1. Bottom: FMR at the threshold forwhich FMR = 0.001 on uncompressed KIND 1 comparisons. The first row elements are not 0.001 because the global threshold was established across all OPS - to - OPSand OPS - to - ICE comparisons. Cells appear green when FMR is less than or equal to 0.001, light red when above 0.002, and dark red above 0.003.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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(a) ICE FNMR

Kind and Compression Native false non-match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0116 0.0174 0.0063 0.0102 0.0335 0.0342 0.0176 0.0205 0.0256 0.0251 0.0197 0.0326 0.0317 0.0248 0.0276 0.0056 0.0053 0.0124 0.01032 k3 Uncomp k1 Uncomp 0.0095 0.0152 0.0067 0.0110 0.0344 0.0379 0.0167 0.0194 0.0244 0.0239 0.0198 0.0307 0.0312 0.0255 0.0287 0.0056 0.0052 0.0118 0.00873 k7 Uncomp k1 Uncomp 0.0094 0.0149 0.0069 0.0112 0.0339 0.0345 0.0161 0.0206 0.0244 0.0237 0.0201 0.0505 0.0459 0.0273 0.0301 0.0056 0.0055 0.0118 0.00864 k16 Uncomp k1 Uncomp 0.0095 0.0150 0.1546 0.2148 - - - - - - 0.0382 0.0357 0.0354 0.0261 0.0284 0.0055 0.0053 0.0111 0.00885 k3 J2K 2000B k1 Uncomp 0.0240 0.0357 0.0213 0.0311 0.0643 0.0690 0.0220 0.0326 0.0306 0.0310 0.0335 0.0704 0.0699 0.0633 0.0606 0.0291 0.0287 0.0884 0.09386 k3 J2K 2000B k1 Uncomp 0.0247 0.0399 0.0245 0.0291 0.0823 0.0870 0.0307 0.0352 0.0367 0.0365 0.0544 0.0785 0.0778 0.0829 0.0879 0.0509 0.0504 0.0990 0.10467 k7 J2K 2000B k1 Uncomp 0.0121 0.0179 0.0099 0.0150 0.0461 0.0467 0.0167 0.0220 0.0245 0.0241 0.0264 0.0564 0.0561 0.0410 0.0406 0.0082 0.0087 0.0381 0.03928 k7 J2K 2000B k1 Uncomp 0.0124 0.0187 0.0106 0.0145 0.0534 0.0541 0.0189 0.0223 0.0257 0.0251 0.0367 0.0583 0.0581 0.0499 0.0533 0.0111 0.0109 0.0428 0.04409 k16 J2K 2000B k1 Uncomp 0.0522 0.0715 0.3360 0.4115 - - - - - - 0.1076 0.1400 0.1434 0.0834 0.0786 0.0458 0.0456 0.1717 0.171110 k16 J2K 2000B k1 Uncomp 0.0534 0.0833 0.3588 0.4024 - - - - - - 0.1571 0.1611 0.1638 0.1146 0.1231 0.0961 0.0960 0.1925 0.191511 k3 J2K 2000B k3 J2K 2000B 0.0454 0.0631 0.0136 0.0202 0.0856 0.0947 0.0246 0.0462 0.0398 0.0405 0.0499 0.1089 0.1106 0.1049 0.0740 0.0239 0.0238 0.1025 0.118112 k7 J2K 2000B k7 J2K 2000B 0.0129 0.0188 0.0079 0.0103 0.0586 0.0587 0.0162 0.0235 0.0234 0.0229 0.0302 0.0622 0.0624 0.0593 0.0468 0.0088 0.0099 0.0543 0.060913 k16 J2K 2000B k16 J2K 2000B 0.1272 0.1542 0.3955 0.5090 - - - - - - 0.1801 0.2478 0.2550 0.1518 0.1004 0.0342 0.0343 0.1843 0.198214 k3 J2K 2000B k7 J2K 2000B 0.0267 0.0386 0.0114 0.0153 0.0761 0.0797 0.0215 0.0358 0.0311 0.0316 0.0406 0.0932 0.0938 0.0876 0.0664 0.0216 0.0222 0.0862 0.098315 k3 J2K 2000B k16 J2K 2000B 0.0878 0.1141 0.3142 0.4348 - - - - - - 0.1308 0.1843 0.1890 0.1288 0.0878 0.0330 0.0331 0.1702 0.192016 k7 J2K 2000B k16 J2K 2000B 0.0628 0.0825 0.3077 0.4076 - - - - - - 0.1188 0.1909 0.1922 0.1052 0.0750 0.0337 0.0348 0.1559 0.1727

(b) ICE cFNMR

Kind and Compression Conditional native false non-match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00002 k3 Uncomp k1 Uncomp 0.0009 0.0009 0.0007 0.0013 0.0063 0.0096 0.0019 0.0026 0.0008 0.0007 0.0027 0.0004 0.0004 0.0038 0.0050 0.0003 0.0002 0.0017 0.00123 k7 Uncomp k1 Uncomp 0.0011 0.0012 0.0010 0.0017 0.0061 0.0068 0.0031 0.0052 0.0046 0.0044 0.0025 0.0245 0.0198 0.0065 0.0075 0.0007 0.0008 0.0016 0.00124 k16 Uncomp k1 Uncomp 0.0016 0.0020 0.1508 0.2090 - - - - - - 0.0227 0.0106 0.0116 0.0052 0.0059 0.0004 0.0005 0.0012 0.00135 k3 J2K 2000B k1 Uncomp 0.0153 0.0217 0.0155 0.0217 0.0368 0.0409 0.0079 0.0164 0.0098 0.0105 0.0170 0.0462 0.0460 0.0428 0.0383 0.0243 0.0242 0.0799 0.08746 k7 J2K 2000B k1 Uncomp 0.0034 0.0039 0.0040 0.0055 0.0183 0.0184 0.0038 0.0063 0.0050 0.0050 0.0091 0.0304 0.0305 0.0199 0.0181 0.0033 0.0039 0.0286 0.03197 k16 J2K 2000B k1 Uncomp 0.0438 0.0584 0.3350 0.4094 - - - - - - 0.0930 0.1159 0.1203 0.0633 0.0565 0.0411 0.0413 0.1647 0.16598 k3 J2K 2000B k3 J2K 2000B 0.0386 0.0514 0.0086 0.0130 0.0609 0.0694 0.0127 0.0328 0.0221 0.0232 0.0348 0.0890 0.0902 0.0866 0.0544 0.0197 0.0198 0.0958 0.11369 k7 J2K 2000B k7 J2K 2000B 0.0060 0.0069 0.0028 0.0034 0.0333 0.0330 0.0063 0.0108 0.0076 0.0075 0.0139 0.0397 0.0397 0.0399 0.0267 0.0043 0.0053 0.0466 0.055510 k16 J2K 2000B k16 J2K 2000B 0.1216 0.1446 0.3964 0.5102 - - - - - - 0.1683 0.2291 0.2375 0.1348 0.0816 0.0303 0.0307 0.1789 0.194711 k3 J2K 2000B k7 J2K 2000B 0.0197 0.0267 0.0063 0.0080 0.0511 0.0542 0.0104 0.0226 0.0140 0.0148 0.0249 0.0721 0.0725 0.0688 0.0465 0.0172 0.0178 0.0792 0.093512 k3 J2K 2000B k16 J2K 2000B 0.0816 0.1035 0.3129 0.4332 - - - - - - 0.1175 0.1648 0.1699 0.1112 0.0685 0.0289 0.0292 0.1645 0.188413 k7 J2K 2000B k16 J2K 2000B 0.0562 0.0714 0.3063 0.4055 - - - - - - 0.1049 0.1699 0.1720 0.0868 0.0553 0.0293 0.0306 0.1499 0.1688

(c) ICE FMR

Kind and Compression Native false match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0010 0.0010 0.0023 0.0024 0.0017 0.0016 0.0024 0.0009 0.0021 0.0021 0.0035 0.0009 0.0009 0.0013 0.0009 0.0015 0.0015 0.0005 0.00052 k3 J2K 2000B k1 Uncomp 0.0011 0.0016 0.0015 0.0007 0.0029 0.0029 0.0050 0.0015 0.0031 0.0027 0.0068 0.0017 0.0017 0.0025 0.0039 0.0064 0.0064 0.0014 0.00143 k7 J2K 2000B k1 Uncomp 0.0011 0.0013 0.0012 0.0006 0.0026 0.0026 0.0029 0.0010 0.0024 0.0021 0.0056 0.0012 0.0012 0.0023 0.0032 0.0036 0.0028 0.0013 0.00134 k16 J2K 2000B k1 Uncomp 0.0011 0.0018 0.0014 0.0007 - - - - - - 0.0070 0.0020 0.0019 0.0027 0.0044 0.0085 0.0084 0.0014 0.0015

Table 10: Accuracy for ICE records stored of the KINDS identified in the rows. The compression applied to the enrollment and verification samples is identified

in columns three and five. The remaining column headers identify the SDKs. All comparisons are native. Top: FNMR at the threshold for which FMR = 0.001 onuncompressed KIND 1 comparisons except in rows with red text where FNMR is given at the threshold for which FMR = 0.001 on the kind of images identified in therow. Cells appear dark green when the error rate is less than the baseline on row 1, light green when it is less than the baseline times 1.1, light red when it exceeds threetimes the baseline, and dark red when the factor is more than ten times. Center: Conditional FNMR at the threshold for which FMR = 0.001 on uncompressed KIND 1comparisons. FNMR is conditional on the same parent KIND 1 image being correctly matched at the same threshold. Cells appear dark green when less than or equalto 0.005, light green when the cFNMR is less than or equal to 0.01, light red when error rate is above 0.03, and dark red above 0.1. Bottom: FMR at the threshold forwhich FMR = 0.001 on uncompressed KIND 1 comparisons. The first row elements are not 0.001 because the global threshold was established across all OPS - to - OPSand OPS - to - ICE comparisons. Cells appear green when FMR is less than or equal to 0.001, light red when above 0.002, and dark red above 0.003.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

66
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(a) BATH FNMR

Kind and Compression Native false non-match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0119 0.0131 0.0056 0.0073 0.0222 0.0223 0.0206 0.0226 0.0332 0.0328 0.0459 0.0550 0.0374 0.0261 0.0258 0.0018 0.0016 0.0215 0.02152 k3 Uncomp k1 Uncomp 0.0122 0.0138 0.0115 0.0156 0.2184 0.2277 0.0219 0.0239 0.0423 0.0422 0.0506 0.0546 0.0386 0.0365 0.0364 0.0017 0.0015 0.0215 0.71143 k7 Uncomp k1 Uncomp 0.0123 0.0137 0.0122 0.0163 0.0600 0.0592 0.0224 0.0248 0.0351 0.0349 0.0463 0.0614 0.0459 0.0455 0.0417 0.0019 0.0021 0.0215 0.02364 k16 Uncomp k1 Uncomp 0.0177 0.0193 0.4183 0.4897 - - - - - - 0.0515 0.1113 0.1042 0.0497 0.0574 0.0019 0.0017 0.0216 0.02805 k3 J2K 2000B k1 Uncomp 0.0218 0.0253 0.0156 0.0196 0.1595 0.1905 0.0231 0.0250 0.0425 0.0424 0.0538 0.0618 0.0509 0.0466 0.0456 0.0077 0.0072 0.0435 0.04526 k3 J2K 2000B k1 Uncomp 0.0222 0.0270 0.0157 0.0193 - 0.2003 0.0240 0.0263 0.0446 0.0445 0.0573 0.0652 0.0536 0.0580 0.0570 0.0093 0.0088 0.0422 0.04247 k7 J2K 2000B k1 Uncomp 0.0179 0.0203 0.0146 0.0183 0.1955 0.1946 0.0233 0.0258 0.0353 0.0351 0.0497 0.0645 0.0540 0.0553 0.0540 0.0052 0.0084 0.0232 0.02538 k7 J2K 2000B k1 Uncomp 0.0183 0.0212 0.0147 0.0186 0.2058 0.2050 0.0242 0.0270 0.0370 0.0368 0.0527 0.0675 0.0563 0.0747 0.0713 0.0053 0.0082 0.0241 0.02649 k16 J2K 2000B k1 Uncomp 0.0915 0.1060 0.6794 0.7277 - - - - - - 0.1516 0.2459 0.2577 0.1250 0.1159 0.0720 0.0723 0.2170 0.296010 k16 J2K 2000B k1 Uncomp - - - - - - - - - - - - - - - - - - - 11 k3 J2K 2000B k3 J2K 2000B 0.0260 0.0303 0.0126 0.0161 0.1913 0.2201 0.0232 0.0254 0.0440 0.0440 0.0566 0.0689 0.0578 0.0501 0.0440 0.0036 0.0032 0.0357 0.035912 k7 J2K 2000B k7 J2K 2000B 0.0195 0.0215 0.0127 0.0156 0.2787 0.2790 0.0235 0.0266 0.0298 0.0296 0.0511 0.0673 0.0583 0.0617 0.0543 0.0037 0.0049 0.0248 0.026813 k16 J2K 2000B k16 J2K 2000B 0.1228 0.1585 0.1067 0.1354 - - - - - - 0.2062 0.2801 0.2885 0.1449 0.1084 0.0180 0.0181 0.1264 0.161714 k3 J2K 2000B k7 J2K 2000B 0.0240 0.0273 0.0134 0.0167 0.2906 0.3145 0.0244 0.0272 0.0427 0.0427 0.0566 0.0760 0.0677 0.0732 0.0638 0.0055 0.0101 0.0366 0.037515 k3 J2K 2000B k16 J2K 2000B 0.1084 0.1400 0.6420 0.7304 - - - - - - 0.1578 0.2743 0.2867 0.1633 0.1290 0.0223 0.0222 0.1813 0.249116 k7 J2K 2000B k16 J2K 2000B 0.0981 0.1190 0.6628 0.7311 - - - - - - 0.1589 0.3273 0.3406 0.1626 0.1302 0.0352 0.0496 0.1917 0.2600

(b) BATH cFNMR

Kind and Compression Conditional native false non-match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00002 k3 Uncomp k1 Uncomp 0.0021 0.0023 0.0076 0.0097 0.2125 0.2228 0.0027 0.0030 0.0101 0.0103 0.0062 0.0017 0.0033 0.0153 0.0156 0.0002 0.0002 0.0002 0.74373 k7 Uncomp k1 Uncomp 0.0024 0.0026 0.0082 0.0103 0.0418 0.0414 0.0028 0.0035 0.0066 0.0068 0.0010 0.0090 0.0115 0.0238 0.0205 0.0004 0.0008 0.0002 0.00304 k16 Uncomp k1 Uncomp 0.0084 0.0087 0.4373 0.5120 - - - - - - 0.0069 0.0642 0.0746 0.0286 0.0374 0.0004 0.0003 0.0003 0.00765 k3 J2K 2000B k1 Uncomp 0.0122 0.0145 0.0114 0.0134 0.1491 0.1826 0.0042 0.0043 0.0110 0.0112 0.0097 0.0113 0.0181 0.0261 0.0254 0.0066 0.0063 0.0238 0.02616 k7 J2K 2000B k1 Uncomp 0.0079 0.0091 0.0104 0.0121 0.1875 0.1868 0.0037 0.0044 0.0068 0.0070 0.0048 0.0121 0.0193 0.0342 0.0332 0.0039 0.0074 0.0019 0.00487 k16 J2K 2000B k1 Uncomp 0.0862 0.1004 0.7139 0.7646 - - - - - - 0.1174 0.2138 0.2419 0.1091 0.1001 0.0743 0.0748 0.2107 0.29608 k3 J2K 2000B k3 J2K 2000B 0.0176 0.0208 0.0089 0.0105 0.1835 0.2148 0.0059 0.0065 0.0139 0.0141 0.0134 0.0226 0.0285 0.0298 0.0237 0.0026 0.0023 0.0154 0.01689 k7 J2K 2000B k7 J2K 2000B 0.0104 0.0112 0.0089 0.0100 0.2773 0.2781 0.0046 0.0059 0.0085 0.0087 0.0067 0.0162 0.0247 0.0423 0.0348 0.0025 0.0038 0.0037 0.007010 k16 J2K 2000B k16 J2K 2000B 0.1196 0.1565 0.1094 0.1396 - - - - - - 0.1779 0.2526 0.2759 0.1307 0.0927 0.0176 0.0178 0.1132 0.151911 k3 J2K 2000B k7 J2K 2000B 0.0151 0.0172 0.0096 0.0108 0.2903 0.3165 0.0062 0.0072 0.0140 0.0143 0.0131 0.0271 0.0367 0.0540 0.0445 0.0044 0.0093 0.0164 0.018512 k3 J2K 2000B k16 J2K 2000B 0.1044 0.1369 0.6743 0.7675 - - - - - - 0.1247 0.2468 0.2743 0.1504 0.1140 0.0222 0.0222 0.1723 0.245713 k7 J2K 2000B k16 J2K 2000B 0.0934 0.1143 0.6964 0.7681 - - - - - - 0.1256 0.3050 0.3327 0.1501 0.1157 0.0356 0.0509 0.1835 0.2574

(c) BATH FMR

Kind and Compression Native false match rates

Enrollment Verification A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

1 k1 Uncomp k1 Uncomp 0.0006 0.0006 0.0016 0.0025 0.0004 0.0004 0.0003 0.0005 0.0004 0.0004 0.0007 0.0005 0.0005 0.0021 0.0016 0.0004 0.0004 0.0003 0.00032 k3 J2K 2000B k1 Uncomp 0.0006 0.0012 0.0004 0.0003 - 0.0009 0.0006 0.0009 0.0006 0.0005 0.0021 0.0010 0.0010 0.0032 0.0044 0.0014 0.0014 0.0005 0.00053 k7 J2K 2000B k1 Uncomp 0.0007 0.0009 0.0003 0.0002 0.0010 0.0010 0.0004 0.0007 0.0005 0.0004 0.0016 0.0007 0.0007 0.0039 0.0046 0.0008 0.0007 0.0005 0.00054 k16 J2K 2000B k1 Uncomp - - - - - - - - - - - - - - - - - - - Table 11: Accuracy for BATH records stored of the KINDS identified in the rows. The compression applied to the enrollment and verification samples is identified in

columns three and five. The remaining column headers identify the SDKs. All comparisons are native. Top: FNMR values at the threshold for which FMR = 0.001 on

uncompressed KIND 1 comparisons except in rows with red text where FNMR is given at the threshold that gives FMR = 0.001 on the kind of images identified in the

row. Cells appear dark green when the error rate is less than the baseline on row 1, light green when it is less than the baseline times 1.1, light red when it exceedsthree times the baseline, and dark red when the factor is more than ten times. Center: Conditional FNMR at the threshold for which FMR = 0.001 on uncompressed

KIND 1 comparisons. FNMR is conditional on the same parent KIND 1 image being correctly matched at the same threshold. Cells appear light green when the cFNMR

is less than or equal to 0.01, dark green when less than or equal to 0.005, light red when error rate is above 0.03, and are dark red above 0.1. Bottom: FMR values at the

threshold for which FMR = 0.001 on uncompressed KIND 1 comparisons. The first row elements are not 0.001 because the global threshold was established across all

OPS - to - OPS and OPS - to - ICE comparisons. Cells appear green when FMR is less than or equal to 0.001, light red when above 0.002, and dark red above 0.003.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

. Table (b): To isolate the effect of the KIND on accuracy, the pure Table (a) FNMR results are restated only for the

set of comparisons that are successful for raw unprocessed KIND 1 images. This corresponds to the conditional

probability

cFNMR(τ ) = P(djk > τ | d11 ≤ τ ) =

P

d∈G H(d11 − τ )(1 − H(djk − τ ))

P

d∈G H(d11 − τ )

(15)

where comparison score djk is the result of a genuine comparison of an enrollment sample of KIND j with a verification sample of KIND k. G represents the set of all genuine comparisons. This approach removes the effect of

baseline SDK performance so that the values in row 1 of the conditional FNMR Tables 9, 10 and 11 are zero. The

values in the remaining rows exclude comparisons that failed with unprocessed images and which might have

then succeeded when, for example, the crop-only format was used.

. Table(c): The empirical FMR measured at threshold τb.

8.2.1. CORE SUFFICIENCY

Referring to the sufficiency data for the three IREX datasets, i.e. Tables 9, 10 and 11, the notable results are as follows.

. When the compact formats are matched without compression (sub-table (b), rows 2 to 4) the error rates are often

close to zero. This is true for many SDKs operating on OPS images, but less true for BATH . The observation of few

format-related errors shows that the formats are not innately defective. For KIND 3 instances this shows that eye

sockets are not necessary for accurate localization. Similarly for KIND 7 instances the presence of eyelid and sclera

masking is not itself problematic. Finally, for KIND 16 instances, the rectilinear-to-polar and polar-to-rectilinear

steps introduce essentially zero errors for I1, slight increases for others (A1, A2, J1, J2, F1), significant error (G1, G2,

H1, H2) and catastrophic errors (B1, B2).

. Note however that for I1 the circumferential and radial sampling rates are markedly larger than those used by A1,

A2, J1, J2 and F1, and while this might appear necessary to avoid initial incremental increases in FNMR, it is not

true for H1 and H2 which use higher sampling rates still.

. The existence and prevalence of essentially zero error rates in rows 2 and 3 of each table supports the important

conclusion that the KIND 3 and KIND 7 compact formats can be used with lossless PNG compression with very

little elevation of error rate. From Figure 29, this allows iris records of sizes of approximately 60KB and 25KB,

respectively, to be produced.

. This recommendation could reasonably extend to KIND 16 except for the elevation of FNMR on the BATH dataset.

Except as noted, these observations are consistent across all three IREX datasets.

WITH COMPRESSION OF THE ENROLLMENT RECORD

The results in the rows 5 to 10 of Tables 9, 10 and 11 correspond to the application of JPEG2000 to compress the compact

enrollment record to 2000 bytes for comparison with an uncompressed KIND 1 instance. Error rates associated with less

compressed records will be better - the general dependency on error rate is shown in section 8.1. The current purpose is

to examine which formats are most suited for very compact storage. The following observations are drawn from rows 6,

8 and 10 of Tables 9, 10 and 11.

. The KIND 7 format is most resilient under compression. The prevalence of green-shaded elements for A1, A2, B1,

B2, D1, D2 E1, E2, I1 and I2 show accuracy is well preserved under compression. This effect was described as

resilience by Rakshit[54] and by Daugman who observed the formats to be serviceable[19] under heavy compression.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Note (Table 11(b)) that this finding is not really true for BATH images; compression itself does elevate error rates.

The reason for this, versus OPS and ICE, is not known and worthy of further investigation particularly with respect

to the photometric properties of the images.

. For KIND 3 the false non-match rates are generally worse than for KIND 7. However this result is both dataset and

SDK-dependent. For BATH and ICE images the KIND 3 false rejection rates are usually much higher that for KIND 7.

But, for OPS images, SDKs D1, D2, E1, E2 and F1 gives KIND 3 error rates are actually lower than those for KIND 7.

See section 8.6.3 for further analysis.

. The KIND 16 error rates are substantially larger than those for KIND 7 with between a factor of two and six increase

in error rate. This is true even for the I1 and I2 implementations from the provider that proposed the unsegmented

polar format.

WITH COMPRESSION OF BOTH THE ENROLLMENT AND VERIFICATION RECORDS

The real strength of the KIND 7 record becomes apparent when both of the images involved in a comparison are severely

compressed. Row 12 in Tables 9, 10 and 11 gives FNMR at FMR = 0.001 when JPEG2000 compression is applied to make

KIND 7 records of size 2000 bytes. For OPS images, the performance of SDKs A1, A2, B1 and B2 is actually better than

uncompressed KIND 1 instances. In addition, SDKs D1, D2, I1, I2, J1 and J2 are only slightly degraded. The situation for

comparison of compressed KIND 3 pairs is usually considerably worse. Notable exceptions for OPS are D1, D2, E1, and

E2 which tightly crop their KIND 3 and thereby elevate the bits-per-pixel. For ICE and BATH images these SDKs revert to

KIND 7 substantially outperforming KIND 3.

Most SDKs perform worse with compression of both images in a comparison rather than with compression of only one.

However, the B1 and I1 SDKs often prefer compression of both images (see rows 5 and 8 in the cFNMR entries of Tables 9,

10 and 11. This may be a result of competing effects: The compression degrades error rates but this is offset by reduced

errors from using the centered formats vs. the parent KIND 1 . This begs the question: do the lowest overall error rates

come from comparison of uncompresssed KIND 3 instances?

8.3. CONFORMANCE TESTING

Interoperability tests have been formally standardized by ISO/IEC 19795-4:2008 Performance Interoperability Testing which

establishes procedures and reporting requirements for cross-provider tests and certification schemes. One of those requirements is that all samples should be conformant to the underlying standard. Conformance of a biometric component

(in this case an IREX record generator) is most effectively assessed by executing defined test assertions on samples produced by that component. Such test assertions are formally prescribed by a conformance testing standard. For iris

imagery that standard is ISO/IEC 29109-6 which checks instances of the ISO/IEC 19794-6 standard for conformance42.

In any case, the conformance test assertions can be categorized into those addressing the syntax of the data (are the

record values correct, and internally consistent?) and those governing the semantic content. This latter category would

test, for example, that the KIND 3 iris really is centered, that the KIND 7 masks are indeed over the eyelids and sclera, and

that the KIND 16 coordinate system is correct.

In the IREX trial itself, NIST checked all records for syntactic conformance. While the list of test assertions has not been

formally documented, the “C” source code of the validating parser is available. Note that the binary structure of the

revised ISO/IEC 19794-6 record has deviated (syntactically) from that finally adopted for the IREX trials.

42Unfortunately work in the SC 37 committee has thus far targeted the 2005 iris interchange standard and not the revision (which is now being

completed) that IREX supports. A NIST comment to remedy this anachronism was submitted to the United States working group, M1, in April 2009

and approved on a 8-1-1 vote, but was not sent on to SC 37 due to a voting technicality - see M1.3 meeting minutes M1/09-0242. The authors will

support actions to complete ISO/IEC 19794-6:201X and its conformance test as quickly as possible.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

69
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 Figure 25: The DET characteristics for all IREX algorithms on the OPS dataset for comparison of 2000 byte JPEG2000 compressed KIND 7 images with raw KIND 1 images. Each SDK failed to produce templates from a fraction, 0 ≤ FTE

≤ 1, of the images and the effects of that are included in this plot.

The FTE rates appear in Table 6. For the OPS dataset this fraction was often zero (see Table 6).

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

70
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 Figure 26: The DET characteristics for all IREX algorithms on the OPS dataset for comparison of 2000 byte JPEG2000 compressed KIND 16 images with raw KIND 1 images. Each SDK failed to produce templates from a fraction, 0 ≤ FTE

≤ 1, of the images and the effects of that are included in this plot.

The FTE rates appear in Table 6. For the OPS dataset this fraction was often zero (see Table 6).

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Figure 27: The figure shows the result of preparing the KIND 7 record with (left) and without (right) the mask boundary blurring

operation. The images have been compressed to 2000 bytes using the JPEG2000 compressor and then reconstructed. The scan lines

below each image reveal more detail of the iris texture is retained in the blurred edge image. This effect was part of the original

Cambridge proposal for KIND 7 which found that blurring preserves the coding budget.

8.4. VARIATION OF IMAGE RECORDS BETWEEN SDKS

In preparing the cropped KIND 3, cropped-and-masked KIND 7, and unsegmented polar KIND 16 instances, the SDKs use

a diversity of algorithms to localize the iris and eyelids. In doing so, they introduce some variation in the output image43

as described here:

. Crop-only KIND 3: Some SDKs crop more aggressively. Particularly the D1, D2, E1 and E2 SDKs leave a smaller

margin above and below the iris than the other SDKs . This is discussed extensively in section 8.5 which quantifies

the effect on matching performance.

. Crop+mask KIND 7: As with KIND 3 there is also variation in the the amount of cropping used in the preparation

of the KIND 7 instances. Also the aggressiveness with which eye lashes are masked (whether or not they’re over the

iris texture), and whether the lower eyelid was confused as being sclera and masked as such. A further variation is

whether the SDK actually implemented the IREX test plan recommendation44 to blur the masked edges. Figure 27

shows images with and without this blurring operation. The blur is intended to spare the coding budget.

. Unsegmented polar KIND 16: The dominant source of variation across SDKs is the choice of radial and circumferential sampling rates. There’s considerable disparity here between implementations: A1, A2, G1, G2, J1 and J2 all

use 64 radial and 256 circumferential samples while I1 and I2 use roughly double (but variable) sampling rates. H1

and H2 were unique in using much larger circumferential rates. In addition, the degree to which the inner circle is

smaller than the pupillary boundary, and the outer circle is beyond the limbic boundary, adds some variation.

43Whether the resulting image conforms to the text of the formal standard would be the subject of so-called Level 3 conformance test assertions.

In its development of ISO/IEC 29109 - Conformance tests for biometric data interchange records, SC 37 Working Group 3 established three levels of

conformance tests: Level 1 for syntax, Level 2 for internal consistency, and Level 3 for semantic image or signal properties (e.g., a frontal face is truly

frontal).

44IREX followed the L1 suggestion to convolve with a binomial kernel u = (1, 6, 15, 20, 15, 6, 1)T . This gave an explicit value for the mask referred

to in the original Cambridge definition of the KIND 7 format[19, 18].

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

72
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

xmargin

ymargin

0.1

0.2

0.3

0.4

0.5

0.4 0.5 0.6

C = 15 C = 20

0.4 0.5 0.6

C = 25

C = 30 C = 35

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FNMR

(a) FNMR A1

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(d) FNMR D1

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FNMR

(e) FNMR E1

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(f) FNMR F1

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(g) FNMR G1

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(h) FNMR H1

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2000

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Size (Bytes)

(l) Image size (bytes)

Figure 28: For the entire partition of the OPS dataset the plots show the dependence of cFNMR on the vertical and horizontal iris

cropping margins for various compression ratios. This applies only for KIND 3 records. The use of conditional FNMR excludes

comparisons that were falsely rejected even before any compression was applied. All computations are driven by the bounding box

coordinates reported by the I1 SDK. The native KIND 3 instances are not used in this analysis. The number of bits per pixel is 8/C,

where C is the compression ratio. The iris radius varies and, because the cropping margins are fixed multiples thereof, the image size

varies. The last plot shows the compressed size, in bytes. This equals the width times height divided by C. The SDK-specific annexes

include these figures in higher resolution alongside analogous display of the RMS change in genuine score.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

73
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

8.5. THE EFFECT OF CROPPING

Generation of the centered iris of the compact KIND 3 record requires cropping of the parent KIND 1 image in both the

horizontal and vertical directions. The resulting iris is centered in a raster. It should have sufficient margin around it to

allow iris detection algorithms to successfully fit the eyelid during segmentation. After consultation with prospective

participants, the IREX test plan recommended that the crop operation should leave a margin (∆x, ∆y) around the iris

where

∆x = ∆y ≥ max(40, 0.6R) (16)

There was lack of consensus on this matter and the recommendation was not adopted by some IREX implementations.

Particularly implementations D1, D2, E1 and E2 elected to crop more aggressively in the vertical direction. This approach

has the advantage of removing eye lashes.

This issue is important because, under compression, a smaller (more tightly cropped) image can be compressed at lower

bit rates to achieve a size objective. Particularly, for given a compression ratio C, an iris of radius R will be compressed

to a size S bytes where

S =

4(R + ∆x)(R + ∆y)

C

(17)

and the number of bits per pixel B is

B =

8

C

(18)

Thus for the KIND 3 crop-only formats the recognition performance will be a function of three independent variables:

the vertical and horizontal cropping margins and the compression ratio. A user of the KIND 3 format can specify these

and accept a variable size S:

S =

4γxγyR

C

(19)

where the margins are now expressed as a multiple of the image radius i.e. ∆x = γxR and similarly for y.

Figure 28 gives the results of a survey over these parameters for the various SDKs. Each plot shows the conditional false

non-match rate, cFNMR, for the entire OPS dataset as a heat map. It is plotted as a function of ∆x∆y at compression ratios

of C ∈ {15, 20, 25, 30, 35, 40, 45, 50} corresponding to bit rates B ∈ {0.53, 0.4, 0.32, 0.27, 0.23, 0.2, 0.18, 0.16} respectively.

The horizontal cropping margin takes on two values, 0.4R and 0.6R. The vertical margin varies from 0.1R to 0.5 in steps

of 0.1R. The values currently adopted (July 2009) for the ISO/IEC 19794-6 are ∆x = 0.6R and ∆y = 0.2R so that image

width and height are w = 2(R + ∆x) and h = 2(R + ∆y) respectively.

The notable results from the figures are as follows.

. Horizontal margin: Restriction to a horizontal margin of 0.4R is injurious in almost all cases. For the F1 and H1

SDK this is true even when compression is light (small C). For other SDKs the use of a narrow image only becomes

problematic at high compression ratios. While SDK A1 is most tolerant of tighter horizontal cropping, enough false

reject errors are observed at ∆x = 0.4R that the standard’s requirement to use ∆x = 0.6R should be maintained.

. Eye lash effects: In the vertical direction the use of larger margins is injurious to FNMR. This would be a counterintuitive result because retention of more of the image should allow better performance. Note that within any

10-element panel, the number of bits per pixel is fixed because it is solely dependent on C. The effect is explained

by realizing that retention of the eye lashes in the image requires the JPEG 2000 compressor to dedicate more of its

coding budget to the worthless high frequency information associated with eye lashes. The effect is particularly

evident here because eye lashes in the OPS dataset are quite prominent; this is a property of the subject population.

. Vertical margin: This issue of how small the vertical margin can be is more straightforward. For all SDKs except

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

74
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

H1 and F1 the accuracy is best at a margin of 1.1 and virtually identical to that at 1.2. However, given the severity

of the problem for some algorithms, and the possible difficulty for arbitrary providers to detect the iris boundary

to within 10% of its radius with such a tight crop, the newly (July 2009) instituted ISO standard specification of

∆y = 0.2R should be maintained.

. Algorithmic resistance to compression: The matching algorithms exhibit different resistance to the iris texturedamaging effects of compression. For any given margin pair (∆x, ∆y), SDKs A1 and D1 give least growth in

the error rate. Note that the quantity plotted is the conditional FNMR defined by equation 15. This measure is

appropriate to represent compression of enrollment quality instances.

. Attainable file sizes: A more important result is that, for seven out of ten providers, compression at 25:1 (i.e. bits

per pixel = 0.32) gives essentially undetectable increase in FNMR and this affords mean compressed raster sizes of

fewer than 5 kilobytes45. This supports one of the main IREX conclusions, that cropping and JPEG 2000 compression

to about 6 kilobytes is virtually error free.

8.6. INTEROPERABILITY OF THE STANDARD FORMATS

The power of formally standardizing iris image interchange records is that it supports a marketplace for products that

either generate standard records or consume (match) them. Thus, for example, a product from provider X can be used

to produce enrollment records which are then stored or transmitted and subsequently used in an identification search

executed by provider Y. This cross-provider aspect requires interoperability and this necessitates that all marketplace

providers have a uniform understanding and implementation of the relevant standard. NIST’s status as an independent

organization is well suited to cross-provider interoperability testing because single providers often do not have access

to SDKs and biometric samples prepared by competing providers. In executing tests like IREX and MINEX[25]46, NIST is

essentially a technical broker.

The IREX cross-provider interoperability tests have quadratic complexity reflecting all combinations of enroller X and

matcher Y47. Interoperability is stated as the FNMR rate at a FMR = 0.0001. The threshold was set for each interoperable

combination (X,Y) and this necessitated computation of the impostor distribution. This in turn necessitated use of the

smaller (but more difficult) ALL-FAILURES partition of the OPS dataset. The result is nevertheless a computationally

expensive operation involving nearly 8 billion comparisons48 for each KIND.

8.6.1. TEST METHOD

The enrollment source images were the 1335 KIND 1 images that constitute the enrollments image of the ALL-FAILURES

partition of the OPS dataset (see section 5.1.1). Each SDK was used to convert these to enrollment IREX records in each of

the other IREX formats. While six SDKs did not support generation of KIND 16 records (see Table 4), they were required

to generate templates and match them.

All 16320 verification images of 8160 subjects in the OPS dataset were converted by NIST into KIND 1 records - this

operation is trivial and does not require localization.

JPEG2000 compression was applied in two separate ways. First, each enrollment IREX record was compressed to a target

size of 3000 bytes. This differs from the 2000 bytes used elsewhere in this report. Second, the compressor was invoked

45For cropping margins of ∆x = 0.6R and ∆y = 0.6R the statistics on the number of bytes are: min = 2772, 25-th percentile = 4137, mean = 4501,

median = 4574, 75-th percentile = 4937 and maximum = 7691.

46MINEX supports fingerprint minutia interoperability - see http://fingerprint.nist.gov/minex

47NIST did not consider the cubic case in which an enrollment record prepared by X and a verification sample prepared by Y are compared by

provider Z.

48The number is 19 x 19 x 1335 x 16320 comparisons.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

75
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

to produce a compression ratio of 40, corresponding to 0.2 bits per pixel. This mode produces a file size that depends on

the initial size of IREX record’s image.

Each SDK was used to convert each enrollment IREX record into a proprietary template. The SDK was applied to convert

each verification IREX record into a proprietary template. The SDK was used to match all pairs of templates to produce N = 1335 x 16320 = 21,787,200 scores. This produces 1335 genuine scores and 21,784,530 impostor scores for each

interoperating combination of enroler (X) and matcher (Y).

The comparison scores from each enroler-matcher pair are subject to the usual analyses, equations 5 and 3. The value

τ that gives FMR (τ ) is computed from the empirical distribution function of the impostor distribution and FNMR (τ ) is

computed from the genuine distribution.

8.6.2. INTEROPERABILITY RESULTS

The results are tabulated in six tables, three for FNMR (Tables 12, 13 and 14) and three for FMR (Tables 15, 16 and 17), with

each group of three referring to the three KINDS. The FNMR tables also include the proportion of enrollment images that

could not be converted to templates by the matching SDK - this is the FTE rate. While the proportion of the uncompressed

KIND 1 verification images that could not be converted to templates is not reported explicitly, the effect is included in the

reported FNMR value. The notable observations are:

. The interoperability matrices show two performance aspects: IREX record production and IREX record consumption

(matching). Thus, across all tables, the best overall producer (A1) is not the best matcher (I2). This is expected

because the algorithmic functions needed to instantiate records are distinct from those needed to extract features

and match.

. ANOVA analyses show the matcher to be more influential on accuracy measures than the record generator. This,

however, is most true for KIND 3 but less so for KIND 7 and hardly at all for KIND 16. This suggests that the fine

segmentation part of the template creation process is harder in KIND 3 images than in the others.

. From the “wins” column of Tables 12 and 13, the KIND 3 and KIND 7 instances from B1 and A1 tend to be preferred

by any given matcher, i.e. to give the lowest error rates. That said the A2, B2, F1, I2, I2, and J2 generators also give

mean FNMR values close to B1 and A1 (i.e FNMR < 0.08).

. From the “wins” row of Tables 12, 13 and 14 the I1 and I2 matchers are the most capable of matching IREX records

regardless of their source. The false non-match rates are less than half of other matchers. The presence of green

shading in the respective columns indicates these SDKs give lower error rates than the producers of the enrollment

IREX records. A1, B1 and J2 also give lower mean error rates (FNMR < 0.07).

. Some matchers prefer other producers’ IREX records over their own. This is indicated by red text in a column. For

example, with KIND 3 records, the E2 matcher gives FNMR of 0.069 with A1 enrollments, versus 0.083 with its own.

One possible explanation is better localization.

. The interoperability matrices of Tables 15, 16 and 17 show FMR when the threshold is fixed to the uncompressed

KIND 1 default. The low variance observed in most columns shows that FMR has little dependence on which SDK

prepared the IREX record. This is a very attractive property. Some SDKs for KIND 16 polar records do exhibit some

dependency on source (e.g., row G2, column E1 in Table 17).

. The B1 and B2 implementations always produce low FMR under compression. This applies for all KINDS. A better

metric would be the deviation from the nominal value. The “win” row and column refer to the worst case change

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

76
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

in FMR from its nominal value of 0.0001. In this respect C1 excels for KIND 3 and KIND 16, while I2 is best for KIND

7.

8.6.3. COMPARING KINDS 3 AND 7

Comparing the interoperability matrices of Tables 12 and 13 there is little difference in the observed error rates. KIND 7 is

expected to give fewer false rejection errors because the mask is highly compressible, and this preserves the iris texture

for a given compression level. However, KIND 7 is often less accurate than KIND 3. Table 18 gives the simple difference

of the FNMR (k3) - FNMR (k7) for each element of the interoperability matrix. This is done for two cases: enrollment

images compressed to 3000 bytes, and enrollment images compressed to 0.2 bits per pixel (i.e. 40:1 compression ratio).

The notable results are as follows:

. Cells are shaded green when the KIND 7 gives lower FNMR than KIND 3. The relative absence of green shading

indicates that KIND 7 is often giving inferior false non-match rates than KIND 3. KIND 3 is superior at the lighter

compression level (the top table, bits per pixel = 0.02) but its advantage is partially eroded with more compression

(size = 3000 bytes).

. The magnitude of the difference is such that KIND 3 is often much better than KIND 7, and that KIND 7 is only ever

marginally better than KIND 3. Again KIND 7 improves under stronger compression.

. Looking at the rows of the matrices in Table 18, some SDKs (G1, G2, D1, D2, E1, E2) make KIND 3 records that are

almost always preferred by the matchers. Similarly, looking at the columns (C1, D1, D2, E1, and at, bpp = 0.2, B1,

B2, H1, H2, I1, I2) the matchers prefer KIND 3 to KIND 7 .

These statements are consistent with two competing effects: The immunity of KIND 7 to severe compression is offset by

errors in the eyelid detection operation which do not affect KIND 3. Masking errors result if the eyelid-iris and sclera-iris

boundaries are difficult to find. These would cause elevated FNMR. Thus, at some compression level, where too much

damage is done to the texture of a KIND 3 raster, KIND 7 accuracy begins to exceed that of KIND 3. Some implementations

are better than others, so the cross-over point is variable. While a further, dedicated, experiment is needed to solidify

this conclusion, it does weigh against KIND 7 in the which-format-to-use guidance given in section 10.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

77
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 KIND 3 INTEROPERABLE FALSE NON-MATCH RATES Row Num Size A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2 Ave Win [5, 95]

%

A1 0.053 0.000 0.219 0.000 0.054 0.000 0.130 0.000 0.379 0.010

-

- 0.235 0.004 0.148 0.005 0.075 0.013 0.069 0.013 0.410 0.008 0.099 0.005 0.104 0.011 0.405 0.010 0.390 0.010 0.022 0.000 0.021 0.000 0.133 0.000 0.055 0.006 0.076 0.002

6 2673 - 3667

A2 0.053 0.000 0.219 0.000 0.054 0.000 0.130 0.000 0.379 0.010

-

- 0.235 0.004 0.148 0.005 0.075 0.013 0.069 0.013 0.410 0.008 0.099 0.005 0.104 0.011 0.405 0.010 0.390 0.010 0.022 0.000 0.021 0.000 0.133 0.000 0.055 0.006 0.076 0.002

0 2673 - 3667

B1 0.058 0.000 0.223 0.000 0.055 0.000 0.132 0.000 0.375 0.004 0.400 0.004 0.229 0.004 0.141 0.004 0.078 0.015 0.072 0.015 0.412 0.006 0.097 0.007 0.104 0.012 0.402 0.006 0.386 0.006 0.023 0.000 0.022 0.000 0.136 0.004 0.054 0.004 0.077 0.002

8 2788 - 3920

B2 0.058 0.000 0.223 0.000 0.055 0.000 0.132 0.000 0.375 0.004 0.400 0.004 0.229 0.004 0.141 0.004 0.078 0.015 0.072 0.015 0.412 0.006 0.097 0.007 0.104 0.012 0.402 0.006 0.386 0.006 0.023 0.000 0.022 0.000 0.136 0.004 0.054 0.004 0.077 0.002

0 2788 - 3920

C1 0.075 0.008 0.240 0.008 0.064 0.002 0.141 0.002 0.400 0.041 0.426 0.044 0.251 0.013 0.159 0.015 0.095 0.022 0.089 0.022 0.422 0.019 0.113 0.025 0.120 0.026 0.434 0.013 0.413 0.013 0.034 0.004 0.035 0.004 0.145 0.011 0.062 0.011 0.092 0.009

0

C2 0.088 0.019 0.253 0.019 0.073 0.012 0.150 0.012 0.424 0.055 0.443 0.057 0.260 0.025 0.171 0.025 0.100 0.034 0.094 0.034 0.438 0.033 0.119 0.031 0.127 0.036 0.451 0.025 0.432 0.025 0.040 0.013 0.038 0.013 0.156 0.020 0.073 0.020 0.100 0.020

0 2755 - 3920

D1 0.115 0.000 0.280 0.000 0.062 0.000 0.143 0.000 0.381 0.004 0.417 0.007 0.265 0.004 0.178 0.006 0.120 0.014 0.111 0.014 0.545 0.128 0.108 0.011 0.109 0.018 0.483 0.001 0.480 0.001 0.027 0.000 0.080 0.000 0.136 0.002 0.055 0.002 0.103 0.001

0 2029 - 2983

D2 0.115 0.000 0.280 0.000 0.062 0.000 0.142 0.000 0.381 0.004 0.417 0.007 0.265 0.004 0.178 0.006 0.120 0.014 0.111 0.014 0.545 0.128 0.108 0.011 0.109 0.018 0.483 0.001 0.480 0.001 0.027 0.000 0.080 0.000 0.136 0.002 0.055 0.002 0.103 0.001

0 2029 - 2983

E1 0.075 0.004 0.239 0.004 0.058 0.004 0.136 0.004 0.375 0.010 0.425 0.019 0.235 0.005 0.150 0.006 0.091 0.013 0.083 0.013 0.502 0.094 0.105 0.010 0.109 0.013 0.409 0.006 0.392 0.006 0.025 0.004 0.040 0.004 0.137 0.006 0.055 0.006 0.086 0.005

0 2029 - 2983

E2 0.075 0.004 0.239 0.004 0.058 0.004 0.136 0.004 0.375 0.010 0.425 0.019 0.235 0.005 0.150 0.006 0.091 0.013 0.083 0.013 0.502 0.094 0.105 0.010 0.109 0.013 0.409 0.006 0.392 0.006 0.025 0.004 0.040 0.004 0.137 0.006 0.055 0.006 0.086 0.005

0 2029 - 2983

F1 0.061 0.007 0.226 0.007 0.055 0.002 0.131 0.002 0.401 0.043 0.428 0.040 0.238 0.012 0.148 0.012 0.078 0.019 0.073 0.019 0.404 0.004 0.105 0.013 0.106 0.019 0.414 0.016 0.394 0.016 0.024 0.002 0.025 0.002 0.135 0.007 0.055 0.007 0.079 0.006

1 2822 - 3920

G1 0.066 0.004 0.231 0.004 0.062 0.004 0.142 0.004 0.390 0.017 0.413 0.020 0.239 0.008 0.151 0.008 0.084 0.021 0.076 0.021 0.420 0.018 0.107 0.008 0.107 0.017 0.425 0.010 0.409 0.010 0.031 0.004 0.031 0.004 0.139 0.007 0.058 0.007 0.085 0.006

0 2624 - 3610

G2 0.064 0.008 0.228 0.008 0.061 0.008 0.138 0.008 0.383 0.022 0.409 0.021 0.246 0.011 0.155 0.012 0.080 0.019 0.076 0.019 0.420 0.018 0.109 0.014 0.106 0.013 0.437 0.014 0.417 0.014 0.032 0.008 0.031 0.008 0.138 0.011 0.058 0.011 0.084 0.011

0 2656 - 3648

H1 0.163 0.021 0.324 0.021 0.146 0.021 0.225 0.021 0.453 0.065 0.488 0.064 0.336 0.037 0.231 0.051 0.176 0.073 0.170 0.073 0.518 0.103 0.196 0.082 0.193 0.082 1.000 0.021 0.712 0.021 0.112 0.024 0.118 0.022 0.898 0.192 0.140 0.042 0.182 0.028

0 2958 - 4243

H2 0.163 0.021 0.324 0.021 0.146 0.021 0.225 0.021 0.453 0.065 0.488 0.064 0.336 0.037 0.231 0.051 0.176 0.073 0.170 0.073 0.518 0.103 0.196 0.082 0.193 0.082 1.000 0.021 0.712 0.021 0.112 0.024 0.118 0.022 0.898 0.192 0.140 0.042 0.182 0.028

0 2958 - 4243

I1 0.058 0.006 0.220 0.006 0.053 0.000 0.131 0.000 0.396 0.040 0.426 0.039 0.235 0.010 0.145 0.010 0.077 0.017 0.072 0.017 0.407 0.006 0.104 0.014 0.108 0.016 0.413 0.012 0.393 0.012 0.022 0.000 0.022 0.000 0.132 0.000 0.055 0.006 0.078 0.004

2 2890 - 4000

I2 0.059 0.006 0.221 0.006 0.055 0.000 0.133 0.000 0.397 0.040 0.426 0.038 0.236 0.010 0.147 0.010 0.076 0.016 0.071 0.016 0.404 0.004 0.105 0.015 0.107 0.016 0.414 0.012 0.394 0.012 0.022 0.000 0.021 0.000 0.133 0.000 0.055 0.006 0.078 0.004

1 2890 - 4000

J1 0.058 0.007 0.221 0.007 0.060 0.007 0.134 0.007 0.409 0.041

-

- 0.236 0.012 0.145 0.013 0.079 0.021 0.073 0.021 0.414 0.012 0.115 0.019 0.109 0.021 0.954 0.062 0.948 0.062 0.028 0.007 0.028 0.007 0.134 0.007 0.055 0.007 0.082 0.009

0 2805 - 3980

J2 0.056 0.004 0.219 0.004 0.057 0.004 0.132 0.004 0.396 0.035

-

- 0.234 0.009 0.143 0.010 0.078 0.019 0.071 0.019 0.413 0.009 0.102 0.013 0.103 0.016 0.413 0.014 0.393 0.014 0.025 0.004 0.025 0.004 0.133 0.004 0.055 0.007 0.078 0.006

1 2805 - 3980

Ave 0.064 0.247 0.061 0.147 0.421 0.462 0.260 0.162 0.086 0.080 0.456 0.113 0.117 0.454 0.434 0.026 0.026 0.149 0.061Wins 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 10 0 0

Table 12: Cross-provider FNMR interoperability measured over the ALL-FAILURES partition of the OPS database (see section 5.1.1). All KIND 3 records were compressed to 0.2 bits per

pixel (CR = 40) using JPEG 2000. The row label identifies the producer of the IREX enrollment KIND 3 record. The column label identifies the SDK that compares (proprietary templates

of) the KIND 3 enrollment record against a KIND 1 verification record. Error rates: In each cell two numbers appear: At top is FNMR at the threshold that gives FMR = 0.0001 for that

combination of algorithm. Below is FTE covering failures of the column-identified SDK to make an enrollment template from the row-identified KIND 3 instance and of the row-identified

SDK to make an IREX record. The FTE is included in the FNMR . Wins: The next to last column gives the number of columns in which the row-identified KIND 3 generator gives the best

accuracy. The last row gives the number of rows for which the matcher gives the best accuracy. Colors: The on-diagonal within-provider elements are colored in yellow. Cells with red text

indicate better accuracy than the matcher’s native performance. Cells filled green indicate better accuracy than when the producer of the enrollment record executes the match. All meansare taken over the best ten elements - as a robustness measure to remove poor performances.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

78
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 KIND 7 INTEROPERABLE FALSE NON-MATCH RATES Row Num Size A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2 Ave Win [5, 95]

%

A1 0.052 0.000 0.216 0.000 0.054 0.000 0.133 0.000 0.379 0.006 0.390 0.007 0.239 0.004 0.148 0.004 0.079 0.011 0.067 0.011 0.403 0.008 0.095 0.009 0.097 0.015 0.420 0.008 0.397 0.008 0.023 0.000 0.023 0.000 0.130 0.000 0.058 0.004 0.076 0.001

8 2673 - 3667

A2 0.052 0.000 0.216 0.000 0.054 0.000 0.133 0.000 0.379 0.006 0.390 0.007 0.239 0.004 0.148 0.004 0.079 0.011 0.067 0.011 0.403 0.008 0.095 0.009 0.097 0.015 0.420 0.008 0.397 0.008 0.023 0.000 0.023 0.000 0.130 0.000 0.058 0.004 0.076 0.001

0 2673 - 3667

B1 0.055 0.000 0.221 0.000 0.055 0.000 0.131 0.000 0.374 0.003 0.393 0.003 0.234 0.004 0.142 0.005 0.079 0.015 0.072 0.015 0.399 0.006 0.097 0.010 0.094 0.012 0.414 0.006 0.401 0.006 0.025 0.000 0.024 0.000 0.133 0.002 0.052 0.002 0.076 0.001

6 2788 - 3920

B2 0.055 0.000 0.221 0.000 0.055 0.000 0.131 0.000 0.374 0.003 0.393 0.003 0.234 0.004 0.142 0.005 0.079 0.015 0.072 0.015 0.399 0.006 0.097 0.010 0.094 0.012 0.414 0.006 0.401 0.006 0.025 0.000 0.024 0.000 0.133 0.002 0.052 0.002 0.076 0.001

0 2788 - 3920

C1 0.082 0.008 0.252 0.008 0.071 0.002 0.153 0.002 0.390 0.019 0.411 0.019 0.263 0.011 0.176 0.013 0.100 0.026 0.093 0.026 0.428 0.010 0.109 0.019 0.109 0.020 0.443 0.007 0.427 0.007 0.041 0.004 0.041 0.003 0.148 0.017 0.069 0.017 0.096 0.007

0

C2 0.097 0.019 0.268 0.019 0.081 0.012 0.163 0.012 0.408 0.028 0.425 0.028 0.276 0.022 0.191 0.022 0.115 0.035 0.102 0.035 0.448 0.022 0.128 0.033 0.123 0.032 0.494 0.019 0.460 0.019 0.058 0.013 0.059 0.013 0.161 0.029 0.084 0.029 0.112 0.019

0 2755 - 3920

D1 0.145 0.043 0.299 0.043 0.079 0.000 0.195 0.000 0.400 0.007 0.419 0.007 0.270 0.004 0.182 0.010 0.138 0.014 0.127 0.014 0.467 0.025 0.142 0.034 0.135 0.044 0.515 0.000 0.510 0.000 0.072 0.001 0.090 0.005 0.156 0.011 0.073 0.011 0.128 0.004

0 2029 - 2983

D2 0.145 0.043 0.299 0.043 0.079 0.000 0.196 0.000 0.400 0.007 0.419 0.007 0.270 0.004 0.182 0.010 0.138 0.014 0.127 0.014 0.467 0.025 0.142 0.034 0.135 0.044 0.515 0.000 0.510 0.000 0.072 0.001 0.090 0.005 0.156 0.011 0.073 0.011 0.128 0.004

0 2029 - 2983

E1 0.122 0.000 0.285 0.000 0.075 0.000 0.193 0.000 0.404 0.006 0.416 0.006 0.267 0.002 0.182 0.005 0.137 0.011 0.126 0.011 0.470 0.029 0.150 0.040 0.140 0.050 0.455 0.000 0.440 0.000 0.073 0.001 0.091 0.001 0.147 0.006 0.066 0.006 0.125 0.001

0 2029 - 2983

E2 0.122 0.000 0.285 0.000 0.076 0.000 0.195 0.000 0.404 0.006 0.416 0.006 0.267 0.002 0.182 0.005 0.137 0.011 0.126 0.011 0.470 0.029 0.150 0.040 0.140 0.050 0.455 0.000 0.440 0.000 0.073 0.001 0.091 0.001 0.147 0.006 0.066 0.006 0.125 0.001

0 2029 - 2983

F1 0.065 0.007 0.233 0.007 0.061 0.002 0.147 0.002 0.426 0.045 0.433 0.045 0.258 0.010 0.170 0.011 0.086 0.020 0.078 0.020 0.414 0.004 0.110 0.016 0.102 0.019 0.425 0.010 0.416 0.010 0.030 0.003 0.028 0.004 0.144 0.007 0.060 0.007 0.085 0.006

0 2822 - 3920

G1 0.077 0.011 0.249 0.011 0.074 0.011 0.152 0.011 0.408 0.028 0.426 0.028 0.266 0.016 0.172 0.018 0.096 0.025 0.088 0.025 0.432 0.017 0.118 0.017 0.107 0.023 0.495 0.014 0.482 0.014 0.045 0.011 0.042 0.011 0.144 0.019 0.067 0.019 0.095 0.014

0 2624 - 3610

G2 0.079 0.017 0.250 0.017 0.073 0.017 0.149 0.017 0.407 0.030 0.427 0.030 0.264 0.020 0.175 0.021 0.098 0.025 0.088 0.025 0.422 0.023 0.122 0.023 0.115 0.021 0.467 0.021 0.457 0.021 0.042 0.017 0.040 0.017 0.148 0.019 0.069 0.019 0.097 0.020

0 2656 - 3648

H1 0.184 0.022 0.345 0.022 0.177 0.022 0.276 0.022 0.506 0.085 0.520 0.084 0.379 0.030 0.265 0.043 0.210 0.094 0.192 0.094 0.595 0.052 0.240 0.086 0.232 0.106 0.658 0.028 1.000 0.028 0.157 0.030 0.154 0.023 0.941 0.192 0.170 0.067 0.220 0.030

0 2958 - 4243

H2 0.184 0.022 0.345 0.022 0.177 0.022 0.276 0.022 0.506 0.085 0.520 0.084 0.379 0.030 0.265 0.043 0.210 0.094 0.192 0.094 0.595 0.052 0.240 0.086 0.232 0.106 0.658 0.028 1.000 0.028 0.157 0.030 0.154 0.023 0.941 0.192 0.170 0.067 0.220 0.030

0 2958 - 4243

I1 0.058 0.006 0.228 0.006 0.058 0.000 0.139 0.000 0.404 0.042 0.422 0.042 0.255 0.010 0.165 0.013 0.090 0.021 0.082 0.021 0.404 0.002 0.100 0.013 0.085 0.015 0.407 0.009 0.396 0.009 0.023 0.000 0.022 0.000 0.133 0.000 0.053 0.005 0.078 0.003

2 2890 - 4000

I2 0.058 0.006 0.228 0.006 0.058 0.000 0.139 0.000 0.404 0.043 0.422 0.043 0.256 0.010 0.166 0.013 0.091 0.022 0.083 0.022 0.403 0.003 0.101 0.013 0.086 0.015 0.407 0.009 0.397 0.009 0.023 0.000 0.022 0.000 0.132 0.000 0.055 0.005 0.079 0.003

0 2890 - 4000

J1 0.060 0.007 0.228 0.007 0.058 0.007 0.136 0.007 0.380 0.012 0.402 0.011 0.237 0.012 0.148 0.013 0.079 0.021 0.073 0.021 0.406 0.012 0.115 0.017 0.109 0.022 0.968 0.060 0.951 0.060 0.030 0.007 0.028 0.007 0.135 0.007 0.056 0.007 0.083 0.009

0 2805 - 3980

J2 0.058 0.004 0.224 0.004 0.056 0.004 0.134 0.004 0.375 0.005 0.393 0.005 0.237 0.008 0.147 0.010 0.078 0.018 0.070 0.018 0.407 0.010 0.103 0.013 0.101 0.014 0.401 0.008 0.384 0.008 0.028 0.004 0.027 0.004 0.133 0.004 0.055 0.007 0.079 0.005

3 2805 - 3980

Ave 0.066 0.252 0.064 0.152 0.428 0.447 0.273 0.172 0.093 0.084 0.451 0.114 0.108 0.467 0.451 0.030 0.029 0.150 0.063Wins 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 11 0 2

Table 13: Cross-provider FNMR interoperability measured over the ALL-FAILURES partition of the OPS database (see section 5.1.1). All KIND 7 records were compressed to 0.2 bits per

pixel (CR = 40) using JPEG 2000. The row label identifies the producer of the IREX enrollment KIND 7 record. The column label identifies the SDK that compares (proprietary templates

of) the KIND 7 enrollment record against a KIND 1 verification record. Error rates: In each cell two numbers appear: At top is FNMR at the threshold that gives FMR = 0.0001 for that

combination of algorithm. Below is FTE covering failures of the column-identified SDK to make an enrollment template from the row-identified KIND 7 instance and of the row-identified

SDK to make an IREX record. The FTE is included in the FNMR . Wins: The next to last column gives the number of columns in which the row-identified KIND 7 generator gives the best

accuracy. The last row gives the number of rows for which the matcher gives the best accuracy. Colors: The on-diagonal within-provider elements are colored in yellow. Cells with red text

indicate better accuracy than the matcher’s native performance. Cells filled green indicate better accuracy than when the producer of the enrollment record executes the match. All meansare taken over the best ten elements - as a robustness measure to remove poor performances.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

79
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 KIND 16 INTEROPERABLE FALSE NON-MATCH RATES Row Num BPP A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2 Ave Win [5, 95]

%

A1 0.064 0.000 0.231 0.000 0.182 0.000 0.309 0.000 0.360 0.007 0.376 0.007 0.286 0.001 0.181 0.004 0.127 0.025 0.094 0.025 0.396 0.002 0.103 0.010 0.100 0.018 0.343 0.000 0.336 0.000 0.028 0.000 0.027 0.000 0.133 0.005 0.056 0.005 0.102 0.000 17 0.16 - 0.22A2 0.064 0.000 0.231 0.000 0.183 0.000 0.309 0.000 0.360 0.007 0.376 0.007 0.286 0.001 0.181 0.004 0.127 0.025 0.094 0.025 0.396 0.002 0.103 0.010 0.100 0.018 0.343 0.000 0.336 0.000 0.028 0.000 0.027 0.000 0.133 0.005 0.056 0.005 0.102 0.000

0 0.16 - 0.22

B1 0.144 0.000 0.354 0.000 0.244 0.000 0.355 0.000 0.845 0.231 0.847 0.231 0.476 0.001 0.351 0.003 0.316 0.029 0.224 0.029 0.710 0.013 0.270 0.018 0.265 0.013 0.709 0.000 0.716 0.000 0.042 0.000 0.040 0.000 0.249 0.002 0.164 0.002 0.218 0.000

0 0.15 - 0.22

B2 0.144 0.000 0.354 0.000 0.244 0.000 0.355 0.000 0.845 0.231 0.847 0.231 0.476 0.001 0.351 0.003 0.316 0.029 0.224 0.029 0.710 0.013 0.270 0.018 0.265 0.013 0.709 0.000 0.716 0.000 0.042 0.000 0.040 0.000 0.249 0.002 0.164 0.002 0.218 0.000

0 0.15 - 0.22

F1 0.110 0.002 0.277 0.002 0.333 0.002 0.467 0.002 0.484 0.022 0.487 0.022 0.453 0.004 0.279 0.007 0.285 0.075 0.198 0.075 0.407 0.004 0.188 0.025 0.192 0.033 0.420 0.002 0.418 0.002 0.031 0.002 0.030 0.002 0.167 0.008 0.081 0.008 0.172 0.003

0 0.15 - 0.21

G1 0.104 0.006 0.270 0.006 0.277 0.006 0.428 0.006 0.428 0.013 0.440 0.013 0.335 0.012 0.213 0.013 0.324 0.018 0.176 0.018 0.414 0.008 0.142 0.019 0.145 0.016 0.392 0.006 0.392 0.006 0.046 0.006 0.046 0.006 0.180 0.014 0.088 0.014 0.157 0.008

0 0.17 - 0.23

G2 0.106 0.010 0.272 0.010 0.292 0.010 0.429 0.010 0.439 0.016 0.450 0.016 0.369 0.014 0.231 0.016 0.538 0.019 0.425 0.019 0.422 0.010 0.157 0.025 0.154 0.024 0.399 0.010 0.402 0.010 0.044 0.010 0.043 0.010 0.183 0.012 0.094 0.012 0.175 0.011

0 0.16 - 0.23

H1 0.222 0.021 0.393 0.021 0.407 0.023 0.518 0.023 0.610 0.100 0.623 0.100 0.531 0.079 0.369 0.081 0.390 0.115 0.293 0.115 0.543 0.072 0.285 0.102 0.282 0.099 0.580 0.024 0.583 0.024 0.144 0.063 0.148 0.058 0.936 0.192 0.195 0.076 0.302 0.045

0 0.14 - 0.20

H2 0.222 0.021 0.393 0.021 0.407 0.023 0.518 0.023 0.610 0.100 0.623 0.100 0.531 0.079 0.369 0.081 0.390 0.115 0.293 0.115 0.543 0.072 0.285 0.102 0.282 0.099 0.580 0.024 0.583 0.024 0.144 0.063 0.148 0.058 0.936 0.192 0.195 0.076 0.302 0.045

0 0.14 - 0.20

I1 0.114 0.000 0.281 0.000 0.318 0.000 0.449 0.000 0.521 0.030 0.527 0.030 0.419 0.002 0.273 0.004 0.260 0.038 0.175 0.038 0.407 0.003 0.184 0.014 0.181 0.009 0.416 0.000 0.414 0.000 0.031 0.000 0.031 0.000 0.165 0.000 0.084 0.003 0.166 0.000

0 0.15 - 0.21

I2 0.115 0.000 0.280 0.000 0.319 0.000 0.451 0.000 0.523 0.031 0.528 0.031 0.418 0.002 0.276 0.004 0.263 0.038 0.175 0.038 0.408 0.002 0.183 0.015 0.183 0.009 0.418 0.000 0.414 0.000 0.032 0.000 0.031 0.000 0.167 0.000 0.086 0.003 0.168 0.000

0 0.15 - 0.21

J1 0.110 0.000 0.273 0.000 0.338 0.000 0.477 0.000 0.990 0.977 0.990 0.977 0.413 0.002 0.256 0.004 0.294 0.023 0.163 0.023 0.394 0.002 0.980 0.956 0.961 0.937 0.000 0.303 0.999 0.303 0.037 0.000 0.037 0.000 0.171 0.004 0.094 0.007 0.197 0.001

2 0.15 - 0.21

J2 0.113 0.000 0.271 0.000 0.339 0.000 0.479 0.000 0.450 0.006 0.455 0.006 0.434 0.004 0.255 0.005 0.254 0.028 0.154 0.028 0.395 0.004 0.172 0.014 0.169 0.015 0.405 0.000 0.414 0.000 0.038 0.000 0.037 0.000 0.167 0.001 0.091 0.004 0.161 0.001

0 0.15 - 0.21

Ave 0.116 0.305 0.303 0.448 0.532 0.543 0.432 0.277 0.285 0.186 0.465 0.197 0.195 0.477 0.477 0.039 0.038 0.191 0.099Wins 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 10 0 0

Table 14: Cross-provider FNMR interoperability measured over the ALL-FAILURES partition of the OPS database (see section 5.1.1). All KIND 16 records were compressed to a fixed target

of 3000 bytes using JPEG 2000. The row label identifies the producer of the IREX enrollment KIND 16 record. The column label identifies the SDK that compares (proprietary templates

of) the KIND 16 enrollment record against a KIND 1 verification record. Error rates: In each cell two numbers appear: At top is FNMR at the threshold that gives FMR = 0.0001 for thatcombination of algorithm. Below is FTE covering failures of the column-identified SDK to make an enrollment template from the row-identified KIND 16 instance and of the row-identified

SDK to make an IREX record. The FTE is included in the FNMR . Wins: The next to last column gives the number of columns in which the row-identified KIND 16 generator gives the best

accuracy. The last row gives the number of rows for which the matcher gives the best accuracy. Colors: The on-diagonal within-provider elements are colored in yellow. Cells with red text

indicate better accuracy than the matcher’s native performance. Cells filled green indicate better accuracy than when the producer of the enrollment record executes the match. All meansare taken over the best ten elements - as a robustness measure to remove poor performances.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

80
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 KIND 3 INTEROPERABLE FALSE MATCH RATES Row Num Size A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2 Worst Win [5, 95]

%

A1 0.00013 0.00013 0.00004 0.00001 0.00010 - 0.00008 0.00018 0.00012 0.00012 0.00006 0.00012 0.00012 0.00014 0.00019 0.00012 0.00012 0.00021 0.00015 0.00011 4 2673 - 3667A2 0.00013 0.00013 0.00004 0.00001 0.00010 - 0.00008 0.00018 0.00012 0.00012 0.00006 0.00012 0.00012 0.00014 0.00019 0.00012 0.00012 0.00021 0.00015 0.00011 0 2673 - 3667B1 0.00015 0.00015 0.00004 0.00002 0.00010 0.00010 0.00008 0.00018 0.00012 0.00012 0.00006 0.00012 0.00012 0.00016 0.00021 0.00012 0.00012 0.00016 0.00014 0.00011 1 2788 - 3920B2 0.00015 0.00015 0.00004 0.00002 0.00010 0.00010 0.00008 0.00018 0.00012 0.00012 0.00006 0.00012 0.00012 0.00016 0.00021 0.00012 0.00012 0.00016 0.00014 0.00011 0 2788 - 3920C1 0.00014 0.00015 0.00004 0.00002 0.00009 0.00009 0.00011 0.00018 0.00011 0.00012 0.00006 0.00012 0.00012 0.00016 0.00020 0.00013 0.00087 0.00016 0.00014 0.00077 1C2 0.00014 0.00015 0.00004 0.00002 0.00009 0.00009 0.00008 0.00017 0.00011 0.00011 0.00006 0.00011 0.00012 0.00023 0.00027 0.00013 0.00013 0.00016 0.00014 0.00017 2 2755 - 3920D1 0.00014 0.00014 0.00003 0.00001 0.00010 0.00010 0.00009 0.00021 0.00014 0.00013 0.00006 0.00012 0.00012 0.00049 0.00064 0.00013 0.00013 0.00015 0.00014 0.00054 1 2029 - 2983D2 0.00014 0.00014 0.00003 0.00001 0.00010 0.00010 0.00009 0.00021 0.00014 0.00013 0.00006 0.00012 0.00012 0.00049 0.00064 0.00013 0.00013 0.00015 0.00014 0.00054 0 2029 - 2983E1 0.00015 0.00015 0.00003 0.00001 0.00010 0.00010 0.00007 0.00019 0.00012 0.00012 0.00007 0.00012 0.00012 0.00023 0.00024 0.00013 0.00013 0.00016 0.00015 0.00014 0 2029 - 2983E2 0.00015 0.00015 0.00003 0.00001 0.00010 0.00010 0.00007 0.00019 0.00012 0.00012 0.00007 0.00012 0.00012 0.00023 0.00024 0.00013 0.00013 0.00016 0.00015 0.00014 0 2029 - 2983F1 0.00015 0.00015 0.00004 0.00001 0.00009 0.00009 0.00008 0.00017 0.00012 0.00012 0.00006 0.00012 0.00012 0.00016 0.00017 0.00012 0.00012 0.00016 0.00015 0.00007 1 2822 - 3920G1 0.00015 0.00015 0.00004 0.00001 0.00010 0.00010 0.00008 0.00018 0.00011 0.00012 0.00006 0.00012 0.00012 0.00014 0.00017 0.00012 0.00012 0.00017 0.00015 0.00008 1 2624 - 3610G2 0.00014 0.00015 0.00004 0.00001 0.00010 0.00009 0.00008 0.00018 0.00012 0.00012 0.00006 0.00012 0.00012 0.00015 0.00019 0.00012 0.00012 0.00016 0.00014 0.00009 0 2656 - 3648H1 0.00016 0.00015 0.00003 0.00003 0.00010 0.00010 0.00040 0.00017 0.00016 0.00016 0.00006 0.00010 0.00011 0.00175 0.00163 0.00014 0.00016 0.00000 0.00013 0.00165 4 2958 - 4243H2 0.00016 0.00015 0.00003 0.00004 0.00010 0.00010 0.00040 0.00017 0.00016 0.00016 0.00006 0.00010 0.00011 0.00175 0.00163 0.00014 0.00016 0.00000 0.00013 0.00165 1 2958 - 4243I1 0.00015 0.00015 0.00004 0.00001 0.00009 0.00009 0.00008 0.00018 0.00011 0.00012 0.00006 0.00012 0.00012 0.00018 0.00018 0.00013 0.00013 0.00012 0.00015 0.00008 1 2890 - 4000I2 0.00015 0.00015 0.00007 0.00003 0.00009 0.00009 0.00008 0.00018 0.00012 0.00012 0.00006 0.00012 0.00012 0.00017 0.00018 0.00013 0.00013 0.00022 0.00028 0.00018 1 2890 - 4000J1 0.00015 0.00015 0.00004 0.00001 0.00009 - 0.00008 0.00017 0.00012 0.00012 0.00006 0.00012 0.00012 0.00362 0.00354 0.00012 0.00012 0.00014 0.00015 0.00352 0 2805 - 3980J2 0.00015 0.00015 0.00004 0.00001 0.00009 - 0.00008 0.00017 0.00012 0.00012 0.00007 0.00012 0.00012 0.00018 0.00020 0.00012 0.00012 0.00014 0.00015 0.00010 1 2805 - 3980W-C 0.00006 0.00005 -0.00003 -0.00006 -0.00000 -0.00000 0.00030 0.00011 0.00006 0.00006 -0.00003 0.00002 0.00002 0.00352 0.00344 0.00004 0.00077 0.00012 0.00018Wins 0 0 0 0 13 6 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 15: Cross-provider FMR interoperability measured over the ALL-FAILURES partition of the OPS database (see section 5.1.1). All KIND 3 records were compressed to 0.2 bits per pixel

(CR = 40) using JPEG2000. The row label identifies the producer of the IREX KIND 3 enrollment record. The column label identifies the SDK that compares (proprietary templates of) the

KIND 3 enrollment record against a KIND 1 verification record. Error rate: In each cell is the FMR at the threshold that gives FMR = 0.0001 when matching uncompressed KIND 1 vs. KIND 1

instances. Colors: The on-diagonal within-provider elements are colored in yellow. Cells filled green indicate FMR better than 0.0001. Cells with red text indicate FMR is better than whenthe producer of the template executes the match. The second to last row and third to last column give worst case values of FMR - 0.0001. The “wins” are the number of times the generator(matcher) gives the smallest absolute change in FMR , i.e. |FMR - 0.0001|.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

81
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 KIND 7 INTEROPERABLE FALSE MATCH RATES Row Num Size A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2 Worst Win [5, 95]

%

A1 0.00015 0.00014 0.00004 0.00002 0.00014 0.00014 0.00005 0.00017 0.00014 0.00014 0.00003 0.00012 0.00012 0.00040 0.00034 0.00009 0.00009 0.00015 0.00009 0.00030 0 2673 - 3667A2 0.00015 0.00014 0.00004 0.00002 0.00014 0.00014 0.00005 0.00017 0.00014 0.00014 0.00003 0.00012 0.00012 0.00040 0.00034 0.00009 0.00009 0.00015 0.00009 0.00030 0 2673 - 3667B1 0.00016 0.00015 0.00004 0.00002 0.00013 0.00013 0.00006 0.00017 0.00013 0.00013 0.00003 0.00012 0.00012 0.00030 0.00035 0.00009 0.00010 0.00011 0.00010 0.00025 1 2788 - 3920B2 0.00016 0.00015 0.00004 0.00002 0.00013 0.00013 0.00006 0.00017 0.00013 0.00013 0.00003 0.00012 0.00012 0.00030 0.00035 0.00009 0.00010 0.00011 0.00010 0.00025 0 2788 - 3920C1 0.00015 0.00015 0.00003 0.00001 0.00013 0.00014 0.00006 0.00018 0.00012 0.00012 0.00006 0.00012 0.00012 0.00033 0.00037 0.00010 0.00011 0.00013 0.00011 0.00027 0C2 0.00015 0.00015 0.00003 0.00001 0.00013 0.00013 0.00006 0.00017 0.00012 0.00012 0.00006 0.00012 0.00012 0.00053 0.00051 0.00010 0.00010 0.00013 0.00011 0.00043 0 2755 - 3920D1 0.00015 0.00014 0.00002 0.00001 0.00013 0.00014 0.00010 0.00019 0.00010 0.00010 0.00005 0.00011 0.00012 0.00068 0.00084 0.00009 0.00010 0.00012 0.00011 0.00074 4 2029 - 2983D2 0.00015 0.00014 0.00002 0.00001 0.00013 0.00014 0.00010 0.00019 0.00010 0.00010 0.00005 0.00011 0.00012 0.00068 0.00084 0.00009 0.00010 0.00012 0.00011 0.00074 0 2029 - 2983E1 0.00016 0.00015 0.00002 0.00001 0.00013 0.00013 0.00007 0.00019 0.00011 0.00012 0.00005 0.00011 0.00012 0.00027 0.00033 0.00009 0.00010 0.00012 0.00011 0.00023 0 2029 - 2983E2 0.00016 0.00015 0.00002 0.00001 0.00013 0.00013 0.00007 0.00019 0.00011 0.00012 0.00005 0.00011 0.00012 0.00027 0.00033 0.00009 0.00010 0.00012 0.00011 0.00023 0 2029 - 2983F1 0.00016 0.00016 0.00004 0.00002 0.00019 0.00019 0.00006 0.00018 0.00011 0.00012 0.00009 0.00012 0.00013 0.00020 0.00031 0.00010 0.00010 0.00012 0.00011 0.00021 1 2822 - 3920G1 0.00012 0.00012 0.00004 0.00002 0.00016 0.00016 0.00008 0.00015 0.00010 0.00011 0.00007 0.00010 0.00010 0.00044 0.00048 0.00008 0.00008 0.00008 0.00007 0.00038 3 2624 - 3610G2 0.00012 0.00013 0.00004 0.00002 0.00015 0.00015 0.00007 0.00016 0.00012 0.00012 0.00005 0.00010 0.00011 0.00027 0.00030 0.00008 0.00008 0.00008 0.00007 0.00020 1 2656 - 3648H1 0.00014 0.00013 0.00003 0.00002 0.00011 0.00012 0.00077 0.00013 0.00013 0.00012 0.00016 0.00011 0.00011 0.00389 0.00641 0.00076 0.00010 0.00000 0.00009 0.00631 1 2958 - 4243H2 0.00014 0.00013 0.00003 0.00002 0.00011 0.00012 0.00077 0.00013 0.00013 0.00012 0.00016 0.00011 0.00011 0.00389 0.00641 0.00076 0.00010 0.00000 0.00009 0.00631 0 2958 - 4243I1 0.00014 0.00014 0.00003 0.00001 0.00012 0.00012 0.00011 0.00017 0.00010 0.00011 0.00006 0.00011 0.00012 0.00019 0.00024 0.00010 0.00012 0.00010 0.00011 0.00014 2 2890 - 4000I2 0.00014 0.00014 0.00006 0.00003 0.00012 0.00012 0.00011 0.00017 0.00010 0.00011 0.00006 0.00011 0.00012 0.00020 0.00024 0.00010 0.00012 0.00018 0.00020 0.00015 2 2890 - 4000J1 0.00014 0.00015 0.00004 0.00002 0.00010 0.00010 0.00006 0.00017 0.00012 0.00012 0.00004 0.00012 0.00012 0.01222 0.00920 0.00010 0.00012 0.00014 0.00015 0.01213 0 2805 - 3980J2 0.00014 0.00015 0.00004 0.00002 0.00010 0.00010 0.00006 0.00017 0.00012 0.00012 0.00004 0.00012 0.00012 0.00018 0.00022 0.00009 0.00012 0.00014 0.00015 0.00012 4 2805 - 3980W-C 0.00006 0.00006 -0.00004 -0.00007 0.00009 0.00009 0.00067 0.00009 0.00004 0.00004 0.00006 0.00002 0.00003 0.01213 0.00910 0.00066 0.00002 0.00008 0.00010Wins 0 0 0 0 0 2 0 0 0 0 0 1 1 0 0 4 11 0 0

Table 16: Cross-provider FMR interoperability measured over the ALL-FAILURES partition of the OPS database (see section 5.1.1). All KIND 7 records were compressed to 0.2 bits per pixel

(CR = 40) using JPEG2000. The row label identifies the producer of the IREX KIND 7 enrollment record. The column label identifies the SDK that compares (proprietary templates of) the

KIND 7 enrollment record against a KIND 1 verification record. Error rate: In each cell is the FMR at the threshold that gives FMR = 0.0001 when matching uncompressed KIND 1 vs. KIND 1

instances. Colors: The on-diagonal within-provider elements are colored in yellow. Cells filled green indicate FMR better than 0.0001. Cells with red text indicate FMR is better than whenthe producer of the template executes the match. The second to last row and third to last column give worst case values of FMR - 0.0001. The “wins” are the number of times the generator(matcher) gives the smallest absolute change in FMR , i.e. |FMR - 0.0001|.

KIND 16 INTEROPERABLE FALSE MATCH RATES Row Num BPP

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2 Worst Win [5, 95]

%

A1 0.00020 0.00020 0.00002 0.00001 0.00010 0.00011 0.00076 0.00015 0.00085 0.00076 0.00007 0.00012 0.00012 0.00011 0.00017 0.00013 0.00014 0.00020 0.00017 0.00075 3 0.16 - 0.22A2 0.00020 0.00020 0.00002 0.00001 0.00010 0.00011 0.00076 0.00015 0.00085 0.00076 0.00007 0.00012 0.00012 0.00011 0.00017 0.00013 0.00014 0.00020 0.00017 0.00075 1 0.16 - 0.22B1 0.00019 0.00018 0.00003 0.00001 0.00006 0.00006 0.00008 0.00016 0.00012 0.00012 0.00002 0.00011 0.00011 0.00114 0.00069 0.00016 0.00015 0.00021 0.00019 0.00104 4 0.15 - 0.22B2 0.00019 0.00018 0.00003 0.00001 0.00006 0.00006 0.00008 0.00016 0.00012 0.00012 0.00002 0.00011 0.00011 0.00114 0.00069 0.00016 0.00015 0.00021 0.00019 0.00104 0 0.15 - 0.22F1 0.00019 0.00019 0.00002 0.00001 0.00009 0.00009 0.00038 0.00016 0.00014 0.00014 0.00006 0.00010 0.00010 0.00021 0.00024 0.00014 0.00015 0.00021 0.00018 0.00028 2 0.15 - 0.21G1 0.00021 0.00020 0.00002 0.00001 0.00010 0.00010 0.00041 0.00016 0.00121 0.00116 0.00006 0.00010 0.00011 0.00014 0.00016 0.00014 0.00014 0.00019 0.00017 0.00111 2 0.17 - 0.23G2 0.00019 0.00018 0.00002 0.00001 0.00010 0.00010 0.00057 0.00017 0.00148 0.00141 0.00006 0.00010 0.00010 0.00011 0.00014 0.00014 0.00014 0.00020 0.00018 0.00138 2 0.16 - 0.23H1 0.00017 0.00018 0.00002 0.00001 0.00008 0.00009 0.00027 0.00011 0.00028 0.00026 0.00009 0.00009 0.00009 0.00062 0.00067 0.00031 0.00132 0.00000 0.00016 0.00122 5 0.14 - 0.20H2 0.00017 0.00018 0.00002 0.00001 0.00008 0.00009 0.00027 0.00011 0.00028 0.00026 0.00009 0.00009 0.00009 0.00062 0.00067 0.00031 0.00132 0.00000 0.00016 0.00122 0 0.14 - 0.20I1 0.00019 0.00020 0.00002 0.00001 0.00011 0.00011 0.00021 0.00018 0.00018 0.00018 0.00006 0.00012 0.00011 0.00018 0.00023 0.00018 0.00018 0.00023 0.00022 0.00013 0 0.15 - 0.21I2 0.00019 0.00020 0.00002 0.00001 0.00011 0.00011 0.00021 0.00018 0.00018 0.00018 0.00006 0.00012 0.00011 0.00018 0.00023 0.00019 0.00019 0.00023 0.00022 0.00013 0 0.15 - 0.21J1 0.00020 0.00020 0.00002 0.00001 0.00000 0.00000 0.00048 0.00021 0.00045 0.00042 0.00007 0.00001 0.00001 0.03124 0.02539 0.00017 0.00017 0.00023 0.00021 0.03114 0 0.15 - 0.21J2 0.00021 0.00020 0.00002 0.00001 0.00011 0.00011 0.00070 0.00019 0.00032 0.00030 0.00006 0.00012 0.00011 0.00015 0.00022 0.00017 0.00017 0.00023 0.00021 0.00060 0 0.15 - 0.21W-C 0.00011 0.00010 -0.00007 -0.00009 0.00001 0.00001 0.00066 0.00011 0.00138 0.00131 -0.00001 0.00002 0.00002 0.03114 0.02529 0.00021 0.00122 0.00013 0.00012Wins 0 0 0 0 4 3 0 0 0 0 3 2 1 0 0 0 0 0 0

Table 17: Cross-provider FMR interoperability measured over the ALL-FAILURES partition of the OPS database (see section 5.1.1). All KIND 16 records were compressed to a fixed target

of 3000 bytes using JPEG 2000. The row label identifies the producer of the IREX KIND 16 enrollment record. The column label identifies the SDK that compares (proprietary templates of)

the KIND 16 enrollment record against a KIND 1 verification record. Error rate: In each cell is the FMR at the threshold that gives FMR = 0.0001 when matching uncompressed KIND 1 vs.

KIND 1 instances. Colors: The on-diagonal within-provider elements are colored in yellow. Cells filled green indicate FMR better than 0.0001. Cells with red text indicate FMR is better

than when the producer of the template executes the match. The second to last row and third to last column give worst case values of FMR - 0.0001. The “wins” are the number of times

the generator (matcher) gives the smallest absolute change in FMR , i.e. |FMR - 0.0001|.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

82
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 INTEROPERABLE DIFFERENCE: FNMR (k3) - FNMR (k7) FOR JPEG 2000 AT CR = 40, BPP = 0.2 Row Bytes A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2 MAD Npos [5, 95]

%

A1 0.001 0.002 0.000 -0.002 0.000 - -0.004 0.000 -0.004 0.001 0.007 0.004 0.007 -0.015 -0.007 -0.001 -0.002 0.002 -0.004 0.000 7 2673 - 3667A2 0.001 0.002 0.000 -0.002 0.000 - -0.004 0.000 -0.004 0.001 0.007 0.004 0.007 -0.015 -0.007 -0.001 -0.002 0.002 -0.004 0.000 7 2673 - 3667B1 0.002 0.002 0.000 0.001 0.001 0.007 -0.004 -0.001 -0.001 0.000 0.013 0.001 0.010 -0.012 -0.016 -0.001 -0.002 0.003 0.001 0.001 10 2788 - 3920B2 0.002 0.002 0.000 0.001 0.001 0.007 -0.004 -0.001 -0.001 0.000 0.013 0.001 0.010 -0.012 -0.016 -0.001 -0.002 0.003 0.001 0.001 10 2788 - 3920C1 -0.007 -0.012 -0.007 -0.012 0.010 0.015 -0.012 -0.017 -0.005 -0.004 -0.006 0.004 0.011 -0.009 -0.013 -0.007 -0.006 -0.003 -0.007 -0.007 4C2 -0.008 -0.015 -0.007 -0.013 0.016 0.018 -0.016 -0.020 -0.015 -0.007 -0.010 -0.009 0.004 -0.043 -0.028 -0.018 -0.021 -0.005 -0.011 -0.011 3 2755 - 3920D1 -0.030 -0.019 -0.016 -0.052 -0.019 -0.001 -0.005 -0.004 -0.018 -0.016 0.077 -0.034 -0.026 -0.031 -0.030 -0.045 -0.010 -0.019 -0.018 -0.019 1 2029 - 2983D2 -0.030 -0.019 -0.016 -0.053 -0.019 -0.001 -0.005 -0.004 -0.018 -0.016 0.077 -0.034 -0.026 -0.031 -0.030 -0.045 -0.010 -0.019 -0.018 -0.019 1 2029 - 2983E1 -0.047 -0.046 -0.017 -0.057 -0.029 0.009 -0.032 -0.032 -0.046 -0.043 0.032 -0.045 -0.031 -0.046 -0.049 -0.048 -0.051 -0.010 -0.010 -0.043 2 2029 - 2983E2 -0.047 -0.046 -0.018 -0.059 -0.029 0.009 -0.032 -0.032 -0.046 -0.043 0.032 -0.045 -0.031 -0.046 -0.049 -0.048 -0.051 -0.010 -0.010 -0.043 2 2029 - 2983F1 -0.004 -0.007 -0.006 -0.016 -0.025 -0.005 -0.020 -0.022 -0.008 -0.004 -0.010 -0.005 0.004 -0.011 -0.022 -0.006 -0.003 -0.009 -0.005 -0.007 1 2822 - 3920G1 -0.011 -0.018 -0.012 -0.010 -0.019 -0.013 -0.027 -0.020 -0.012 -0.012 -0.012 -0.011 0.000 -0.070 -0.073 -0.014 -0.010 -0.004 -0.010 -0.012 0 2624 - 3610G2 -0.015 -0.022 -0.012 -0.011 -0.025 -0.018 -0.018 -0.020 -0.018 -0.013 -0.001 -0.013 -0.009 -0.029 -0.040 -0.010 -0.010 -0.010 -0.011 -0.013 0 2656 - 3648H1 -0.021 -0.021 -0.031 -0.051 -0.053 -0.031 -0.043 -0.034 -0.034 -0.022 -0.076 -0.044 -0.039 0.342 -0.288 -0.044 -0.037 -0.043 -0.030 -0.037 1 2958 - 4243H2 -0.021 -0.021 -0.031 -0.051 -0.053 -0.031 -0.043 -0.034 -0.034 -0.022 -0.076 -0.044 -0.039 0.342 -0.288 -0.044 -0.037 -0.043 -0.030 -0.037 1 2958 - 4243I1 0.000 -0.008 -0.004 -0.007 -0.009 0.004 -0.020 -0.019 -0.013 -0.010 0.003 0.004 0.023 0.006 -0.003 -0.001 -0.001 -0.001 0.001 -0.001 6 2890 - 4000I2 0.001 -0.007 -0.004 -0.006 -0.007 0.004 -0.020 -0.019 -0.016 -0.012 0.001 0.004 0.021 0.007 -0.003 -0.001 -0.001 0.001 0.000 -0.001 7 2890 - 4000J1 -0.001 -0.007 0.001 -0.002 0.029 - -0.001 -0.004 0.000 0.001 0.008 0.000 0.001 -0.014 -0.004 -0.001 0.000 -0.001 -0.001 -0.001 5 2805 - 3980J2 -0.001 -0.005 0.001 -0.002 0.022 - -0.004 -0.004 0.000 0.001 0.006 -0.001 0.002 0.012 0.008 -0.002 -0.002 0.000 0.000 0.000 7 2805 - 3980MAD -0.007 -0.012 -0.007 -0.011 -0.009 0.004 -0.016 -0.019 -0.013 -0.010 0.006 -0.005 0.002 -0.014 -0.022 -0.007 -0.006 -0.004 -0.007Npos 5 4 2 2 6 8 0 0 0 4 12 7 11 5 1 0 0 5 3

INTEROPERABLE DIFFERENCE: FNMR (k3) - FNMR (k7) FOR JPEG 2000 AT 3000B Row BPP

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2 MAD Npos [5, 95]

%

A1 0.003 0.007 0.008 0.000 -0.001 - -0.007 -0.001 -0.001 0.004 0.001 0.014 0.009 -0.013 0.006 0.003 0.004 0.032 0.013 0.003 12 0.16 - 0.22A2 0.003 0.007 0.008 0.000 -0.001 - -0.007 -0.001 -0.001 0.004 0.001 0.014 0.009 -0.013 0.000 0.003 0.004 0.032 0.013 0.003 12 0.16 - 0.22B1 0.005 0.009 0.013 0.000 -0.001 0.008 0.001 0.000 -0.003 0.001 -0.003 0.018 0.010 0.019 0.016 0.004 0.004 0.016 0.016 0.005 14 0.15 - 0.22B2 0.005 0.009 0.013 0.000 -0.001 0.008 0.001 0.000 -0.003 0.001 -0.003 0.018 0.010 0.019 0.016 0.004 0.004 0.016 0.016 0.005 14 0.15 - 0.22C1 0.001 0.003 0.002 -0.003 -0.037 -0.019 -0.015 -0.017 -0.003 -0.001 -0.013 0.007 0.010 0.061 0.021 0.001 -0.002 0.010 0.010 0.001 10C2 -0.004 -0.005 0.002 -0.005 -0.045 -0.027 -0.021 -0.016 -0.011 -0.007 -0.007 0.004 0.004 -0.018 -0.016 -0.008 -0.012 0.004 0.006 -0.007 5 0.15 - 0.22D1 -0.028 -0.011 -0.011 -0.050 -0.022 -0.003 -0.002 -0.005 -0.017 -0.015 0.074 -0.019 -0.019 -0.043 -0.061 -0.041 -0.006 -0.018 -0.015 -0.017 1 0.20 - 0.30D2 -0.028 -0.011 -0.011 -0.050 -0.022 -0.003 -0.002 -0.005 -0.017 -0.015 0.074 -0.019 -0.019 -0.043 -0.060 -0.041 -0.006 -0.018 -0.015 -0.017 1 0.20 - 0.30E1 -0.048 -0.036 -0.016 -0.055 -0.042 -0.019 -0.034 -0.033 -0.051 -0.046 0.027 -0.032 -0.028 -0.022 -0.032 -0.046 -0.051 -0.015 -0.013 -0.033 1 0.20 - 0.30E2 -0.048 -0.036 -0.017 -0.055 -0.042 -0.019 -0.034 -0.033 -0.051 -0.046 0.027 -0.032 -0.028 -0.022 -0.032 -0.046 -0.051 -0.015 -0.013 -0.033 1 0.20 - 0.30F1 -0.002 0.005 -0.001 -0.010 -0.082 -0.053 -0.021 -0.018 -0.008 -0.004 -0.010 0.005 0.006 0.055 0.003 0.001 -0.001 0.004 0.009 -0.001 8 0.15 - 0.21G1 -0.008 -0.010 -0.006 -0.014 -0.035 -0.026 -0.022 -0.014 -0.011 -0.013 -0.010 -0.011 0.008 -0.043 -0.046 -0.010 -0.009 -0.001 0.004 -0.011 2 0.17 - 0.23G2 -0.010 -0.013 -0.006 -0.010 -0.047 -0.028 -0.018 -0.016 -0.019 -0.019 -0.005 -0.014 0.004 -0.016 -0.019 -0.008 -0.005 -0.001 0.004 -0.013 2 0.16 - 0.23H1 -0.023 -0.017 -0.019 -0.037 -0.023 -0.025 -0.008 -0.030 -0.031 -0.019 -0.082 -0.037 -0.027 0.711 0.049 -0.033 -0.033 0.006 -0.014 -0.023 3 0.14 - 0.20H2 -0.023 -0.017 -0.019 -0.037 -0.023 -0.025 -0.008 -0.030 -0.031 -0.019 -0.082 -0.037 -0.027 0.711 0.049 -0.033 -0.033 0.006 -0.014 -0.023 3 0.14 - 0.20I1 0.004 0.009 0.009 0.001 -0.019 -0.007 -0.018 -0.010 -0.002 -0.004 0.001 0.023 0.020 0.010 0.001 0.007 0.009 0.036 0.023 0.004 13 0.15 - 0.21I2 0.004 0.006 0.011 -0.001 -0.017 -0.006 -0.017 -0.010 -0.004 -0.006 0.001 0.022 0.022 -0.006 0.002 0.009 0.009 0.045 0.027 0.002 11 0.15 - 0.21J1 0.003 -0.002 0.011 0.004 -0.014 0.569 -0.001 0.004 0.010 0.010 -0.010 0.016 0.025 0.028 0.036 0.006 0.005 0.017 0.019 0.010 15 0.15 - 0.21J2 0.002 -0.003 0.009 0.001 -0.001 0.581 -0.004 0.004 0.010 0.010 -0.005 0.007 0.020 0.029 0.017 0.005 0.006 0.016 0.017 0.007 15 0.15 - 0.21MAD -0.002 -0.003 0.002 -0.005 -0.022 -0.019 -0.008 -0.010 -0.008 -0.006 -0.003 0.005 0.008 -0.006 0.002 0.001 -0.002 0.006 0.009Npos 9 8 10 3 0 4 2 2 2 6 8 11 13 9 12 10 8 13 13

Table 18: Cross-provider FNMR (KIND 3 ) - FNMR (KIND 7 ) measured over the ALL-FAILURES partition of the OPS dataset (see section 5.1.1). The row label identifies

the producer of the IREX enrollment record. The column label identifies the SDK that compares (proprietary templates of) the record against a KIND 1 verificationrecord. Error rates: In each cell is the difference in FNMR for KIND 3 and KIND 7 at the threshold that gives FMR = 0.0001 for that combination of algorithm. The top

table applies to images compressed at 40:1. The bottom table applies to images compressed to 3000 bytes. JPEG2000 was used throughout. Colors: The on-diagonal

within-provider elements are colored yellow. Cells with red text indicate |∆FNMR | > 0.02. Cells filled green indicate

∆FNMR

> 0 i.e. KIND 7 outperforms KIND 3 i.e.

the “expected” result, under compression. MAD: These rows and columns give the median absolute deviation. Npos: These rows and columns give the number ofoccasions KIND 7 outperforms KIND 3.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

83
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

8.7. EFFECT OF LOSSLESS COMPRESSION

In section 8.2 the three IREX formats were matched without compression to assess whether they were sufficient for the

intended purpose. It was shown that they invariably offer excellent accuracy relative to the parent 640x480 KIND 1

records. However, as shown in section 7.4, under (severe) lossy compression the impostor distribution moves, often to

the left. This will have negative implications for identification systems and this motivates the study of how far the three

IREX records can be compressed without any change in the pixel values. The ISO/IEC 19794-6 standard allows the use of

two lossless compressors: JPEG2000 and PNG . The result of applying the PNG option to greyscale raster data is presented

here for the four IREX formats. Lossless compression does not alter the original image and therefore preserves the iris

texture exactly as it was rendered by the camera.

The notable observations are drawn from Figure 29 which uses boxplots to show the distributions of the sizes obtained

by application of the PNG compressor to all images in all three datasets. Compressed size is also broken out by SDK.

Variation across SDKs is observed only to the extent that the SDKs prepared the various KINDS differently. The modes of

variation are described in section 8.4.

The notable results for lossless compression are as follows.

. The median size for the most interoperable KIND 3 records (see Table 12), from producers A1, B1 and I1 are around

65KB. Smaller sizes are available around 50KB for the E1 SDK which crops an image to three quarters of the height

that A1 and B1 do. By more tightly cropping the iris, the number of bits per pixel is larger when compressing

to a given file size (here 3000 bytes) and D1 and E1 KIND 3 instances are accordingly among the most matchable

instances for a fixed target file size (see the interoperable error rates in Table 12). Tight vertical margins are sustainable

(see section 8.5) so the size of losslessly compressed KIND 3 records is 50KB (as attained by SDK D1).

. The best interoperable error rates for KIND 7 in Table 13 are again for producers B1 and A1. These images can be

compressed to a median size of about 30KB. This is at least a factor of two smaller than KIND 3, the improvement

obviously due to the compressibility of the mask regions.

. For KIND 16 records, the best interoperable error rates (see Table 14) occur for the A1 algorithm, and are losslessly

compressed down to 10KB. The low size is attributable to the low circumferential and radial sampling rates. The

variance also is very small because the polar image size is fixed. The low mean and variance are distinct advantages

of the KIND 16 record. Whether losslessly compressed KIND 16 records outperform JPEG2000 compressed KIND 7

records at 10KB has not been studied but is worthy of further investigation.

8.8. PREDICTIVE POWER OF IMAGE QUALITY SCORES

Three SDKs, A1, D1, and G1 reported iris image quality scores. These are computed during the preparation of the IREX

records. The quality values are stored in the record header. This section examines the association between those values

and the observed accuracy measures. The analysis is restricted to the quality scores reported for KIND 3 records.

Correlation with quality A1 D1 G1

iFMR -0.051 -0.403 -0.321

iFNMR -0.104 -0.236 -0.334

Table 19: Spearman correlation coefficients for quality scores of SDKs A1, D1 and G1 and image-specific errors. iFMR and iFNMR are

computed at overall threshold of iFMR =0.001 for KIND 1 uncompressed comparisons. Quality scores are computed for KIND 3 record

types. A1 quality scores show little correlation with image error, while D1 and G1 show some correlation.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009 ICE.k03.A1 OPS.k03.A1 BATH.k03.A1 ICE.k07.A1 OPS.k07.A1 BATH.k07.A1 ICE.k16.A1 OPS.k16.A1 BATH.k16.A1 ICE.k03.A2 OPS.k03.A2 BATH.k03.A2 ICE.k07.A2 OPS.k07.A2 BATH.k07.A2 ICE.k16.A2 OPS.k16.A2 BATH.k16.A2 ICE.k03.B1 OPS.k03.B1 BATH.k03.B1 ICE.k07.B1 OPS.k07.B1 BATH.k07.B1 ICE.k16.B1 OPS.k16.B1 BATH.k16.B1 ICE.k03.B2 OPS.k03.B2 BATH.k03.B2 ICE.k07.B2 OPS.k07.B2 BATH.k07.B2 ICE.k16.B2 OPS.k16.B2 BATH.k16.B2 ICE.k03.C1 OPS.k03.C1 BATH.k03.C1 ICE.k07.C1 OPS.k07.C1 BATH.k07.C1 ICE.k16.C1 OPS.k16.C1 BATH.k16.C1 ICE.k03.C2 OPS.k03.C2 BATH.k03.C2 ICE.k07.C2 OPS.k07.C2 BATH.k07.C2 ICE.k16.C2 OPS.k16.C2 BATH.k16.C2 ICE.k03.D1 OPS.k03.D1 BATH.k03.D1 ICE.k07.D1 OPS.k07.D1 BATH.k07.D1 ICE.k16.D1 OPS.k16.D1 BATH.k16.D1 ICE.k03.D2 OPS.k03.D2 BATH.k03.D2 ICE.k07.D2 OPS.k07.D2 BATH.k07.D2 ICE.k16.D2 OPS.k16.D2 BATH.k16.D2 ICE.k03.E1 OPS.k03.E1 BATH.k03.E1 ICE.k07.E1 OPS.k07.E1 BATH.k07.E1 ICE.k16.E1 OPS.k16.E1 BATH.k16.E1 ICE.k03.E2 OPS.k03.E2 BATH.k03.E2 ICE.k07.E2 OPS.k07.E2 BATH.k07.E2 ICE.k16.E2 OPS.k16.E2 BATH.k16.E2 ICE.k03.F1 OPS.k03.F1 BATH.k03.F1 ICE.k07.F1 OPS.k07.F1 BATH.k07.F1 ICE.k16.F1 OPS.k16.F1 BATH.k16.F1 ICE.k03.G1 OPS.k03.G1 BATH.k03.G1 ICE.k07.G1 OPS.k07.G1 BATH.k07.G1 ICE.k16.G1 OPS.k16.G1 BATH.k16.G1 ICE.k03.G2 OPS.k03.G2 BATH.k03.G2 ICE.k07.G2 OPS.k07.G2 BATH.k07.G2 ICE.k16.G2 OPS.k16.G2 BATH.k16.G2 ICE.k03.H1 OPS.k03.H1

BATH.k03.H1 ICE.k07.H1 OPS.k07.H1

BATH.k07.H1 ICE.k16.H1 OPS.k16.H1

BATH.k16.H1 ICE.k03.H2 OPS.k03.H2

BATH.k03.H2 ICE.k07.H2 OPS.k07.H2

BATH.k07.H2 ICE.k16.H2 OPS.k16.H2

BATH.k16.H2 ICE.k03.I1 OPS.k03.I1

BATH.k03.I1 ICE.k07.I1 OPS.k07.I1

BATH.k07.I1 ICE.k16.I1 OPS.k16.I1

BATH.k16.I1 ICE.k03.I2 OPS.k03.I2

BATH.k03.I2 ICE.k07.I2 OPS.k07.I2

BATH.k07.I2 ICE.k16.I2 OPS.k16.I2

BATH.k16.I2 ICE.k03.J1 OPS.k03.J1

BATH.k03.J1 ICE.k07.J1 OPS.k07.J1

BATH.k07.J1 ICE.k16.J1 OPS.k16.J1

BATH.k16.J1 ICE.k03.J2 OPS.k03.J2

BATH.k03.J2 ICE.k07.J2 OPS.k07.J2

BATH.k07.J2 ICE.k16.J2 OPS.k16.J2

BATH.k16.J2

1e+05 9e+04 8e+04 7e+04 6e+04 5e+04 4e+04 3e+04 2e+04 1e+04

ICE OPS BATH

ICE

OPS

BATH

200000 180000 160000 140000 120000 100000 80000 60000 40000 20000

Figure 29: The distribution of losslessly compressed image size in bytes by DATASET, KIND and SDK. The whiskers extend to the value expected to give 99.9% coverage

of a Normal deviate.

The excellent compression of the KIND 16 records is due to the the radial and circumferential sampling of the parent image. This is not a lossless operation in the sense that

polar-to-rectilinear reconstruction yields pixel values different from those in the parent.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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quality

image false match rate

2e−05

5e−05

1e−04

2e−04

5e−04

1e−03

2e−03

5e−03

1e−02

2e−02

0 20 40 60 80

quality

image false non−match rate

0.0

1e−04

0.001

0.01

0.1

1

(a) Quality scores computed by A1

quality

image false match rate

2e−05

5e−05

1e−04

2e−04

5e−04

1e−03

2e−03

5e−03

1e−02

2e−02

0 20 40 60 80 100

quality

image false non−match rate

0.0

1e−04

0.001

0.01

0.1

1

(b) Quality scores computed by D1

quality

image false match rate

2e−05

5e−05

1e−04

2e−04

5e−04

1e−03

2e−03

5e−03

1e−02

2e−02

0 20 40 60 80 100

quality

image false non−match rate

0.0

1e−04

0.001

0.01

0.1

1

(c) Quality scores computed by G1

Figure 30: Quality score vs. image FMR and image FNMR for images in the ICE dataset. The quality scores are computed by the

algorithm identified in the figure label as applied to uncompressed KIND 3 images. Image error rates are computed using comparison

scores of uncompressed KIND 1 records and aggregated over all SDKs. Threshold was set to give global FMR = 0.001 on uncompressed

KIND 1 instances.

8.8.1. RELATIONSHIP OF QUALITY TO IMAGE ERROR RATES

A quality algorithm is effective if it assigns higher quality scores to images with low image false match rate and image

false non-match rates. Figure 30 shows the scatter plot of quality vs. aggregate iFMR and iFNMR. (equations 11 and

13). Histograms of image error rates and quality appear on the side of the plots. For all three quality algorithms, there

are negative correlation between quality scores and image error rates. However, SDKs D1 and G1 quality scores exhibit

a higher correlation with image error rates (see Table 19). Image errors are computed at the threshold of FMR = 0.001

and is aggregated over all comparison SDKs. This threshold is chosen to assure there are few errors per image. Image

false match rates are computed over 16,320 impostor comparisons, and the average number of genuine comparisons per

image is 245.

Figure 31 shows the boxplots of quality scores for images of each KIND and for the four ICE partitions CLEAR ICE, BLUE

GOATS, BLUE WOLVES and BLACK ICE 49. An effective quality algorithm should assign the highest scores to CLEAR ICE

images and the lowest to BLACK ICE images. BLUE GOATS and BLUE WOLVES should have quality scores in between.

Any other result is undesirable. The notable results are as follows.

. Quality scores computed by algorithms D1 and G1 trend in the expected direction (highest quality with CLEAR ICE

progressing down to lowest quality with BLACK ICE. D1 exhibiting a bigger difference in the quality score median

of the four partitions.

. Another observation is the median of G1 quality scores is much higher than those of A1 and D1. That means the

49The partitioning was computed using all comparison algorithms with the threshold set to give a global FMR = 0.001. Recall that CLEAR ICE images

are those with image FMR and image FNMR lower than the operational FMR and FNMR and BLACK ICE are images with both image FMR and FNMR

higher than the operational error rates.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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0

20

40

60

80

100

Quality score

k1_uncomp k3_comp k7_comp k16_comp k1_uncomp k3_comp k7_comp k16_comp k1_uncomp k3_comp k7_comp k16_comp

A1 D1 G1

clear ice blue goats blue wolves black ice

Figure 31: Boxplots of quality scores for the four ICE partitions: CLEAR ICE, BLUE GOATS, BLUE WOLVES and BLACK ICE. All three

image quality algorithms are applied to images of each KIND. The KIND 1 records are uncompressed and all others are JPEG2000

compressed to 2000 bytes. The SDKs are present on the horizontal axis because each was used independently to partition the ICE

images into the four categories. In all cases a global threshold was used, that which gives FMR = 0.001 on uncompressed KIND 1

instances.

same quality score value for G1 and D1 (or A1) will relate to different image error rates. This indicates raw quality

scores are not directly interoperable and some calibration is needed.

. Table 19 shows correlation coefficients between aggregated image error and quality scores of SDKs A1, D1 and G1.

The latter two assign higher quality scores to images with lower image error rate, which is exhibited by a modest

negative correlation. The weak correlation between A1 quality scores and image-specific error rates indicates the

lack of a strong relationship between quality algorithm A1 and image-specific error rates. SDK A1 assigns lower

quality scores to BLACK ICE images, but only slightly higher quality scores to CLEAR ICE than BLUE GOATS or BLUE

WOLVES.

. Quality algorithms might be expected to be most effective in the native case, when the quality computation and the

matcher are from the same provider. Nonetheless, it could be useful if quality scores of one SDK could correlate

with performance of other SDKs. Effectiveness of each SDK in predicting performance of different comparison

algorithms are shown in Figure 32. Each plot corresponds to one of the three quality SDKs that computed quality

scores (A1, D1, and G1). Partition has been done across all comparison algorithms (aggregate iFMR and iFNMR ) at

operational threshold FMR = 0.001 (shown in the leftmost set of boxes) as well as each comparison algorithms.

The plots of Figure 32 are useful in examining association of the quality scores with different comparison algorithms, that

is whether the quality algorithm could be generalized across comparison algorithms. Quality scores generated by SDKs

D1 and G1 are reasonably generalizable to other comparison algorithms: CLEAR ICE images are given higher quality

scores than BLACK ICE images for each comparison algorithm. The median A1 quality scores of BLACK ICE images is

higher than median quality score of CLEAR ICE images for comparison algorithms A2, J1 and J2. Furthermore, A1 quality

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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0

20

40

60

80

100

scoring sdk

A1 quality score

all A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 I1 I2 J1 J2 H2 H1

clear ice blue goats blue wolves black ice

(a) Quality scores computed by A1

0

20

40

60

80

100

scoring sdk

D1 quality score

all A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 I1 I2 J1 J2 H2 H1

clear ice blue goats blue wolves black ice

(b) Quality scores computed by D1

0

20

40

60

80

100

scoring sdk

G1 quality score

all A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 I1 I2 J1 J2 H2 H1

clear ice blue goats blue wolves black ice

(c) Quality scores computed by G1

Figure 32: Boxplots of quality score for the CLEAR, BLUE GOATS, BLUE WOLVES, and BLACK image partitions of the ICE dataset.

Quality scores are computed using the quality algorithms identified in the figure labels as applied to uncompressed KIND 3 records.

ICE partition is done at threshold FMR = 0.001. The set of boxes on the left shows quality scores vs. ICE partition where image errors

are aggregated over all SDKs. The remaining boxes show the quality score distribution for ICE 2006 partition using each comparison

algorithms. Quality algorithm D1 seems to be most effective followed by quality algorithm G1.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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scores of BLUE WOLVES and BLUE GOATS are higher than CLEAR ICE quality scores for comparison algorithms A1, A2,

J1 and J2. These are not the expected behavior and suggests that A1 quality scores cannot be used as a predictor of

performance for A2, J1 or J2 SDKs. For all other SDKs, the difference in A1 median quality scores of images in the four

partitions of ICE dataset, is much less then the difference in median quality scores of D1 and G1.

8.8.2. ERROR VS. REJECT CURVES

Figures 33 and 34 show the error vs. reject curves[26] for images in the OPS dataset for all SDKs. Similar results for ICE

images are shown in Figure 35. The goal is to state how efficiently rejection of low quality samples results in improved

performance. This models the operational case in which quality is maintained by reacquisition after a low quality sample

is detected. Consider that a pair of samples (k, l) from subject i, with qualities q

(k)

i

and q

(l)

i

, are compared to produce a

score s

(kl)

ii , and this is repeated for N such pairs.

We introduce thresholds u and v that define levels of acceptable quality and define the set of low-quality entries as

R(u, v) = n(i, k, l) | q

(k)

i < u, q(l)

i < vo

(20)

We compute FNMR as the fraction of genuine scores below threshold computed for those samples not in this set

FNMR(t, u, v) =







s

kl

ii | s

kl

ii ≥ τ,(i, k, l) ∈/ R(u, v)

	









s

kl

ii | s

kl

ii < ∞,(i, k, l) ∈/ R(u, v)

	



(21)

If the quality values are perfectly correlated with the genuine comparison scores, setting threshold τ to give an overall

FNMR of x and then rejecting x percent with the lowest qualities should result in FNMR of zero after recomputing FNMR.

In Figure 33 we measured FNMR improvements as poor quality samples are rejected. We set the value of τ to give a false

non-match rate of two percent, u and v are varied to show the dependence of FNMR on quality. Likewise, in Figure 34, to

measure FMR improvement as poor quality samples are rejected, we set threshold to give an overall FMR of two percent

and then reject proportion x with the lowest qualities and re-compute FMR. Pair wise quality is computed using the

minimum of the two images being compared.

The most operationally relevant part of the error vs. reject curves is usually on the left side where a small fraction, x,

of low-quality rejections would be tolerable from the perspective of forcing a second enrollment attempt. However, for

the ICE database used here, the appropriate fraction is probably larger because the camera’s own quality measurement

apparatus was suppressed (see section 5.3) such that many of the ICE images wouldn’t ordinarily be available.

For a good quality algorithm, FNMR should decrease quickly with the fraction rejected. An almost flat curve (as is the

case with A1) suggests that the quality algorithm is not effective in prediction of performance. Another desirable feature

of a quality algorithm is how well it can be generalized to predict performance of other SDKs. Figure 33 indicates that

SDK G1 is most effective in prediction of it’s own performance, but also does well with I1, I2 and J1. Rejection of samples

deemed to have low quality by the D1 quality measure improved the FNMR of H1, H2, I1, I2, and J1 more than other

SDKs including D1 or D2.

It is important to note that, because the way images in OPS dataset have been selected, this dataset consists of matchable

images (see section 5.1). The small percentage of images that cause recognition error, are not really problematic images

and so the quality algorithms might be unable to detect and quantify image imperfections to the fine degree needed here

to make a difference in error vs. reject curves. That might be the reason no big improvement in error rates is observed as

poor quality samples are rejected. We conclude that effectiveness of quality scores in predicting the positive or negative

contribution of the image to overall false match and false non-match error rates are more appropriately quantified by

quality score correlation with image-specific error rates.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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0.0 0.1 0.2 0.3 0.4 0.5

0.010 0.015 0.020 0.025

fraction of comparisons rejected

false non−match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(a) Quality scores computed by A1

0.0 0.1 0.2 0.3 0.4 0.5

0.010 0.015 0.020 0.025

fraction of comparisons rejected

false non−match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(b) Quality scores computed by D1

0.0 0.1 0.2 0.3 0.4 0.5

0.010 0.015 0.020 0.025

fraction of comparisons rejected

false non−match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(c) Quality scores computed by G1

Figure 33: FNMR vs. reject curves for quality scores from SDKs A1, D1 and G1 on dataset OPS . The threshold is set to give an initial

FNMR = 0.02. The gray dotted line shows the ideal case where rejection of two percent of lowest quality results in zero FNMR. While

50% rejection is not operationally feasible, it is shown here to display any unexpected behavior of the quality assessment algorithm.

Quality scores of SDK G1 is most effective in predicting performance of SDK G2. Both SDKs D1 and G1 reasonably predict performance

of SDKs I1 and I2. Quality scores of SDK D1 more effectively predicts the FNMR of the SDK I2 than D1.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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0.0 0.1 0.2 0.3 0.4 0.5

0.0170 0.0180 0.0190 0.0200

fraction of comparisons rejected

false match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(a) A1

0.0 0.1 0.2 0.3 0.4 0.5

0.0170 0.0180 0.0190 0.0200

fraction of comparisons rejected

false match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(b) D1

0.0 0.1 0.2 0.3 0.4 0.5

0.0170 0.0180 0.0190 0.0200

fraction of comparisons rejected

false match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(c) G1

Figure 34: FMR vs. reject curves for quality scores from SDKs A1, D1 and G1 on dataset OPS . The threshold is set to give overall

FMR of 0.02. There is no strong relation between quality scores and FMR except for scoring SDK G2 which shows a slight decrease in

FMR when rejecting low quality images assessed by SDK D1 and bigger improvement in FMR when rejecting images assessed to be the

lowest quality by SDK G1. The gray dotted line shows the ideal case where rejection of two percent of lowest quality results in zero

false match rate. While 50% rejection is not operationally feasible, itis shown here to display any unexpected behavior of the quality

assessment algorithm.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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FNMR vs. reject curves for ICE dataset are shown in Figure 35. Results are similar to that observed for the OPS dataset.

Rejection of images judged to have poor quality by SDKs G1 or D1 improves the FNMR of most SDKs except for C1 and F1.

The greatest reduction in FNMR was achieved for A1, I1, I2, G2, H2, H1. In general the FNMR vs. reject curves for SDKs

D1 and G1 are similar. Rejection of samples that SDK A1 considered of low quality scores did not significantly improve

the false non-match rate. One caveat here is that ICE contains multiple images per subject, and the number of images

varies by subject. Therefore rejection of a single image usually results in exclusion of many genuine comparison scores.

To compare effectiveness of quality scores in predicting performance of each SDK, we plotted error vs. reject curves for

each comparison SDK. Figure 36 shows FNMR vs. reject curves for SDKs C1, H1 and G2. Each plot has three curves

corresponding to three quality assessment algorithms A1, D1 and G1. Threshold is set at overall FMR = 0.0001 to ensure

there are enough false non-matches. Quality scores of SDK D1 works reasonably for SDKs H1, H2, G2, and J1. Quality

scores of SDK G1 works well for G2, G1, H1, H2, and I1.

Note that the main difference in plots of Figures 33 and 36 is the choice of threshold at which false nonmatch rates are

computed. For Figure 33 threshold is fixed at per SDK FNMR = 0.02. Threshold value is different for each SDK, but there

is same fraction of false reject for each of them. In Figure 36, the same threshold is applied to all SDKs, therefore different

FNMR results for each SDK. While setting threshold at fixed FNMR per SDK is more reasonable in evaluating effectiveness

of quality algorithms in improving FNMR when rejecting lowest quality samples, fixing the threshold value models an

operational scenario where different comparison or quality algorithms could be deployed at a threshold fixed across the

application.

There is no strong relation between quality scores and FMR . Rejection of samples considered low quality by SDK G1,

improved FMR of G2 slightly, and FMR of H1, H2, J1, I1, and I2 barely. SDK D1 shows a slight decrease in FMR for G2, H1,

H2, and J1. While a decrease in FMR with rejection of bad quality samples would be beneficial, impostor dissimilarity

scores should be high regardless of the underlying quality: False matches should occur only when samples are biometrically similar (with regard to a matcher) as for example when identical twins’ faces are matched.

Quality evaluation is a subject of ongoing research, and further work in this area is underway.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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0.0 0.1 0.2 0.3 0.4 0.5

0.010 0.015 0.020 0.025

fraction of comparisons rejected

false non−match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(a) Quality scores computed by A1

0.0 0.1 0.2 0.3 0.4 0.5

0.010 0.015 0.020 0.025

fraction of comparisons rejected

false non−match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(b) Quality scores computed by D1

0.0 0.1 0.2 0.3 0.4 0.5

0.010 0.015 0.020 0.025

fraction of comparisons rejected

false non−match rate

A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 G1 G2 H1 H2 I1 I2 J1 J2

(c) Quality scores computed by G1

Figure 35: FNMR vs. reject curves for quality scores from SDKs A1, D1 and G1 on dataset ICE . Threshold is set at overall false

non-match rate of two percent. While 50% rejection is not operationally feasible, it is shown here to display any unexpected behavior

of the quality assessment algorithm. Both SDKS D1 and G1 reasonably predict performance of other SDKs except C1 and F1. Quality

scores of SDK A1 does not effectively predicts the FNMR of any SDKs.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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0.0340

0.0 0.1 0.2 0.3 0.4 0.5

0.0345 0.0350 0.0355 0.0360

fraction of comparisons rejected

false non−match rate

quality A1 − k1

quality D1 − k1

quality G1 − k1

(a) C1 OPS

0.0 0.1 0.2 0.3 0.4 0.5

0.0446

0.0447

0.0448

0.0449

0.0450

0.0451

0.0452

fraction of comparisons rejected

false non−match rate

quality A1 − k1

quality D1 − k1

quality G1 − k1

(b) C1 ICE

0.0 0.1 0.2 0.3 0.4 0.5

0.024 0.026 0.028 0.030 0.032 0.034 0.036 0.038

fraction of comparisons rejected

false non−match rate

quality A1 − k1

quality D1 − k1

quality G1 − k1

(c) H1 OPS

0.0 0.1 0.2 0.3 0.4 0.5

0.020

0.025

0.030

0.035

0.040

fraction of comparisons rejected

false non−match rate

quality A1 − k1

quality D1 − k1

quality G1 − k1

(d) H1 ICE

0.0 0.1 0.2 0.3 0.4 0.5

0.0045 0.0050 0.0055 0.0060 0.0065 0.0070

fraction of comparisons rejected

false non−match rate

quality A1 − k1

quality D1 − k1

quality G1 − k1

(e) G2OPS

0.0 0.1 0.2 0.3 0.4 0.5

0.025

0.030

0.035

0.040

fraction of comparisons rejected

false non−match rate

quality A1 − k1

quality D1 − k1

quality G1 − k1

(f) G2 ICE

Figure 36: FNMR vs. reject curves for images matched by selected SDKs and quality assessed by SDKs A1, D1, and G1. The graphs in

the left column apply to OPS images, with ICE images on the right. The threshold is set to give overall FMR of 0.0001, which results in

different initial FNMR for each SDK. For OPS none of the three quality score algorithms is effective in predicting performance of SDK

C1. Quality scores of D1 and G1 seem to be effective for H1. Quality scores of G1 works best for SDK G2. While 50% rejection is not

operationally feasible, it is shown here to display any unexpected behavior of the quality assessment algorithm.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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0.0 0.2 0.4 0.6 0.8

0

2

4

6

8

10

Eyelid Occlusion

Density

OPS

ICE

Bath

Figure 37: For the three IREX datasets, the density of the occlusion metric, Θ, as computed from the KIND 3 and KIND 7 instances

generated by the A1 implementation applied to OPS images.

8.9. EFFECT OF GEOMETRIC PROPERTIES

The following subsections quantify the effects of eyelid occlusion, dilation and iris-pupil center displacement on recognition accuracy. These studies are made possible by utilizing the standardized metadata present in the IREX records.

8.9.1. EYELID OCCLUSION OF THE IRIS

Eyelid occlusion is a well known problem for iris matchers [57, 38, 37]. When an eyelid occludes part of the iris, it

reduces the information available for matching. The upper eyelid tends to be much closer to the iris and is responsible

for a greater amount of occlusion than the lower eyelid. The resting position of the upper eyelid is usually between 0.5

and 2.0 millimeters below the top of the iris[5], but can depend on several factors. For example, when a person is tired,

the upper eyelids droop, and several studies [30, 61, 13] have shown that eyelids lose tension as a person ages. This

causes both the upper and lower eyelids to rest further down on the eye.

A person’s gaze direction can also affect the position of the upper and lower eyelids relative to the pupil center. Read

[55] determined that when a person looks down by 40 degrees, the distance from the pupil center to the lower eyelid

reduces by an average of 2.7mm, and the distance from the pupil center to the upper eyelid reduces by 0.5mm. How an

upward gaze affects the eyelid positions is less well known. The same study found that asians and females tend to have

a smaller distance between the upper and lower eyelids, although Hollingsworth [31] found no statistical difference in

the amount of eyelid occlusion between males and females. Becker et al. [9] noted that the upper eyelid can undergo

idiosyncratic movements of up to 5 degrees while the eye itself is completely stationary. This suggests there may be

some benefit in capturing a sequence of images and retaining only the one with smallest occlusion.

Since eyelid occlusion degrades matching accuracy, a person may attempt to purposefully occlude his iris to elicit a false

match or false non-match. Daugman [14] described a method of compensating for occlusion by normalizing the score

according to how many bits are available for comparison between two iris images. This should transform all samples of

impostor scores to the same binomial distribution. When the impostor distribution is stabilized, the performance of the

iris samples is reflected in the genuine distribution.

Most iris matchers mask the areas of the iris that are occluded by the eyelids before performing the comparison. The

mask typically has a horizontal or parabolic edge. IREX required participants to replace the upper and lower eyelids with

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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Genuine Score

FMR=.01

FMR=.001

0.50.40.30.20.10.0

k3 k7 k16 k3 k7 k16

Uncompressed J2K 2000B

Comparisons with little or no occlusionAll ComparisonsComparisons with heavy occlusion (a) G1 Genuine OPS

Genuine Score

FMR=.01

FMR=.001

0.50.40.30.20.10.0

k3 k7 k3 k7

Uncompressed J2K 2000B

Comparisons with little or no occlusionAll ComparisonsComparisons with heavy occlusion (b) D2 Genuine OPS

Genuine Score

FMR=.01

FMR=.001

0.50.40.30.20.10.0

k3 k7 k3 k7

Uncompressed J2K 2000B

Comparisons with little or no occlusionAll ComparisonsComparisons with heavy occlusion (c) D1 Genuine OPS

Impostor Score

FMR=.01

FMR=.001

0.500.480.460.440.420.400.380.36

k3 k7 k16

Uncompressed J2K 2000B

Comparisons with little or no occlusionAll ComparisonsComparisons with heavy occlusion

(d) G1 Impostor OPS

Impostor Score

FMR=.01

FMR=.001

0.500.480.460.440.420.400.380.36

k3 k7

Uncompressed J2K 2000B

Comparisons with little or no occlusionAll ComparisonsComparisons with heavy occlusion

(e) D2 Impostor OPS

Impostor Score

FMR=.01

FMR=.001

0.500.480.460.440.420.400.380.36

k3 k7

Uncompressed J2K 2000B

Comparisons with little or no occlusionAll ComparisonsComparisons with heavy occlusion

(f) D1 Impostor OPS

Figure 38: The effect of eyelid occlusion on the the genuine (top) and impostor (bottom) distributions for three iris recognition algorithms applied toimages from the OPS dataset.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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a solid color for KIND 7 records, which makes the sample more compressible. In the analyses that follow, the difference

between the cropped KIND 3 and cropped-and-masked KIND 7 records, is exploited to estimate eyelid occlusion. It is

computed as the fraction of pixels within the annular iris region that were replaced by the mask in the KIND 7 record.

Θ =

P

(x,y)∈R δ(Ik3(x, y) − Ik7(x, y))

P

(x,y)∈R 1

(22)

where δ(x) is the Delta function, and R denotes the iris region as approximated by the annulus defined by the best-fit

limbic and pupillary circles reported by the I1 generator. Figure 37 plots the distribution of the measured occlusion for

iris images from each dataset according to A1.

The effect of eyelid occlusion on the comparison score is assumed to be proportional to the maximum occlusion over

the enrollment and verification samples (i.e. Θ = max(Θ1, Θ2)). To measure the effect of eyelid occlusion on recognition

accuracy, comparisons were separated into three groups: those with Θ above the 90th percentile among genuine comparisons; and those with Θ below the 10th percentile; and those in the middle. In this case, the Θ values were 0.1700 and

0.0014 respectively. Figure 38 compares the score distributions for both sets for various algorithms and record formats.

Figures for all SDKs are available in the IREX SUPPLEMENTAL appendices.

Several conclusions can be drawn from the figures:

. Large amounts of occlusion lead to higher genuine scores and lower impostor scores for most algorithms. Some

algorithms produced higher impostor scores when the occlusion amount was large.

. The occlusion effect is not reduced for compressed images. The genuine score distributions for the high and low

occlusion sets appear equally separated regardless of whether the images are JPEG2000 compressed or not.

. All of the record formats appear equally affected by eyelid occlusion.

. Large amounts of eyelid occlusion increase the probability of a false match for D2 but not for D1. Furthermore,

large amounts of occlusion actually reduce the probability of a false match for D1 when the images are JPEG2000

0.0 0.1 0.2 0.3 0.4

0.0

0.1

0.2

0.3

0.4

Eyelid Occlusion in Left Eye

Eyelid Occlusion in Right Eye

 4 

 8 

 12 

 18 

Figure 39: Density plot of eyelid occlusion for left-right eye pairs from the same person. Each point corresponds to an eye pair from

the OPS dataset. If no correlation existed, the contours would be straight lines of slope negative one.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

compressed.

The fact that the eyelid occlusion effect is as pronounced for KIND 7 compressed images as any other is interesting.

The eyelid regions in a KIND 7 record are masked to make the image easier to compress, and one might expect that

images with less visible iris texture would compress better, since less information needs to be retained. It may be that the

variations in the amount of occlusion are not enough to produce a significant effect on how well an image compresses.

When both irises of a person are captured concurrently, the amount of eyelid occlusion presented is expected to be similar

for both images. The OPS images were captured sequentially, but Figure 39 shows that a correlation is still present. This

would offer one explanation for why cross-eye comparisons might be more likely to false match for some algorithms.

8.9.2. DILATION OF THE IRIS

The primary anatomical function of the iris is to control the amount of light that enters the eye by varying the size of the

pupil. This is accomplished by two complementary muscles groups 1) the sphincter muscles, which constrict the pupil,

and 2) the dilator muscles, which expand the size of the pupil. The sphincter muscle fibers are oriented circumferentially

and are located behind the pupillary region of the iris. The dilator muscles are oriented radially and are located behind

the stroma. Although the stroma is nearly flat, it cannot be entirely regarded as a two-dimensional surface since it consists

of a meshwork of interlacing fibrous tissue. When the pupil size changes, these fibers can change both in direction and

shape, altering the apparent texture of the iris surface. For example, Phang [48] noted that crypts close during mydrasis,

but open during miosis, sometimes to reveal underlying iris vessels. This section investigates how variations in pupil

size affect the accuracy of the iris matchers.

The most common method of compensating for deformations caused by changes in pupil size is to assume uniform

elasticity of the iris surface[15]. However, Hollingsworth et al.[31] demonstrated that this results in a pupillary dilation

effect. In particular, it was shown that large differences in pupillary dilation are more likely to produce false non-matches.

Furthermore, Phang[48] noted that the precise surface deformations vary from one person to another, and studies have

demonstrated that non-linear methods of deformation correction are better at reducing error rates[63, 60]. In general,

the iris features near the pupillary region stretch more in the radial direction than those near the periphery, although the

0.0 0.2 0.4 0.6 0.8

0

1

2

3

4

5

Dilation

Density

OPS

ICE

Bath

Figure 40: Estimated dilation densities for the three IREX datasets.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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pupillary region itself stretches less than the immediately surrounding area in the cilliary region.

Pupil size is very difficult to control at the time of capture. It depends primarily on the pupillary light reflex (infrared

light is not in the visible spectrum and does not elicit a pupillary response), and full control of lighting conditions at

the time of capture is rarely guaranteed. Furthermore, several other factors are known to affect pupil size. For example,

extreme emotional states (e.g. fear and pain)[28] cause pupillary dilation, as do certain drugs (e.g. alcohol, opiates)[51].

Drugs that cause pupillary constriction are known as miotics. Persons can be trained to vary the size of their pupils[22].

Thus, it is unrealistic to assume that changes in dilation can simply be eliminated at the time of capture.

The amount of measured pupillary dilation can be quantified as a simple ratio

D =

Rp

Ri

(23)

where Rp and Ri are circular estimates of the pupil and iris radii respectively. The distribution of pupillary dilations for

each dataset is presented in Figure 40. These are computed from information encoded in the IREX record headers. The

I1 SDK was used for this purpose. The amount of pupillary dilation ranged from .10 (lowest) to .84 (highest) among all

three datasets. Values outside this range are likely to be artificially induced by drugs or other means, assuming lighting

conditions are otherwise normal.

To investigate the effect of pupillary dilation on recognition accuracy, the relationship to iFMR and iFNMR (equations 11

and 13) was investigated. The ICE images were partitioned into three sets according to dilation. The high dilation set

contains the 10% of images that have the dilation above 0.5227. The low dilation set contains the 10% of images with

the dilation below 0.3329. The remaining 80% were placed into a third set. These boundary criteria are computed across

dilation estimates from the A1, A2, I1, and J1 SDKs applied to all ICE images.

Effect on FNMR : Figure 41 shows boxplots of aggregate iFNMR for each of the three dilation partitions using shape

information provided by A1, A2, I1, and J1 in the IREX record headers. The distribution of iFNMR is further broken down

by record type and compression state. Figures presenting boxplots of iFNMR for each individual algorithm are available

in the SUPPLEMENTAL APPENDICES. The figure shows a higher distribution of iFNMR for the high dilation set, possibly

as a result of less iris area being available for comparison. This behavior diminishes when the images are JPEG2000

compressed to 2000 bytes. The difference in iFNMR is much less pronounced between the low and medium dilation

sets, suggesting that perhaps only high amounts of pupillary dilation are problematic for matchers. In some cases, the

distribution of iFNMR is actually lowest for the medium dilation set. It may be that the pupil border is sometimes more

difficult to localize when the pupil is highly constricted.

Effect on FMR : Figure 42 presents the same information for iFMR. As with the previous figure, error rates are generally

higher for the high dilation set when the iris images are uncompressed, and the separation between the three sets diminishes when the images are JPEG2000 compressed. The medium dilation set often produces a lower iFMR distribution than

the low dilation set when the images are stored as compressed KIND 16 records. The variation in iFMR increases when

the images are JPEG2000 compressed, as evidenced by the greater inter-quartile ranges.

The reliability of any conclusions drawn from the figures depends on the accuracy of the computed dilation ratios, which

in turn depend on the accuracy of the shape information provided by the SDKs. Nevertheless, it should be noted that

the dilation ratios computed for the different SDKs were generally in agreement. The low dilation sets for each of the

four SDKs share 83% of the same images in common. Similarly, all four high dilation sets share 85% of the same images

in common. Moreover, the most significant observation, that high pupillary dilation increases both iFMR and iFNMR (and

thus the probability of getting either type of error), is true across all four SDKs.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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1e+001e−011e−021e−031e−04

record type

image false non−match rate

k1k1 _ uncompressed k3k1 _ compressed k7k1 _ compressed k16k1 _ compressed

lower 10%: dilation < 0.3596 middle 80% upper 10%: dilation > 0.5545 (a) Dilation estimated using SDK A1

1e+001e−011e−021e−031e−04

record type

image false non−match rate

k1k1 _ uncompressed k3k1 _ compressed k7k1 _ compressed k16k1 _ compressed

lower 10%: dilation < 0.3596 middle 80% upper 10%: dilation > 0.5545 (b) Dilation estimated using SDK A2

1e+001e−011e−021e−031e−04

record type

image false non−match rate

k1k1 _ uncompressed k3k1 _ compressed k7k1 _ compressed k16k1 _ compressed

lower 10%: dilation < 0.3115 middle 80% upper 10%: dilation > 0.4959 (c) Dilation estimated using SDK I1

1e+001e−011e−021e−031e−04

record type

image false non−match rate

k1k1 _ uncompressed k3k1 _ compressed k7k1 _ compressed k16k1 _ compressed

lower 10%: dilation < 0.3276 middle 80% upper 10%: dilation > 0.5182 (d) Dilation estimated using SDK J1 Figure 41: Distribution of aggregate iFNMR for ICE images divided into three sets according to pupillary dilation amount. The amount of dilation was computed using equation 23 and the pupil and limbus radii as reported in the IREX record headers generated by A1 (top-left), A2 (top-right), I1 (bottom-left), and J1 (bottom-right). Aggregate error rates are computed over all SDKs using an SDK dependent threshold that produces an FMR of 0.01 for comparisons between uncompressed images.

iFNMR are shown on a log-scale. 10 percentile and 90 percentile dilation are shown in the legend.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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1e−015e−021e−025e−031e−035e−041e−045e−05

record type

image false match rate

k1k1 _ uncompressed k3k1 _ compressed k7k1 _ compressed k16k1 _ compressed

lower 10%: dilation< 0.3596 middle 80% dilation upper 10%: dialtion> 0.5545 (a) Dilation estimated using SDK A1

1e−015e−021e−025e−031e−035e−041e−045e−05

record type

image false match rate

k1k1 _ uncompressed k3k1 _ compressed k7k1 _ compressed k16k1 _ compressed

lower 10%: dilation< 0.3596 middle 80% dilation upper 10%: dialtion> 0.5545 (b) Dilation estimated using SDK A2

1e−015e−021e−025e−031e−035e−041e−045e−05

record type

image false match rate

k1k1 _ uncompressed k3k1 _ compressed k7k1 _ compressed k16k1 _ compressed

lower 10%: dilation< 0.3115 middle 80% dilation upper 10%: dialtion> 0.4959 (c) Dilation estimated using SDK I1

1e−015e−021e−025e−031e−035e−041e−045e−05

record type

image false match rate

k1k1 _ uncompressed k3k1 _ compressed k7k1 _ compressed k16k1 _ compressed

lower 10%: dilation< 0.3276 middle 80% dilation upper 10%: dialtion> 0.5182 (d) Dilation estimated using SDK J1 Figure 42: Distribution of aggregate iFMR for ICE images divided into three sets according to pupil dilation amount. The amount of dilation was computed using eq. 23 and the pupil and limbus radii as reported in the IREX record headers generated by A1 (top-left), A2 (top-right), I1 (bottom-left), and J1 (bottom-right). Aggregate error rates, shown on a log-scale, are computed over all SDKs using an SDK-dependent threshold that produces an FMR of 0.01 for comparisons between uncompressed images. The 10-th and 90-th percentile dilations are shown in the legend.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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∆D

Density

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0

1

2

3

4

5

6

7

8 Genuine Comparisons

Impostor Comparisons

Figure 43: Estimated probability density of dilation change, ∆D, as computed using equation 24, for genuine and impostor comparisons on images in the OPS database.

DILATION CHANGE

When considering only a single iris image, a constricted pupil may be more preferable since it maximizes the area of the

iris texture. However, when two iris images are compared, the relative change in dilation between the two is expected

to be more relevant. We define the relative change in dilation between the two images being compared as:

∆D = 1 −

 

R

(2)

i

R

(1)

i

! R

(1)

i − R

(1)

p

R

(2)

i − R

(2)

p

!

= 1 −

1 − D(1)

1 − D(2) (24)

where D(1) > D(2) is assumed without loss of generality. The first form of the equation consists of two ratios. The first

is a scaling factor to compensate for possible differences in magnification. The second divides the annular width of the

first iris by that of the second, which provides a measurement of the amount of radial stretching in the iris. Figure 43

shows that the probability distribution for ∆D for genuine and impostor comparisons. Note that the two distributions

differ. In particular, the likelihood of the comparison being between samples of the same iris decrease as ∆D increases.

This is significant because it offers additional discriminating information on the comparison that could be integrated into

an iris matching algorithm. To understand this, assume a given comparison between OPS images, and assume that it is

equally likely to be either a genuine or impostor comparison. If the comparison produces a ∆D of 0.1, then the odds are

approximately 2 to 1 that it is an impostor comparison. If the comparison produces a ∆D of 0.4, then the odds are 10 to

1 that it is a genuine comparison. In this respect, the amount of pupillary dilation could be referred to as a soft-biometric

[35] since it offers some useful information about an individual, but lacks the distinctiveness to sufficiently differentiate

any two individuals.

A large change in dilation is likely to make two images of the same iris appear less similar. A matching algorithm that

is unable to compensate will produce a higher comparison score. To determine if this effect is present, comparisons

were separated into two partitions: those with ∆D > 0.16 (i.e. the 90-th percentile); and those with ∆D < 0.02 (i.e. the

10-th percentile). Figure 44 compares the score distributions for the two subsets for various algorithms and record types.

The figure also shows results when the enrollment images are JPEG2000 compressed to 2000 bytes. Figures for all IREX

algorithms are available in the IREX SUPPLEMENTAL appendices.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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Genuine Score

FMR=.01

FMR=.001

1.41.31.21.11.00.90.80.70.60.50.40.3

k3 k7 k16 k3 k7 k16

Uncompressed J2K 2000B

Comparisons with small dilation changeAll ComparisonsComparisons with large dilation change (a) I1 Genuine OPS

Genuine Score

FMR=.01

FMR=.001

0.60.50.40.30.20.10.0

k3 k7 k16 k3 k7 k16

Uncompressed J2K 2000B

Comparisons with small dilation changeAll ComparisonsComparisons with large dilation change (b) H1 Genuine OPS

Genuine Score

FMR=.01

FMR=.001

0.50.40.30.20.1

k3 k7 k16 k3 k7 k16

Uncompressed J2K 2000B

Comparisons with small dilation changeAll ComparisonsComparisons with large dilation change (c) F1 Genuine OPS

Impostor Score

FMR=.01

FMR=.001

1.361.341.321.301.281.261.241.221.201.181.161.14

k3 k7 k16

Uncompressed J2K 2000B

Comparisons with small dilation changeAll ComparisonsComparisons with large dilation change

(d) I1 Impostor OPS

Impostor Score

FMR=.01

FMR=.001

0.520.500.480.460.440.420.40

k3 k7 k16

Uncompressed J2K 2000B

Comparisons with small dilation changeAll ComparisonsComparisons with large dilation change

(e) H1 Impostor OPS

Impostor Score

FMR=.01

FMR=.001

0.520.500.480.460.44

k3 k7 k16

Uncompressed J2K 2000B

Comparisons with small dilation changeAll ComparisonsComparisons with large dilation change

(f) F1 Impostor OPS

Figure 44: Effect of dilation change on the genuine distribution (top) and impostor distribution (bottom) for three IREX algorithms: I1 (left), H1 (middle), and F1

(right). Results are stated over the 16320 image pairs of the OPS dataset.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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Some common trends were observed. Most notably:

. Genuine scores were increased for the high ∆D set and decreased for the low ∆D set. The trend occurred for all

record formats, and also when the enrollment images were JPEG2000 compressed to 2000B.

. Impostor scores were not appreciably affected by dilation change.

. The dilation effect has less of an effect on the genuine score when the enrollment images are JPEG2000 compressed.

This conclusion is based on the observation that the three dilation partitions tend to be more separated when the

enrollment images are uncompressed.

. The dilation effect was noticeably more pronounced for H1’s KIND 16 records. Error rates were, in general, slightly

higher for H1’s KIND 16 records, indicating that the effect may be due to image processing that occurred during the

conversion to polar format.

That the dilation effect is less pronounced when the enrollment images are JPEG2000 compressed (as evidenced by the

separation of the three partitions, which is less with enrollment image compression) is interesting. A possible explanation

is that the changes in dilation primarily affect the finer details on the iris texture, which get blurred when an image is

JPEG2000 compressed. In other words, the alterations in the texture due to dilation change might get thrown away when

the image is compressed. In the current analysis, only the enrollment images were compressed; if both images were

compressed, the dilation effect might be further reduced.

Previous studies on how variations in pupil size affect comparison scores have restricted their analyses to un-occluded

iris images. This is to eliminate eyelid occlusion as a possible confounding factor. When the pupil is dilated, more of the

iris texture is pushed to the periphery, where it is more likely to be occluded by eyelids or eyelashes. Removing occlusion

is ideal when the goal is to analyze the precise deformations that occur on the iris surface. However, acquiring unoccluded images requires full cooperation from the subjects, which is atypical in operational systems. Most operational

images contain some amount of occlusion, since subjects are usually not instructed to open their eyes wide and stare

directly at the iris camera. If the goal is to determine how dilation change affects operational images, perhaps as part

of a quality measure, then the effect of dilation change on matching accuracy should be measured in the presence of

occlusion.

Since eyelid occlusion and dilation change both affect the comparison score, and both are correlated with each other, it

may be that some of the dilation effect is just a hidden occlusion effect, or vice versa. Therefore, we use logistic regression

to determine the effect that each has on the comparison score independently of the other

P(Y(d)= 1) = (1 + e

−z

)

−1

(25)

where Y is the response variable related to the comparison score, d, and z is the linear predictor defined as

z = β0 + β1 ∆Di + β2 Θi (26)

where βi are the regression coefficients, and Θi and ∆Di are the independent variables representing the amount of

occlusion and dilation change respectively. The coefficients β1 and β2 determine the respective contributions to the

response variable. In our analysis, we relate occlusion and dilation change to the genuine distribution by using FNMR as

the response variable.

Y (s) = H(d − τ ) (27)

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

104
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

FNMR=.10 FNMR=.05

β0 β1 β2 β0 β1 β2

(intercept) (dilation) (occlusion) (intercept) (dilation) (occlusion)

A1 -5.91 3.62 10.13

A1 (j2k 2000B) -5.19 4.99 9.91

A2 -4.08 2.10 (p=0.0058) 3.14

A2 (j2k 2000B) -4.14 1.93 (p=0.0064) 5.10

B1 -4.03 9.18 8.17 -4.64 8.15 7.61

B1 (j2k 2000B) -3.75 5.64 8.6 -4.48 4.60 8.78

B2 -3.98 8.58 8.29 -4.51 7.01 7.53

B2 (j2k 2000B) -3.78 5.65 8.74 -4.43 4.70 8.41

C1 -3.04 4.91 3.79 -3.51 3.04 2.81

C1 (j2k 2000B) -3.02 2.71 5.21 -3.44 1.61 (p=0.0100) 3.30

D1 -3.51 8.67 4.65 -3.9 5.96 3.81

D1 (j2k 2000B) -3.39 7.76 4.41 -3.89 5.77 3.84

D2 -4.34 6.43 12.25 -4.60 4.29 9.84

D2 (j2k 2000B) -4.29 5.75 12.31 -4.61 4.19 9.96

E1 -3.47 7.84 5.01 -4.16 6.85 5.12

E1 (j2k 2000B) -3.37 7.23 4.71 -4.08 6.35 4.95

E2 -3.5 8.12 5.04 -4.20 7.08 5.30

E2 (j2k 2000B) -3.43 7.44 4.97 -4.17 6.69 5.36

F1 -3.02 4.73 3.75 -3.27 2.08 1.47 (p=0.0016)

F1 (j2k 2000B) -3.05 3.82 4.65 -3.40 2.10 2.56

G1 -3.94 4.31 10.83 -4.43 2.91 9.54

G1 (j2k 2000B) -3.75 2.89 10.35 -4.39 2.35 9.56

G2 -3.96 4.01 11.14 -4.49 2.69 10.04

G2 (j2k 2000B) -3.79 3.16 10.45 -4.51 2.99 9.99

H1 -3.25 5.43 5.11 -3.56 3.43 2.95

H1 (j2k 2000B) -3.14 2.38 6.31 -3.68 2.42 4.62

H2 -3.36 6.16 5.43 -3.62 3.69 3.22

H2 (j2k 2000B) -3.17 2.46 6.47 -3.72 2.30 5.00

I1 -4.15 10.99 7.73 -5.05 10.29 8.92

I1 (j2k 2000B) -3.23 3.83 6.07 -4.14 4.25 6.79

I2 -4.18 11.17 7.79 -5.06 10.53 8.8

I2 (j2k 2000B) -3.26 4.00 6.16 -4.12 4.05 6.78

J1 -3.71 7.08 7.32 -4.37 6.30 7.06

J1 (j2k 2000B) -3.55 2.49 9.10 -4.28 2.26 8.84

J2 -3.83 7.73 7.82 -4.64 7.31 8.15

J2 (j2k 2000B) -3.70 2.82 9.91 -4.45 2.11 9.87

Table 20: Results of fitting the logistic regression model, which show the effect of dilation change and eyelid occlusion on the

probability of a false non-match. Results are presented for each algorithm, and at two operating points. P-values are listed when

p > .001.

where d is the genuine score, τ is a user-defined score threshold, and H(.) is the step function of equation 2. The

regression models the probability of a false non-match given the explanatory dilation and occlusion variables. The

response variable was chosen because it focuses on the upper tail of the genuine distribution (i.e. the probability of a

false non-match at a threshold that is closer to what might be used by operational systems). A logit model was chosen

because it assumes a binomial response variable and correctly restricts probabilities to the range (0, 1).

If dilation change exerts only a small independent effect on the genuine score, then its coefficient will be small relative

to the occlusion coefficient. The regression model was implemented in R[2]. P-values were computed for each predictor

using the chi-squared test, which is analogous to the ANOVA test for linear regression. Results of fitting the model to the

OPS database are presented in Table 20. Occlusion values were estimated from C1 records using the method described in

the previous section. Cells for the A1 SDK are left blank when the desired FNMR could not be attained at any threshold.

The following conclusions are made based on the results of fitting the model:

. Eyelid occlusion and changes in dilation both have an adverse effect, but the amount varies depending on the

algorithm. The occlusion amount tends to be a stronger predictor of the probability of a false non-match, but in

many cases dilation change is more significant. In particular, eyelid occlusion is a stronger predictor for A1, A2,

D2, G1, and G2, while dilation change is a stronger predictor for D1, E1, and E2.

. Compressing the enrollment image reduces the effect of dilation change. At an FNMR of 0.05, using JPEG2000

compressed enrollment images reduced the dilation change coefficient for 15 of the 18 algorithms. This behavior

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

105
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

was noted previously.

. The effect of eyelid occlusion and dilation change is significant at both thresholds. This establishes that the two

factors affect the upper tail of the genuine distribution as well as the median and quartiles.

. The genuine distribution for D2 is significantly more affected by eyelid occlusion than that for D1.

. Eyelid occlusion and dilation change always had a statistically significant effect on the probability of getting a false

non-match (assuming a significance level of .01).

Research into pupillary dilation as a predictor of performance is relatively new. Most studies on the subject of iris sample

quality have focused on iris occlusion, gaze direction, and blur. This section confirms that changes in pupillary dilation

do affect recognition accuracy. In fact, pupillary dilation affects the genuine score as much as eyelid occlusion in some

cases. Deformation models are unlikely to entirely eliminate the dilation effect since the iris is multi-layered, and the

precise deformations of the iris surface vary for different people. Therefore, there may be a benefit to storing multiple

images of the same iris at different pupillary dilations. Another alternative is to store additional information describing

the specific deformations that occur. Error rates could be reduced by exploiting the fact that a large dilation change

indicates a decreased likelihood of the two iris images originating from the same person.

8.9.3. DISPLACEMENT OF THE IRIS AND PUPIL CENTERS

Proper iris feature extraction requires an accurate localization of the pupillary boundary, which is known to be neither

perfectly circular, nor precisely concentric with the limbus boundary (see Figure 45). Although the simplest method

of specifying the pupil boundary is to assume a circular shape, studies have shown that fitting with the slightly more

flexible ellipse can improve matching accuracy[58, 56]. More elaborate methods of delimiting the pupillary boundary

include contour-based methods such as snakes[16, 7], and shape-fitting models such as the circular and elliptical Fourier

Series[54]. The IREX specification supported encoding of circular and elliptical boundaries, and the use of Freeman Chain

Codes (FCC) to define arbitrary closed paths.

The precise shape of the pupillary boundary varies for different individuals, but it is unlikely to offer potential as a

discriminator because its shape can vary over time, and in response to dilation. In particular, Wyatt[65] discovered that

the pupil tends to become more circular as it dilates, and less circular as a person ages. The same study discovered that

the boundary shape is fairly consistent during a single session, but can differ across sessions.

Figure 45: ICE image demonstrating that the pupil boundary is neither perfectly circular nor precisely concentric with the limbus

boundary. The center of the pupil is shifted up and to the left relative to the center of the limbus boundary. The pupil and limbus

boundaries are outlined in white.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

106
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

 50 

 100 

 200 

 400 

0.00 0.05

0.00

0.05

Horizontal Displacement

Vertical Displacement

Right Eye

Nose 50 

 100 

 200 

 400 

−0.05 0.00

0.00

0.05

Horizontal Displacement

Vertical Displacement

Left Eye

Nose

Figure 46: Displacement of pupil center relative to limbus center for left and right eyes. The right figure shows displacement for the

left eye. Vertical and horizontal displacement was measured as the pixel disparity between the pupil and limbus centers divided by

the pixel radius of the limbus boundary.

The pupil center (i.e. center of mass) tends to be above and nasal (i.e. closer to the nasal cavity) relative to the center of the

limbus boundary. Figure 46 shows the displacements according to I1, where displacement is measured as the absolute

difference of the pupil and limbus centers divided by the limbus radius. According to I1, the average displacement is

0.04 (±0.02) above and 0.02 (±0.01) nasal. Barry et al.[6] estimated the average at 0.4 mm above, and 0.25 mm nasal,

which is consistent with current results if we assume a constant iris diameter of 11.8 mm. The density plots also indicate

that deviations from the mean location are approximately radially symmetric.

The amount of pupil displacement is correlated for left and right eyes from the same person when geometry information

produced by I1 is used. The Pearson correlation coefficient is 0.411 for OPS images, where the paired data is the distance between the pupil and limbus centers for each pair of eyes. The correlation coefficient implies only a weak linear

relationship, although a significance test using 16318 degrees of freedom produces a two-tailed p-value less than 10−6.

Figure 47 shows the density plot for the difference in pupil displacement for left and right eyes from the same person.

Positive values indicate the left eye pupil is more displaced. For simplicity, the figure does not separate vertical and

horizontal displacement and only considers the absolute amount of displacement between the pupil and limbus centers.

The density plot concentrates about the origin, indicating that neither pupil center has a propensity to be more displaced

than the other. The large amount of variation suggests that the amount of pupil displacement in one eye is not very

indicative of the amount of pupil displacement in the other eye.

To determine if pupil displacement affects the comparison score, comparisons were separated into two sets: 1) those with

a low amount of displacement, and 2) those with a high amount displacement. We define the amount of displacement

for a comparison as the average displacement for both images involved in the comparison. The displacement amount

was considered high if it was above the 90th percentile for genuine comparisons and low if it was below the 10th percentile. Figure 48 compares the score distributions for both sets for various algorithms and record formats. Figures for

all eighteen algorithms are available in the IREX SUPPLEMENTAL appendices.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

107
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

−0.10 −0.05 0.00 0.05 0.10

0

5

10

15

20

Difference in displacement

Density

Figure 47: Distribution of the difference in pupil displacement between left and right eyes from the same person. Positive values

indicate the left eye pupil has greater displacement.

The amount of pupil displacement has a small but discernible effect on the genuine score for some algorithms, most

notably J1, where higher displacements tend to generate higher genuine scores. There are several possible explanations

for this effect. An algorithm that assumes concentricity of the pupil and iris centers would fail to produce an accurate

segmentation of the iris features when the pupil center is significantly displaced from the iris center. Large pupil displacements might also make it more difficult for algorithms to compensate for deformations of the iris due to dilation

change. The most likely reason, however, is that large displacements are indicative of an iris image that is ill-suited for

matching due to some other reason. For example, a contact lens can distort the appearance of the iris and translate the

apparent position of the pupil. Further investigation into the cause of the pupil displacement effect is recommended.

8.10. IMAGE ERROR RATE ESTIMATES

For any given image, its image-specific error rates will vary depending on the algorithm. That is iFMR and iFNMR will

be expected to vary when computed using comparison scores of different algorithms. This variation is addressed here.

Figure 49 shows the scatter plot of aggregate iFNMR vs. iFMR for ICE images. Each plot corresponds to one of the record

types: uncompressed KIND 1, JPEG2000 compressed (to 2000 bytes) KIND 3, JPEG2000 compressed (to 2000 bytes) KIND

7, and JPEG2000 compressed (to 2000 bytes) KIND 16. The dotted black lines mark the FMR and FNMR at the operating

threshold of FMR = 0.001. The spread of points demonstrates image performance variability. Images can be categorized

according to their level of difficulty.

. CLEAR ICE These are image for which the aggregate iFMR is less than the nominal FMR, and aggregate iFNMR is less

than the nominal FNMR. These images occupy the lower left quadrant of the plots and may be considered easy to

recognize.

. BLACK ICE These images occupy the upper right quadrant. They are the most challenging images of the ICE dataset

since their image error rates are higher than the nominal error rates indicated by the dotted black line.

. BLUE GOATS Images in the top left quadrant have iFNMR > FNMR (τ ) and iFMR ≤ FMR (τ ). These are more

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

108
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

Match Score

FMR=.001

FMR=.01

0.1

0.2

0.3

0.4

0.5

k3 k7 k16 k3 k7 k16

Uncompressed JP2 2000B

Comparisons with small pupil displacement

All Comparisons

Comparisons with large pupil displacement

(a) F1 Genuine OPS

Match Score

FMR=.001

FMR=.01

0.0

0.1

0.2

0.3

0.4

0.5

k3 k7 k16 k3 k7 k16

Uncompressed JP2 2000B

Comparisons with small pupil displacement

All Comparisons

Comparisons with large pupil displacement

(b) G1 Genuine OPS

Match Score

FMR=.001

FMR=.01

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

k3 k7 k16 k3 k7 k16

Uncompressed JP2 2000B

Comparisons with small pupil displacement

All Comparisons

Comparisons with large pupil displacement

(c) I1 Genuine OPS

Match Score

FMR=.001

2000

2500

3000

3500

k3 k7 k16 k3 k7 k16

Uncompressed JP2 2000B

Comparisons with small pupil displacement

All Comparisons

Comparisons with large pupil displacement

(d) J1 Genuine OPS

Figure 48: Effect of pupil displacement on the genuine distribution for various algorithms. Results are stated over the 16320 image

pairs of the OPS dataset.

frequently falsely rejected but do not attract false matches.

. BLUE WOLVES Images residing in the bottom right quadrant have iFMR > FMR (τ ) and iFNMR ≤ FNMR (τ ). These

images are implicated in more false matches and are generally easy to match.

An interesting observation is variation in aggregate image error rates across the four plots of figure 49. Aggregate image

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

109
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

image false match rate

image false nonmatch rate

●

clear ice: 56.25%

blue goats: 12.13%

blue wolves: 24.41%

black ice: 7.21%

2e−05 1e−04 5e−04 2e−03 1e−02 5e−02 2e−01 1e+00

0.0

1e−04

0.001

0.01

0.1

1

(a) k1k1 uncompressed

image false match rate

image false nonmatch rate

●

clear ice: 14.56%

blue goats: 6.71%

blue wolves: 58.9%

black ice: 19.84%

1e−04 5e−04 2e−03 5e−03 2e−02 5e−02 2e−01 5e−01

0.0

1e−04

0.001

0.01

0.1

1

(b) k3k1 - JPEG2000 2000B

image false match rate

image false nonmatch rate

●

clear ice: 22.8%

blue goats: 7.25%

blue wolves: 54.26%

black ice: 15.7%

5e−05 2e−04 1e−03 5e−03 2e−02 1e−01 5e−01

0.0

1e−04

0.001

0.01

0.1

1

(c) k7k1 - JPEG2000 2000B

image false match rate

image false nonmatch rate

●

clear ice: 13.02%

blue goats: 11.37%

blue wolves: 51.81%

black ice: 23.8%

2e−05 1e−04 5e−04 2e−03 1e−02 5e−02 2e−01 1e+00

0.0

0.001

0.01

0.1

1

(d) k16k1 - JPEG2000 2000B

Figure 49: Image FNMR vs. image FMR for 31415 images of the ICE dataset for various KINDS. The image errors are computed at

score threshold corresponding to global FMR = 0.001 and aggregated across all SDKs. The red dot in each plot is the center of mass

(computed as the mean value) of the image errors. The black dotted lines corresponds to aggregate error rate of the system. Image

false non-match (and false match) probability densities are plotted on the top (and left side). The legend shows percentage of CLEAR

ICE, BLUE GOAT, BLUE WOLF and BLACK ICE images.

errors for JPEG2000 compressed KIND 16 show a wider spread in image FMR than the image FNMR , while for JPEG2000

compressed KIND 7 the spread is wider in image FNMR.

Figure 50 shows the cumulative distribution functions (CDFs) of aggregate iFMR for four leading implementations. Ideally, all images have equal likelihoods for error, in which case the CDFs would be step functions. In practice some images

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

110
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

1e−04 5e−04 1e−03 5e−03 1e−02 5e−02 1e−01

0.0 0.2 0.4 0.6 0.8 1.0

image false match rate

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k1k1

k7k1

k3k1

k16k1

(a) A1 FMR

1e−04 5e−04 1e−03 5e−03 1e−02 5e−02 1e−01

0.0 0.2 0.4 0.6 0.8 1.0

image false match rate

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k1k1

k7k1

k3k1

k16k1

(b) D1 FMR

1e−04 5e−04 1e−03 5e−03 1e−02 5e−02 1e−01

0.0 0.2 0.4 0.6 0.8 1.0

image false match rate

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(c) I1 FMR

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0.0 0.2 0.4 0.6 0.8 1.0

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(d) J1 FMR

Figure 50: Cumulative distribution functions of the image false match rates for selected SDKs. Each CDF of iFMR is computed for each

KIND, at thresholds corresponding to overall FMR = 0.01 (on uncompressed KIND 1 instances). The KIND 1 records are uncompressed,

all others are JPEG2000 compressed to 2000 bytes. The CDFs of both image error rates are included in the IREX SUPPLEMENTAL

appendices for all SDKs.

produce fewer errors, and some more. A matching algorithm that constrains the worst-case behavior is preferable. The

notable observations are:

. While the threshold is set to FMR = 0.0150, it is generally the case that more than 50% of images have image false

match rates less than 0.01.

. For some SDKs, the application of lossy compression causes iFMR distributions to move to the right and to broaden.

The problematic area is the upper tails of the iFMR distributions where, especially for KIND 16 and KIND 3 images,

there are higher numbers of false matches.

. The CDFs of the A1 implementation are most similar, and are more bounded at the top end.

8.10.1. DEPENDENCE ON COMPARISON ALGORITHM

Figure 49 is useful in assessing the difficulty of an image (and so the dataset if proper summarization is performed)

because image error rates are computed across a diverse set of comparison algorithms. However, it does not reveal

50The iFMR is computed over 16320 comparisons, so it is necessary to use a high threshold to get observable errors.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

111
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

image false match rate

image false non−match rate

●

clear ice: 57.75%

blue goats: 12.68%

blue wolves: 25.81%

black ice: 3.76%

0.0 1e−05 5e−05 5e−04 5e−03 5e−02 5e−01

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1

(a) I2 k1k1 uncompressed

image false match rate

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clear ice: 68.13%

blue goats: 9.71%

blue wolves: 14.85%

black ice: 7.32%

0.0 1e−05 5e−05 5e−04 5e−03 5e−02 5e−01

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(b) E1 k1k1 uncompressed

Figure 51: Image FNMR vs. image FMR for 31415 images of the ICE dataset for two SDKs. Image errors are computed at the threshold

that gives global FMR = 0.001 for each SDK . The red dot in each plot is the center of the mass (computed as the mean value) of

the image errors. The black dotted lines correspond to aggregate error rate of the system. Image false non-match (and false match

probability) density is plotted on the top (left side). The relative spread of the image errors suggests comparison algorithm I1 is more

robust to image variation than comparison algorithm E1. The legend shows percentage of clear ice, blue goat, blue wolf and black ice

images.

image error variations among comparators. An image with a high FMR or FNMR for one comparison algorithm, might

result in low image error rates when a different comparison algorithm is applied. In other words, a difficult to process

and recognize image for one algorithm, might be easy for another algorithm. To examine the dependence of image

errors on comparison algorithms, we computed image error for each comparison algorithm. Figure 51 shows image

error rate for SDK I2 and SDK E1. The ideal case is when a matching algorithm produces constant false matches and false

non-match (with a very small spread) for any image regardless of its underlying properties and quality. However, the

relatively wide spread and heavy tails of the distributions in Figure 51(b) suggest that it is not the case in the real world.

It can be seen that the spread of the blue cloud (and the histogram of image errors) is different for the two algorithms.

That means an image that presents wolf-like behavior to one algorithm (i.e. its iFMR is larger than overall FMR of the

system at the operating threshold) might be a sheep when matched by a different comparison algorithm (i.e. not causing

any false matches). This is a strong argument for fusion of matching algorithms. Image error plots for each SDK are in

the IREX SUPPLEMENTAL appendices.

The cause of the performance variations is interesting. The extent to which combinations of image (or user) covariates

and/or matching algorithm might cause this is, to our knowledge, unreported. Another interesting problem is whether,

regardless of the matching algorithm, there are wolves (or goats) at large (i.e. users that account for disproportional share

of the overall FMR or FNMR ). We intend to pursue both subjects in the future. For now, we focus on investigating a)

how to quantify the level of difficulty of an image (and so a dataset) and b) the ability of matching algorithms to produce

comparison scores that are robust to variation in image (or user) covariate. In other words, if an algorithm operating at

a fixed threshold could maintain a relatively constant false match rate or false non-match rate regardless of image (or

user) properties.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

112
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

9. CONSIDERATIONS FOR OVERALL PERFORMANCE

The performance data contained in this report and its accompanying IREX SUPPLEMENT is intended to guide developers,

standards makers, and users. This last category might include policy makers, system integrators, designers and analysts.

This discussion suggests that no single best algorithm exists because the definition of best is multiply defined and should,

in any case, be considered in response to operational requirements.

The following text lists criteria that will have more or less importance in a given application.

. False rejection In many performance tests, particularly offline technology tests, there is an explicit emphasis on

false non-match rate as the primary performance measure. Thus, reports will summarize a DET characteristic

by tabulating FNMR at a fixed FMR 51. This is clearly a valid and valuable discriminator between algorithms or

technologies for access control-like applications in which false rejection performance is the primary performance

indicator. This is reasonable because enrolled users of the system are inconvenienced by high FRR, and because

impostors are often rare (i.e. prior probability is small).

But in non-access control applications, the primacy of FRR is less justified. For example, in a watchlist open-set

identification scenario[1] the enrolled persons may very rarely appear and the primary performance metric is the

rate at which un-enrolled passers-by are falsely matched. This too necessitates control and stability of FMR. On the

“FNMR at FMR = 0.0001” metric the best three IREX providers are:

– For uncompressed KIND 1 images: I, B, J (In Figures 10, 11, and 12, for the OPS, BATH and ICE datasets respectively, the leading two are I and B. Thereafter J is is in third place except for the BATH dataset, where A and D

perform better).

– For KIND 3 images JPEG2000 compressed to 0.2 bits per pixel: I, B, J (see column means in the penultimate row

of Table 12).

– For KIND 7 images JPEG2000 compressed to 0.2 bits per pixel: I, B, J (see column means in the penultimate row

of Table 13).

– For KIND 16 images JPEG2000 compressed to 2000 bytes: I, J, A (see column means in the penultimate row of

Table 14).

However, the above assumes that a security objective (expressed by some low FMR requirement) can be met via

appropriate setting of a threshold. While this can always be achieved by setting a conservative threshold, it requires

some calibration of the false match rate (either by the manufacturer or by a testing laboratory) and some assurance

that the false match rate is stable across, at least, time, environmental conditions, and population. This advocates

for the second performance criterion below, impostor distribution stability.

. Impostor distribution stability The stability of the impostor distribution is a very valuable property for a biometric

system that is used out-of-the-box with a fixed operating threshold because it gives predictable occurrence of false

matches. In one-to-many identification applications it means that the expected number of false matches produced

in a search, a metric called selectivity[10] will be stable.

Figures 24 and 14 (and the corresponding Figures in the IREX SUPPLEMENTAL APPENDICES) show that some algorithms produce somewhat varying impostor distributions across datasets and compression levels. Note that in

advocating this as an important algorithm performance metric:

51Many studies refer to Type I and Type II error rates as false reject rate, FRR and false accept rate, FAR and they do so with identical meanings to the

FNMR and FMR quantities used here. However, the SC37 Working Group 5 committee usefully gave different meanings to these terms to express the

difference between matching error rates and transactional rates, the former being the result of comparison of two images or templates and the latter

being the result of an attempt by a user to authenticate to a system (for example by retrying, or by presenting a second eye).

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

113
IREX IREX - INTEROPERABLE IRIS EXCHANGE October 30, 2009

– We are not denying the existence of wolves and lambs in the biometric zoo[20] but instead requiring their

prevalence and behavior to be stable.

– We acknowledge that while score normalization can be applied on samples of the operational data in order to

stabilize the distribution, there are two problems with this approach: First is that the capability must actually

be installed and maintained; and second that it is most effective in a closed operational environment in which

the cameras and population are known. In federated applications in which standardized data records are

collected remotely (e.g., worldwide) in a variety of circumstances (cameras, compression regimes) the ability

to control impostor distributions is more difficult.

The SDKs from the following IREX providers demonstrate generally small variation in the impostor distributions:

B, A, E and D (see the lines of constant-threshold in the four sets of DETs in Figure 24).

. Resistance to compression The ability of a recognition system to function after compression has been applied

is vital in some applications. Compression makes both the localization and the feature extraction more difficult,

because it blurs boundaries and alters the iris texture. Referring to the cropping survey results in Figure 28, the

algorithms giving the smallest increases in FNMR values (i.e. conditional FNMR ) are from providers A, D, B.

. Localization Instantiation of the KIND 3 record requires only an initial detection and cropping of the iris. Referring

to Figure 12 the lowest average FNMR at FMR = 0.0001 values are available from the KIND 3 IREX records produced

by SDKs from providers A, B and I.

For KIND 7 there is the additional task of finding the iris-sclera and iris-eyelid boundaries. In this respect the list of

best providers is identical to that for KIND 3 A, B, and I.

For KIND 16 polar records, the implementation performs the pupil and iris detection tasks, choses radial and circumferential sampling rates and performs rectlinear-to-polar interpolation. For this operation providers A, J and I

instantiate the most interoperable instances.

. High speed Computational cost is clearly important as a performance related variable. The relative contributions

of the durations of the various functions (preparation of standard images, template generation, and matching) need

to be factored into the particular application (e.g., one-to-one vs. one-to-many in a large population.) Referring to

the figures of section 7.6, the SDKs from the following IREX providers have lowest computational cost: D, G, J.

10. PROPOSED CHANGES TO THE STANDARDS

The following comments are made toward the current commmittee draft of the ISO/IEC 19794-6

. The KIND 16 unsegmented polar format should be deleted from the draft standard.

. Some version of the guidance given in Figure 52 should be inserted into the revised ISO/IEC 19794-6 standard. This

content should be advanced as a informative annex and accompanied by a note that there may be some dependency

on the iris camera in use.

. JPEG should not be used for compact records. For 1:N applications lossy compression should be discouraged and

JPEG should be entirely prohibited.

. A conformance testing Annex should be developed as a normative Annex of ISO/IEC 19794-6 - The current project

ISO/IEC 29109-6 is targeting the 2005 standard - NIST expects the new revised standard to be adopted vs. the 2005

one.

A = SAGEM B = COGENT C = CROSSMATCH D = CAMBRIDGE E = L1 x1 = PRIMARY

F = RETICA G = LG H = HONEYWELL I = IRITECH J = NEUROTECHNOLOGY x2 = SECONDARY

KIND 1 = RAW 640x480 KIND 3 = CROP KIND 7 = CROP+MASK KIND 16 = CONCENTRIC POLAR

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Recommended Target Record Size 

Role Format Compressor 2KB 4KB 8KB 16KB 32KB 64KB 128KB 256KB 307KB 

All KIND 1 Uncompressed 

All KIND 3 Uncompressed 

All KIND 7 Uncompressed 

All KIND 3 PNG Lossless 

All KIND 7 PNG Lossless 

1:N KIND 3 JPEG 2000 Lossy 

1:N KIND 7 JPEG 2000 Lossy 

1:1 KIND 3 JPEG 2000 Lossy 

1:1 KIND 7 JPEG 2000 Lossy 

Figure 52: Provisional recommendations on the application of compression to standard image formats. The horizontal axis shows

target file size in kilobytes on a logarithmic scale. Note the dependence on the intended application: Identification applications should

avoid lossy compression. This guidance is provisional.

. Spectral information should be added to the standard.

. Lossless compression should be recommended as the default container for iris imagery. High Q-value JPEG compression should not be deprecated.

. The standard should acknowledge 640x480 as a de facto, but allow the possiblity that larger images may become

common, and useful. The standard needs refined guidance on iris size.

. A field to encode pupil dilation should be added.

. The WG3 committee should consider whether a numerical field could be added to the standard that quantifies compression damage. The purpose is to augment score normalization techniques. Without this field a blind estimate

of compression is needed.

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