IREX VI
 

Temporal Stability of Iris Recognition Accuracy

NIST Interagency Report 7948

P. Grother J. R. Matey E. Tabassi G. W. Quinn M. Chumakov

Information Access Division
 

National Institute of Standards and Technology
 

July 11, 2013
 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 1 

Executive Summary
 

Purpose: This work was conducted determine whether iris recognition accuracy decreases with the time lapsed between 

collection of initial enrollment and recognition images. More specifically, it seeks to quantify accuracy changes associated 

with any permanent changes to the iris and its proximal anatomy. This study is intended to quantify natural ageing effects 

in a healthy population; medical conditions and injuries can rapidly and severely affect recognition, so these are out of 

scope. 

Background: Stability is a required definitional property for a biometric to be useful. Quantitative statements of stability 

are operationally important as they dictate re-enrollment schedules e.g. of a face on a passport. The ophthalmologists who 

filed initial patents on iris recognition posited the iris to be “extremely stable” over “many years” but that “features which 

do develop” do so “rather slowly”[31]. A further patent held that irises have “texture of high complexity, that prove to 

be immutable over a person’s life”[21]. This view held until several recent empirical studies suggested otherwise. Those 

studies, and ours, were motivated to check the veracity of the 1994 patent’s assertion that an enrolled iris can be viable over 

decades. Two studies, using separate iris image collections from the University of Notre Dame, reported a large increase 

in false rejection rates[8, 29]. The studies made attempts to account for several possible causes of the observed ageing, but 

could not conclude that the iris texture itself was changing. Their results, however, were widely reported[59, 24, 3] with 

statements such as “irises, rather than being stable over a lifetime, are susceptible to ageing effects that steadily change 

the appearance over time”[33]. A further study, however, identified pupil-dilation[27] as the primary causal variable. 

Operational iris systems have identified individuals over periods up to 10 years[5] and 7 years[6]. 

Conclusions: Using two large operational datasets, we find no evidence of a widespread iris ageing effect. Specifically, the 

population statistics (mean and variance) are constant over periods of up to nine years. This is consistent with the ability 

to enroll most individuals and see no degradation in overall recognition accuracy. Furthermore, we compute an ageing 

rate for how quickly recognition degrades with changes in the iris anatomy; this estimate suggests that iris recognition 

of average individuals will remain viable over decades. However, given the large population sizes, we identify a small 

percentage of individuals whose recognition scores do degrade consistent with disease or an ageing effect. These results 

are confined to adult populations. Additionally, we show that the template ageing reported in the Notre Dame studies 

is largely due to systematic dilation change over the collection period. Pupil dilation varies under environmental and 

several biological influences, with variations occuring on timescales ranging from below one second up to several decades. 

Our data suggests that the natural constriction of pupil size over decades does not necessitate re-enrollment of a well 

enrolled iris. The I S O /I E C 19795-1 testing standard defines ageing as any increase in error rates with time. This definition 

is imprecise because temporary changes due to environment (e.g. lighting) or user behavior (e.g. blinking) might yield 

elevated error rates without any change in the biometric source itself. Dilation has been suggested to be part of ageing 

under that definition. Instead we assert that ageing stems from irreversible changes to the anatomy, primarily the iris 

texture. Dilation should not be considered part of ageing because it varies stochastically and can be mitigated - some 

iris cameras normalize dilation by shielding or by active illumination. Corneal shape changes have been suggested as 

influential on iris recognition too, but their effect has never been quantified. 

Technical Summary 

Approach: Given observations of the kind shown in Figure 1, we consider ageing to be a time-series problem[16] requiring 

analysis of dissimilarity scores between enrollment and recognition images as a function of elapsed time. 

This derives from Czajka’s suggestion[16] that permanent changes in the iris texture would give a nonstationary genuine score distribution, such that ageing would generally produce non-linear and even 

non-monotonic changes to score statistics. We restrict our analysis to the linear case, using longitudinal mixed-effects regression models to compute ratesof-change of each eye’s scores, and the populationaverage thereof. 

Detection of long-term trends is complicated by shortterm stochastic variations inherent in acquiring digital images from a live analog anatomic source. Here 

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Figure 1: Varying recognition outcomes: Hamming distances (HD) from 

comparison of an enrolled iris image with images collected in a physical 

access control system over three years. Small H D values indicate high 

similarity between iris images. Note that high values are often followed 

by low values, and a trend is difficult to discern. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

1 
JU LY 11, 2013	 I RE X V I - IR I S A G EI NG 2 

variations in quantities such as pupil dilation, gaze angle, focus, and eyelid position manifest themselves as the “measurement noise” evident in Figure 1. 

Also, we apply eight contemporary iris recognition algorithms to the images used in the Notre Dame studies. We relate 

pupil dilation and exposed iris area measures to recognition outcomes. Additionally, we review other published studies, 

formulate recommendations for conduct of biometric ageing studies and for the mitigation of ageing effects in operational 

systems. 

Results: The primary results of this investigation are as follows. 

�	 Rate of accuracy change: Our best estimate of iris recognition ageing is derived from a 7876 person subset of 

an operational registered traveler deployment[5] who have used the system on forty or more occasions over 

at least four years and up to nine years. The estimate is a population-average from a linear mixed-effects 

regression model. It states Hamming distances increase at a rate of (8 ± 2)x10−7 per day. Sec. 4.2 

We put this rate into context in three ways. First, this rate, if sustained, would mean that an average person 

would exhibit an increase ΔH D= 0.003 over a ten year period. Such an increase is an order of magnitude 

smaller than the variation (σ = 0.04) that an average individual exhibits in routine usage of an iris camera. If 

sustained this rate would cause consistent identification failure only over time periods longer than a human 

lifetime. Second, this age-related change is far smaller than the change measured for the use of different 

cameras for enrollment and search (ΔH D= 0.05). Third, this change is smaller than that observed between 

frequent and infrequent users of a border-crossing system (ΔH D= 0.02). Our rate-of-change estimate must 

be considered provisional pending application of refined statistical techniques to larger and richer data sets, 

generalization to other recognition algorithms, better modelling of dilation and inter-camera comparisons, and 

consideration of bilateral ageing in both eyes. 

A low population-average ageing rate does not necessarily mean that some persons do not age more quickly. 

Our individual-specific measurements on this dataset show symmetric variation around the population mean 

- roughly equal numbers of subjects have increasing and decreasing score trajectories. The rate-of-change 

estimate varies with the camera used, and with the subset of the population used. Additionally, given the 

population size, we expect some instances of ocular disease to cause some variation. Sec. 4.2.1. 

�	 Field operational data: When state-of-the-art recognition algorithms are applied to 3.5 million images collected 

over a period of about 6 years from 622,464 subjects the genuine score distributions are stable and show no 

evidence of an widespread upward template-ageing trend. Sec. 4.4. 

�	 Re-analysis of Notre Dame collections: We use eight recent commercial recognition algorithms to broadly reiterate 

the empirical time dependence reported for both the 2004-2008 and 2008-2010 Notre Dame (N D) collections. 

However, three observations in the second collection motivate an analysis that explains the observed variation. 

We note a) heterogeneity in the false non-match errors, particularly the errors are concentrated in fewer than 

one third of the individuals, b) that pupil dilation varies, particularly across the three collection semesters, 

with widespread pupil constriction in 2010, and c) that accuracies over consecutive one year intervals differ. 

The dilation change is key. When amounts proportional to the dilation difference between two images are 

subtracted from the observed iris dissimilarities, the ageing effect substantially disappears or is at least difficult 

to detect given the residual presence of other influential factors in the images. Sec. 4.3.1 

�	 New methodologies for biometric ageing studies: In the domain of biometric performance testing, this study 

innovates in at least two ways. a) It includes extensive visualizations of individual-specific measurements. 

These are advanced as exploratory analysis[93] precursors to more quantitative methods. b) The adoption of 

mixed-effects regression models from the medical literature should be directly relevant to other longitudinal 

biometric studies, particularly face ageing where time dependence is clearly evident. This arises from the 

models’ ability to handle imbalanced, irregularly sampled, individual-specific observations, with temporal 

correlations. Additionally, the random-effects afford modeling of “biometric zoo”[25] heterogeneities across 

individuals. Sec. 4.2.1 

�	 Guidance on ageing measurement: The report lists technical considerations for organizations engaged in biometric 

ageing studies. These include a recommendation that ageing studies should adopt the tight image acquisition 

controls used in many ophthalmalogical studies, and in some iris recognition studies. Sec. 5.1 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

2 
�

�

JU LY 11, 2013 I RE X V I - IR I S A G EI NG 3 

Guidance on ageing mitigation in biometric systems: We advance a set of considerations for mitigation of temporal 

effects in operational systems. Among these are that system owners should log all pertinent performance data 

- scores, qualities, timings - to support retroactive analysis. More directly, while ageing can often be mitigated 

by collection of a replacement enrollment sample, ee caution against doing this in an un-attended process. Sec. 

5.2 

Classification of biometric ageing effects: We propose a classification for effects leading to longitudinal variation 

in biometric accuracy. C L A S S A variations are caused by systematic effects that can be remediated by the 

system operator; these effects, e.g. failed illuminators in cameras, would typically affect many users. C L A S S B 

variations are subject-specific and can be remediated by modifying the behavior, skill, cooperation or physical 

condition of the subject. For example, a subject could be asked to open his eyes more widely. C L A S S C variations 

are subject-specific and cannot be remediated without a substantial design change to the system. An example 

would be to use shorter exposure times to mitigate motion symptoms of Parkinson’s disease. These changes 

are separate from the anatomical biometric information source itself. C L A S S D is similarly subject-specific and 

not easily remediated, and which are related to the biometric information source, in this case the iris texture. 

Variations of this type are irreversible and violate the permanence requirement for a biometric. This paper 

attempts to quantify the effects of C L A S S D changes on iris recognition. Sec. 5.3. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

3 
�

�

�

�

JU LY 11, 2013 I RE X V I - IR I S A G EI NG 4 

Acknowledgements 

The authors would like to thank the sponsors of this activity. These are the Criminal Justice Information
 

Systems division of the Federal Bureau of Investigation, the U.S. VISIT office in the Department of Homeland
 

Security (DHS), and the Science and Technology Directorate of DHS.
 

The NIST authors are especially indebted the Canada Border Services Agency (C B S A) whose invaluable dataset
 

was provided to NIST and comprises the main contribution of this report. Author Michael Chumakov is
 

employed by C B S A. Additional thanks go to those individuals with foresight enough to log the internal details
 

of the operational use of, by some measures, the world’s largest iris recognition deployment.
 

Our gratitude goes to James Wayman and Vince Stanford who reviewed this paper, to Kevin Bowyer of Notre
 

Dame for his encouragement in starting this work, and to Jonathan Phillips at NIST for describing and facilitating access to the Notre Dame datasets. The authors are grateful also to Dan Potter of Scitor Corporation for
 

perspectives and insights, and to Terry Waters for ophthalmological advice. Thanks go to John Daugman for
 

noting the existence of arcus senilis. Similarly to Svetlana Shchegrova of AOptix Inc. for candid discussions on
 

the topic. Thanks go to John Yap of the FDA for directing our attention to longitudinal analysis techniques.
 

The authors would also like to thank the United States Department of Defense for their support and collaboration.
 

Finally, the authors are grateful to the iris recognition vendors who provided their state-of-the-art recognition
 

engine prototypes for use in this effort. Use of high accuracy and operationally deployable algorithms is
 

essential to the relevance and robustness of the results.
 

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. 

Release Notes 

All IREX related reports, drafts, announcements and news items may be found on the homepage 

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

Appendices: This report is accompanied by a number of appendices which present exhaustive results on a peralgorithm basis. These are machine-generated and are included because the authors believe that visualization of 

such data is broadly informative and vital to understanding the context of the report. 

Typesetting: Virtually all of the tabulated content in this report was produced automatically. This involved 

the use of scripting tools to generate directly type-settable LATEX content. This improves timeliness, flexibility, 

maintainability, and reduces transcription errors. 

Graphics: Many of the Figures in this report were produced using Deepayan Sarkar ’s Lattice package[83] running under R, the capabilities of which extend beyond those evident in this document. 

Contact: Correspondence regarding this report should be directed to P G R O T H E R at N I S T dot G O V or J A M E S dot 

M AT E Y at N I S T dot G O V. 

Call for data: The authors are very enthusiastic to extend the ageing analyses presented in this paper, particularly 

by considering other datasets. We therefore explicitly request members of the community to make available 

images or recognition results from those images. N I S T would be a willing recipient for appropriate data. The 

academic community would offer considerable analytic insights if such data ere openly provided to them. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

4 
JU LY 11, 2013	 I RE X V I - IR I S A G EI NG 5 

The Iris Exchange (IREX) Program 

In 2008 N I S T established the I R E X program to give quantitative support to iris recognition standardization, development and 

deployment. The activities that have been conducted under I R E X so far are described below. All I R E X-related reports, drafts, 

announcements and news items may be found on the homepage http://iris.nist.gov/irex. 

C	 IREX I: The 2009 I R E X I evaluation, which tested the efficacy of leading commercial and university algorithms on the specialized 

image formats proposed for the ISO /I E C 19794-6[22] iris image data interchange standard. IR E X I also established viable limits for 

standardized image compression algorithms applied to iris images. Accuracy was measured over one-to-one comparisons. 

C	 IREX II: The 2010-2011 Iris Quality Calibration and Evaluation (I QC E), which assessed the capabilities of iris image quality assessment algorithms and supported the I S O/IEC 29794-6[64] iris image quality standard by establishing metrics, reference thresholds, 

and ranges for various appearance, geometric and photometric properties of iris images. Accuracy was measured using one-to-one 

comparisons operating separately from the image quality assessment algorithm. 

C	 IREX III: The 2011 I REX I I I activity executed the first public independent comparative evaluation of one-to-many iris recognition 

algorithms running on enrolled populations up to 3.9 million. It considered one- and two-eye recognition to validate results 

published in the academic literature that iris is a very powerful biometric. 

C	 IREX IV: The 2012-2013 I R E X IV activity, proposed as a direct follow-on to the I RE X I I I study, applies recent one-to-many recognition 

algorithms to uncompressed iris images. This work supports development of definitive JP EG 2000 compression profiles for iris 

identification. This extends the IR EX I work by considering the false positive demands of one-to-many, and by refining J PE G 2000’s 

parameters. The compression profiles will be contributed to formal iris image standards[22, 97]. 

C	 IREX V: The 2012-2013 I R E X V activity leverages lessons-learned in I R E X -I I /I I I in crafting best practice recommendations for avoiding the collection of poor iris images. These are primarily directed at operators of iris camera equipment. 

C	 IREX VI: Starting with this report, this activity considers any appropriate longitudinal aspects of iris recognition. 

C	 IREX VII: The forthcoming 2012 I R E X VII activity is dedicated to definition of abstraction layers around iris cameras and algorithms. IREX I 

Accuracy of 

compact iris 

images in support 

of the ISO/IEC 

19794-6 and 

ANSI/NIST Type 

17 standards 

IREX II / IQCE 

Image quality 

definition, 

evaluation, 

calibration 

supporting the 

ISO/IEC 29794-6 

standard 

IREX III 

One-to-many

Iris recognition - 

Single and twoeye accuracy and 

resource 

requirements 

IREX IV 

Minimum error 

(weighted FNIR + 

FPIR) one-tomany recognition; 

Rapid ID 

compression 

profiles 

IREX V 

Instructional and 

guidance 

materials for 

image collection 

IREX VI 

Temporal 

dependence,

 Iris aging, 

template aging, 

longitudinal 

effects. 

IREX VII 

Abstraction layers

Encapsulating 

core iris 

processing 

functions 

IREX (Iris Exchange)

NIST’s umbrella program for iris recognition performance, standards, and image-based interoperability

2008-2009 2010-2011 2011-2012 2012 2012-2013 2012-2013 2013- 

Test Test Test Test Best Practice 

Documents Test Best Practice Documents 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

5 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 6 

Contents 

EX E C U T I V E SUM MA RY 1
 

TE CH NICA L SU MM ARY 1
 

AC K NO WL E D G E ME N T S 4
 

DI S C L AIM E R 4
 

RE L E AS E NO TE S 4
 

TH E IREX PR O G R AM 5
 

1 INT R O D U C TIO N 8
 

1.1 DE FI N TI TIO N O F I R I S A G E I NG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
 

1.2 SOU RCE S O F L ON G I TU D IN A L C HA N GE I N B I O M E TR I C S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
 

1.3 OV ERV IE W OF P RIO R I R I S A G E I NG ST U D I E S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
 

2 EX P ER I M E NTA L D ATA SET S 14
 

2.1 BO R DE R C R OS S IN G D ATA O P S -XI NG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
 

2.2 NOT RE DA M E D ATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
 

2.3 FI E LD-C O L L ECT E D D ATA O P S -FIE L D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
 

3 LO NG I TU D I N A L VAR I ATIO N I N I R I S R E C OG NIT I ON 18
 

3.1 OV ERV IE W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
 

3.2 EFFEC T OF D I L AT I O N A N D D I L AT I ON -C H A NG E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
 

4 RE S ULT S 23
 

4.1 OV ERV IE W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
 

4.2 RE S U LT S F O R OPS-X IN G T R A NSA C T I O N S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
 

4.3 RE S U LT S F O R N D RE C OG N I TI O N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
 

4.4 RE S U LT S F O R OPS-FI ELD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
 

5 DI S C U SS I O N AN D C ON C L USIO NS 59
 

5.1 CO N S IDE RATIO NS F O R TE S T S OF BI O M E TR I C A GEIN G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
 

5.2 CO N S IDE RATIO NS F O R O PE RATIO NA L M IT I G AT I O N OF B I O M E TR I C A G EI NG . . . . . . . . . . . . . . . . . . . . . . . . . 60
 

5.3 HI E RA RCH Y O F LO NG I T U D I NA L E FF E C TS I N BI O M E TR I C S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
 

5.4 FUT U RE W O R K . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
 

List of Figures 

1 EX A MP LE TIM E S E R IE S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
 

2 EX A MP LE O F FA C E AG E I N G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
 

3 OPS-X IN G S U MM A RY S TAT I S T I C S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
 

4 HA M MIN G D ISTA N C E V S . T I M E , BY I N D I V IDU A L . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
 

5 HA M MIN G D ISTA N C E V S . T I M E , BY I N D I V IDU A L A ND D ATA S E T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
 

6 HD A S A F U NCT I ON O F S E A R C H A ND EN RO L LM E N T P U P I L D I L AT I ON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
 

7 HD A S F U NCT I ON O F RADIAL T H I C K NE S S C H AN G E, A N D DIL AT I ON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
 

8 OPS-X IN G TI ME EVO L UT I ON O F H D DI S TR I B UT IO N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
 

9 OPS-X IN G TI ME EVO L UT I ON O F PO P U LATIO N F N MR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
 

10 CO MPA R IN G L O N G V S . A L L OT HER HD S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
 

11 HA B I TUAT IO N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
 

12 OPS-X IN G G R A D I E NT P R E D I C TO R S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
 

13 N D FAL S E N O N -M AT C H C O NCE N TR AT I O N IN E Y ES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
 

14 N D 04-08 C O M PAR I S O N S C O R E D I S TR I B UT IO NS V S . T I ME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
 

15 N D 08-10 C O M PAR I S O N S C O R E D I S TR I B UT IO NS V S . T I ME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
 

16 N D 04-08 I N D I V I D U A L T I ME-S ER I E S IO2P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
 

17 N D 04-08 I N D I V I D U A L T I ME-S ER I E S D03P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

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JU LY 11, 2013 I RE X V I - IR I S A G EI NG 7 

18 N D 08-10 I N D I V I D U A L T I ME-S ER I E S I02P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
 

19 N D 08-10 I N D I V I D U A L T I ME-S ER I E S D03P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
 

20 N D 08-10 RAW FN M R V S. TH RE S H O L D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
 

21 CO VA RI ATE PR OG R ESS IO N I N ND08-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
 

22 N D 08-10 DILAT IO N V S . TI ME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
 

23 N D 04-08 DILAT IO N V S . TI ME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
 

24 IM A GE S FR O M N D 08-10 IN D I VIDUA L 05455L . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
 

25 EFFEC T OF P U P I L DILAT IO N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
 

26 IM A GE S FR O M N D 08-10 IN D I VIDUA L 05456L . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
 

27 N D 08-10 RAW A N D AD J US T E D F N MR VS. TH R E S HO LD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
 

28 N D 08-10 RAW A N D AD J US T E D F N MR VS. TH R E S HO LD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
 

29 N D 08-10 RAW A N D AD J US T E D F N MR VS. TH R E S HO LD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
 

30 N D 08-10 DILAT IO N, AR EA AD J U S TED TI M E S ER I E S I02P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
 

31 N D 08-10 DILAT IO N, AR EA AD J U S TED TI M E S ER I E S D03P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
 

32 N D 04-08 RAW A N D AD J US T E D F N MR VS. TH R E S HO LD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
 

33 TI ME EVO LUT I ON O F FALS E N E GATIV E I D E NT I FI C AT I ON R AT E O N T HE O P S -FI E L D PA RT I TI O N S . . . . . . . . . . . . . . . 57
 

34 TI ME EVO LUT I ON O F DISS IM IL A R IT Y O N T HE O P S -FI E L D PA RT I T I O N S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
 

List of Tables 

1 IREX VI DATASE T S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
 

2 OPS-X IN G S U MM A RY S TAT I S T I C S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
 

3 OPS-X IN G RE G R E S S I ON R ESU LT S B Y EY E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

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JU LY 11, 2013 I RE X V I - IR I S A G EI NG 8 

Figure 2: Face ageing: Images separated by 25 and 7 years respectively, show clear change in facial appearance with age. Reproduced 

with permission[47]. 

1 Introduction 

The I S O /I E C 23 82-37 vocabulary standard[4] requires that ‘repeatable biometric features” can be extracted from a “biometric characteristic”, reflecting the practical need for recognition accuracy to be maintained over useful time periods. 

Jain et al.[44] identified the more idealized property of permanence as a definitional property. In any case, the archetype 

here is the face image stored in identity documents (e.g. passports). Humans understand that faces are recognizable over 

useful time spans - passports are most commonly issued for 5 or 10 years - and that over extended periods, the face is 

subject to irreversible changes that render recognition more difficult - see Figure 2. As physiologically and biochemically 

active systems, all biometric traits are potentially time-dependent1 and this is a legitimate area for empirical study. Ageing 

itself may depend on known covariates such as age, gender, race, and on time-varying environmental factors. 

Longitudinal variation of biometric comparison scores is expected because capture of biometric traits is an analog to digital 

conversion process that is not exactly repeatable. Samples captured on any two occasions differ because of variations in 

how the human presents to the system, differences due to sensor noise, differences in the imaging environment, and any 

change in the expressed biometric trait itself. This last aspect includes temporary and permanent changes that can occur 

on timescales ranging from less that one second up to decades. 

The biometrics research literature is replete with studies where biometric data is collected from an individual on as few 

as two occasions. The first instance is regarded as an enrollment instance for incorporation into a gallery. The second 

instance, the probe, is compared with the first (gallery) instance to produce a comparison score. These are aggregated 

over a population to produce a statement of authentication accuracy of legitimate users2 . Figure 1 shows dissimilarity 

scores, in this case Hamming distances (H Ds), produced when a person’s enrollment sample is compared with samples 

collected over a period of about three years. The considerable H D variation includes random effects measurement error 

inherent in the non-repeatability of the biometric capture process, and the effects of any systematic changes to the biometric 

trait itself. The size of such source-specific variation has rarely been reported in the scientific literature because frequent 

data collection is expensive and difficult to sustain. Unquantified score variation is a problem in many biometric studies 

which rely on population averaging over uncorrelated random effects to allow the investigator to draw conclusions about 

whatever systematic change has been applied (e.g. to the sensor or algorithm). 

Ageing is primarily of interest because changing appearance eventually means that a person cannot be recognized against 

prior samples, i.e. false non-match. This effect is recognized in the I S O -I E C 19795-1 biometric testing standard [53] which 

says “Of particular importance when planning the test is the time interval between enrollment and the collection of verification or 

1As Benjamin Franklin noted, “When you’re finished changing, you’re finished”. 2When comparing samples, formal testing standards [53, 90] state accuracy as the false non-match rate (F N M R). It is computed from scores with loweris-more-similar semantics, e.g. Hamming distances, as the fraction of genuine comparison scores, sj , j = 1 . . . N, that are worse than (i.e. above) any 

threshold, τ. N

1 N F N M R(τ) = H(sj − τ) (1) N

j=1

where H is the Heaviside step function. F N M R is the complement of the empirical distribution function. Further, it is sometimes very useful to compute 

the image-specific false non-match rate iF N M R as the number of occasions a single image is involved in failed comparisons[88]. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

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identification data. Longer time intervals generally make it more difficult to match samples to templates due to the phenomenon 

known as template ageing”. The standard then defines “template-ageing” as an “increase in error rates caused by timerelated changes in the biometric pattern, its presentation, and the sensor ”. This definition in terms of recognition error 

rates rather than the core comparison scores reflects the operational relevance of error rates. 

The standard notes that for “some modalities, performance a short time after enrollment, when the user appearance and behaviour 

has changed very little, is far better than that obtained weeks or months later”. The I S O standard then requires “that genuine 

transaction data shall therefore be separated in time from enrollment by an interval commensurate with the target application” ... ”at least by the general time of healing of the body part”. A standardized test of access control systems[52] 

requires enrollment and authentication samples to be separated in time by a minimum of 7 days3 . 

Jain[44] also identified ubiquity and uniqueness as required properties of a biometric trait. Together these allow (most) 

members of a population to be accurately differentiated from all others, i.e. that false matches occur with calibrated 

rarity. In ageing studies, false match rate has been of secondary interest because it is defined over a population - i.e. the 

comparison of images from a single source against those of N people of generally different age. However, it is possible 

that as an individual ages, their likelihood of matching other individuals will change. This possibility appears in face 

recognition where younger persons are more likely to false match than older ones. The iris ageing literature has noted 

stability of the impostor distribution [8, 28, 29]. For these reasons this paper, rather unusually in biometrics, does not cite 

false match rates (F M R). This reflects the narrow focus of this paper to same-person effects. 

1.1 Defintition of iris ageing 

This report operates under the following definition. 

iris ageing 

irreversible changes to the healthy iris or neighboring anatomy that yield mated dissimilarity scores that increase monotonically with time-separation of the compared images 

N O T E 1: The restriction to healthy individuals is practical. It is made because while injury and disease can lead to arbitrarily 

rapid and serious changes4 

, its prevalence and incidence are low or confined to certain small sub-populations5. The operational 

utility of all biometric characteristic rest on their stability in healthy individuals. 

C	 N OT E 2: The monotonic qualifier is included to separate temporary and reversible changes from permanent and irreversible ones. 

Reversible changes include dilation of constricted pupils (e.g. by reducing light), or drooping of eyelids. Irreversible changes 

would include those to the arrangement of stroma in the iris, a change to the shape of the cornea, or a change in the limbus (as 

discussed in the next section). 

C	 N OT E 3: The inclusion of the phrase “or neighboring anatomy” acknowledges that changes to the limbus might impede segmentation, or that corneal shape changes can alter iris appearance via optical refraction. 

C	 N OT E 4: The scores “increase” because changes to the iris are expected to lead to reduced similarity (because of non-negativity of 

distance metrics). 

C	 N OT E 5: This definition implies the use of a recognition algorithm. This is done because recognition outcomes are what matter 

operationally. The algorithm here is assumed not to change because it is assumed that, given sufficient understanding of ageing 

processes, algorithm developers could mitigate their effects. 

C	 N OT E 6: Aspects of this definition may be applicable to other biometric modes. 

3The standard, for its purposes, also imposes a maximum of 90 days for revisit transaction. 4For iris, see for example results pre- and post cataract-surgery[23], and diseases affecting segmentation[58, 92]. Disease affects other modalities too: For 

face, Bells palsy[65]; For fingerprints, palmar eczema, dishidrosis, amputation, arthritis, and acute damage due to manual labor[26]. 5For example, pterygium, an iris-occluding growth of blood vessels across the iris, has higher prevalence in agricultural workers[89]. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

9 
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The assumption that iris ageing processes will give steadily increasing scores implies that it will not immediately yield 

outright false non-match failure. This assumption has correctly motivated ageing studies to remove poor images from their 

analyses[8]. Accordingly, ageing studies should include tight enough capture controls that outright recognition failures 

should not occur, or, secondarily, that failures should be discarded entirely during the analysis. Here a failure should be 

suspected if the genuine score is high e.g. comparable with an impostor score. 

1.2 Sources of longitudinal change in biometrics 

Biometric comparison scores vary systematically and stochastically. Iris scores vary with at least the following. 

Sensor changes: Sequential captures of an inanimate test target will yield images that differ due to read- and shotnoise effects in the sensor electronics[56]. In most situations, these will usually have little effect on iris recognition 

performance. If, however, a camera’s LED illuminators degrade[72, 73], recognition can obviously be impeded. 

Environmental changes: Changes in either the ambient or infrared illumination incident on the eye, or in the light 

reflected toward the sensor (e.g. due to atmospheric effects), will yield differences in the captured images. Sensors 

running with a automatic gain control alter their response depending on incident illumination. 

Behavioral changes: It is well known that individuals using a biometric system on a regular basis become acclimated 

to it. This is most often known as habituation[51] and is known to be more prominent if feedback is provided e.g. 

as a yes/no decision. The O P S -X I N G data used in this report reveals evidence of habituation - the persons who use 

the system most frequently produce lower (i.e. more similar) average comparison scores - see the discussion later 

around Figure 11. 

Changes in the eye itself: In healthy individuals the following are either known, or understood to be, influential on 

iris recognition over various time scales. 

–	 Pupil size: Pupil size varies over timescales below one second up to several decades. The Hippus phenomenon, 

irregular onset oscillations at 0.05 to 0.3 Hz[11] with amplitude around 1mm, has unknown cause and can be 

chaotic[80]. Size is influenced by cognitive effort[66] and old/new memory recall[38]. Fatigue causes modulation of pupil size[95], cycling with periods above 5 seconds with amplitude 0 to 1mm[60]. On longer timescales, 

pupil dilation is affected by many pharmacologic agents, beyond those used in routine ophthamology. Particularly, stimulants and sedatives act to broadly increase and decrease pupil size respectively[82] but the physiological mechanism matters: drugs acting on the parasympathetic and sympathetic nervous systems differentially innervate the iris sphincter and pupillary dilator muscles respectively[41]

6 . Smoking gives an acute 

increase in pupil size by suppressing parasympathetic activity[61], but not on longer timescales[86]. Over variable timescales, pupil dilation changes with environmental illumination levels, such as those in the workplace, 

at home, and places in between, perhaps on a diurnal timescale. Over very long timescales pupil size decreases 

in healthy adults by about 0.4mm per decade[94, 13]

7 due to fibrosis and an increase in rigidity of the sphincter muscle, and not due to a change in retinal sensitivity[17]. While iris recognition algorithms are designed 

6Disruption of the parasympathetic, or stimulation of the sympathetic, nervous systems produce the same effect, mydriasis (pupil dilation), because the 

iris sphincter muscle acts to constrict the pupil, the dilator muscle to dilate it. 

7This is a dark-adapted value in which the iris is radially narrow. Over eight decades and 263 individuals, the study [13] reports that for subjects 18 to 

19 years (n=6), the mean dark-adapted pupil diameter was 6.85 mm (range: 5.6 to 7.5 mm); 20 to 29 years (n=66), 7.33 mm (range: 5.7 to 8.8 mm); 30 to 

39 years (n=50), 6.64 mm (range: 5.3 to 8.7 mm); 40 to 49 years (n=51), 6.15 mm (range: 4.5 to 8.2 mm); 50 to 59 years (n=50), 5.77 mm (range: 4.4 to 7.2 

mm); 60 to 69 years (n=30), 5.58 mm (range: 3.5 to 7.5 mm); 70 to 79 years (n=6), 5.17 mm (range: 4.6 to 6.0 mm); and 80 years (n=4), 4.85 mm (range: 

4.1 to 5.3 mm). This is a reduction of about one third over 6 decades. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

10 
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to have some invariance to pupil dilation[19], the various technical approaches rely on models of iris texture 

change and these, empirically, do not remove all the dependence of iris comparison scores on the dilation 

present in the images[36, 23, 40, 87, 34] - see section 3.2 for a large-population result. 

Under our section 1.1 definition of iris-ageing, we do not regard pupil dilation as a component because much 

of its variation is stochastic. The decades-long constriction process would be a source of iris ageing but its 

effects a) may nevertheless be small8, b) are tolerated by current algorithms, and c) compensated for by certain 

cameras. As a slow process, it will have had negligible influence on published studies. 

Nevertheless short term pupil dilation effects are influential on iris recognition, and should be compensated for 

in iris ageing studies. 

–	 Eyelid occlusion: Eyelid occlusion degrades iris recognition accuracy[87] because segmentation is impeded, 

and information available from the iris increases non-linearly with the height of the palpebrae fissure (P F), the 

distance from the upper to lower eyelid margins, normally 9 to 12 mm. The eyelid position is under voluntary and autonomic control via the tarsal muscles[82]. It is most obviously manifest on millisecond timescales 

as blinking, but on longer timescales, fatigue and pharmaceuticals consumption are influential. The medical 

condition ptosis is characterized by drooping of the upper eyelid as far as the pupil. Ptosis occurs in later 

years. Head position also affects eyelid position - down gaze angles to 40 degrees produce ΔP F= −0.024 mm 

deg−1[37] or higher[78]. 

–	 Corneal shape: The cornea covers the iris and its optical refractive properties mean that changes in shape may 

alter an iris image. Shape changes do occur during accommodation[69], with head position[49], with surgery, 

and over decades[48]. The effects on iris recognition have not been quantified although some modeling has 

been done[71, 46]. 

–	 Arcus senilis: This condition[15] manifests as an opaque white to gray colored ring at the periphery of the 

cornea. It is caused by deposits of cholesterol in the cornea or hyaline degeneration. It occurs in older persons. 

Albdedo changes could impede accurate segmentation of the outer boundary[18]. 

1.3 Overview of prior iris ageing studies 

The core issue of whether the iris texture9 ages has been the subject of the following prior studies. These aim to quantify 

iris ageing and are motivated to check the Daugman patent assertion[21] that iris texture is “essentially immutable over a 

person’s life”. 

Baker et al. used 6797 images of 23 persons to report an adverse, statistically significant, increase in F N M R from 

comparisons of images collected more than 1200 days apart vs. those from short term collections fewer than 120 

days apart[8]. This shift in the genuine distribution was noted “across a broad range of threshold values”. 

Notably the researchers manually screened all images for poor quality, excluding those with “out-of-focus irises, 

major portions of the iris occluded, obvious interlace artifacts etc”. This step was necessary because the LG2200 

camera had been modified to produce images that would normally have been subject to “built-in quality control 

checks”. Additionally images for which the IrisBEE algorithm gave a “noticeably poor ” segmentation were also 

excluded. 

8 For example, if a person with an 11mm diameter iris is enrolled at age 20 with pupil diameter 7mm, and this reduces to 5mm at age 70, this would 

correspond to ΔD = 1 − (1 − 0.64)/(1 − 0.45) = 0.33. By looking at Figure 7, or by drawing vertical lines on Figure 6, we estimate Hamming distance 

changes ΔH D< 0.1. This would be tolerable if the initial enrollment was free of image quality problems. 9The term texture is used in this document to refer to the visible parts of the iris notably the collarette, Fuch’s crypts, base crypts, the stroma, and 

Schwalbe’s contraction and radial folds, and the pupillary ruff. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

11 
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The study dismissed pupil dilation as responsible for the ageing effect based on a small rank correlation of average 

dilation difference and average dissimilarity scores. The study also dismissed exposed iris area as causal concluding 

that “there is no substantial correlation between the number of non-occluded bits and elapsed time” and that “change 

in the amount of iris occluded does not account for the increase in false reject rate”. It is not clear whether the 

correlation was against absolute time, or time between captures i.e. whether the analysis was done for single images, 

or pairs. The correlation of the number of bits with comparison score was not reported. The team first reported on 

ageing in 2009[7] using a smaller population, and a non-commercial recognition algorithm. 

Fenker et al. reported that recognition error rates increase by 153% over a three year period, and by 82% over a two 

year period. 10. These results were the primary motivators of this N I S T study. A number of media stories11 included 

this figure without noting that this result was the ratio of two numbers (short term F N M R of 0.096 (i.e. nearly 10%) 

and long term F N M R of 0.243, i.e. nearly 25%). This 153% increase in F N M R is noisy. The application of a bootstrap 

uncertainty estimation procedure gave an interval of [80%,307%] around this estimate but it was implemented to 

first sample subjects (with replacement) and then scores - it did not additionally sample images as indicated in 

standards[53]. This would have been reasonable if subjects were hypothesized to cause recognition failure, but not 

reasonable if particular images were responsible. 

This study[29] made no attempt to compensate for effects of pupil dilation. That aspect had been addressed in a 

prior study[28] which used images in 2008-2010. The study found significant12 ageing effects which remained even 

after measures were taken to remove the effects of dilation change. This issue is discussed at length in section 4.3.1. 

Tome-Gonzalez et al. used 8128 images of 254 individuals from the BioSecurID database to investigate ageing[91]. 

The data was collected in four sessions “separated typically by one to four weeks” to show that inter-collectionsession false non-match rates were higher than intra-session rates. However: a) The recognition error rates are 

very high (F N M R ≈ 0.1 intra-session and F N M R ≈ 0.25 inter-session, at F M R = 0.01) despite culling 1905 incorrectly segmented images. High F N M R is indicative of poor images or a poor algorithm relative to the commercial 

mainstream[34]. b) The dependence on time is not monotonic. The authors note that “once a minimum time between 

samples has passed, error rates are not apparently increased” noting that inter-session 1-3 is worse than 1-4 which 

may be due to statistical significance problems. c) The authors do not discuss the high session 1 pupil sizes that are 

evident in Figure 9 in [91] as being influential. d) The total duration of the study is so short (16 weeks) that any 

anatomical ageing effect would be smaller than in other studies. 

Sazanova et al. used 7628 images of 244 subjects (46 over more than one year) to produce formal rate-of-change-ofcomparison-score statements of iris ageing for the Masek[55] and Neurotechnology algorithms[84]. They acknowledged the stochastic time-varying nature of contrast, occlusion, illumination and blur as factors that can undermine 

10 This results appear in Table 2 of [29] for the period 2008-2011. The number for the 2008-2010 interval is 82%. N I S T was unable to conduct studies 

with the 2011 images, pending legal issues. The N D results were obtained with the commercial Neurotechnology recognition engine. The company has 

submitted competitive recognition prototypes to the N I S T I R E X -I I I and I R E X -I V evaluations. The threshold was set to 580 per a false match calibration of 

1 in 2 million. This threshold corresponds to a threshold of 0.0017 on the B02P, as the developer uses a 1/x mechanism to convert between dissimilarity 

and similarity scores. For the 2008-2011 set, F N M R increased from 0.096 to 0.243 corresponding to a mean percent increase of 153%, with confidence 

interval of [85, 307]%. This corresponds to 0.096x5244 = 503 within semester errors, and 0.243 x 20888 = 5076 in Spring 08 to Spring 11 comparisons. 

This involves 32 people and 2338 images. 

11 These papers have been widely, and with less than complete accuracy, quoted in the press with titles, Ageing Eyes Hinder Biometric Scans[33], 

Accuracy of Iris Recognition Systems Degrades over Time[59], Iris aging raises issues about recognition accuracy[96], Aging process confounds iris 

recognition biometrics[2], Future Eye Scanners Must Combat Aging Eyes[24], Researchers question long-term reliability of iris recognition[3, 12], Aged 

eyes prevent iris recognition[1], Researchers question long-term reliability of iris recognition[3], and Ageing irises could confound biometric checks[32]. 

Inevitably this meme has propagated internationally to government and other procurement officials tasked with determining whether iris is a viable 

biometric. 

12This and other studies use full cross-comparison of images. While this gives the best estimate of the mean, the scores are not independent such that 

uncertainty estimates are governed more closely by the number of images O(N) rather than scores O(N2). This issue can lead to optimistic uncertainty 

estimates. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

12 
�

�

�

JU LY 11, 2013 I RE X V I - IR I S A G EI NG 13 

recognition and used a robust linear regression to estimate coefficients for these and for time between captures. This 

regression was conducted once, over the entire imbalanced population - it did not account for correlated observations, nor inter-person heterogeneity. Notably the study did not include dilation in the regression, and it proposed 

to model this and other (unspecified) “changes in data acquisition procedure” in future work. The work quantified 

false rejection rates over time at fixed false match rates (F M R), rather than fixed threshold. This only matters to the 

extent that F M R is invariant with ageing and that the use of the F M R = 0 operating point - an extreme value - is 

repeatable across partitions. The publication does include time-series - but not for specific individuals - and these 

reveal noise (i.e. variation in comparison scores) far in excess of the computed age-related change. 

Fairhurst et al. used images of 632 images of 79 users (158 eyes) with a Masek[55] implementation modified to 

reduce segmentation errors[27]. The authors note the role of dilation, the age-related effect of ambient illumination 

on dilation, and conclude that “dilation decreases with age and consequently Hamming distances decrease”. They 

note the inconsistency of Baker ’s[8] result, that dilation change is not responsible for the noted ageing effect, with 

other Notre Dame research that dilation change does change recognition scores[40]. They “affirm the importance of 

a reliable and robust segmentation” in iris recognition and conclude that “after eliminating this segmentation factor ” 

that “physical ageing effects ... are primarily the result of physiology of pupil dilation mechanisms”. 

Rankin et al. exposed 456 visible light images from 76 subjects imaged on 3 occasions each 3 months apart to several 

variants of their own iris recognition algorithms to conclude that ageing does occur. Their paper bins genuine H D 

scores in the intervals [0,0.09), [0.09,0.19), [0.19,0.29), [0.29,0.39), [0.39,0.49), and adopts a threshold of H D ≥ 0.39 to 

declare false rejection. The paper notes lower F N M R at time lapses of 6 months than at 3 months (Table 1, F N M R(3) 

= 0.212 vs. F N M R(6) = 0.205), and that scores (Table 2) shift lower (better) after 6 months vs. 3. These error rates are 

much higher than those published in large scale tests[34] at such thresholds either because the images are visiblelight or because the algorithm is poor. Daugman asserted as much[20] suspecting the existence of segmentation 

errors. This was denied[77] by the study authors13 . 

Czajka reports results for 571 images of 58 eyes imaged on two or more occasions ranging from 30 days up to 2960 

days. They applied three recognition algorithms, two commercial, and one self-developed. Noting zero recognition 

errors from one algorithm, the paper notes the explicit time-series nature of ageing, suggesting that studies of biometric ageing should address the stationarity of the dissimilarity scores. This view requires all statistical moments 

of the score distribution to be stable rather than just whatever summary statistic is used in routine performance assessment. The outcome is a 14% deterioration in the mean of 2432 Neurotechnology comparison scores in the 5 to 9 

year period vs. the 1588 scores in the up-to-2-year period. This is consistent with ageing. While the paper establishes 

strong significance via a t-test, it ignores correlation of asymmetric match scores, and of scores from cross-comparison 

of images. The study observed that more accurate algorithms are more susceptible to ageing. Czajka uses this observation to claim the existence of iris texture ageing effects because a) better segmenters give better alignment of iris 

regions being compared, and b) better algorithms have higher sensitivity to individual features of the iris texture. 

The study suggests that its finding of iris ageing should be confirmed by exploiting “big, heterogeneous datasets”. 

13 While all ageing studies have acknowledged that correct segmentation is critical, there may be definitional differences of what is “correct”. One step 

is to visually check that the detected boundaries (e.g. circles) align with the limbal and pupillary boundaries. However this should be done for pairs 

of images to ensure consistency because it is known that polar representations of iris features are sensitive to the precise location of, particularly, the 

pupil boundary. One study[57] showed fractional H D values increase by 0.023 per linear pixel misplacement of the pupil. Thus a pair of images with 

fine differences in boundaries will produce degraded similarity scores. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

13 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 14 

A B C D 

Quantity ND04-08 ND08-10 OPS-XING OPS-FIELD 

Source type Image matching Image matching Archived logs Image matching 

Computation 2012 2012 2003-2012 2012 

Num rec. algs 9 8 1 5 

Source algs ICE/IREX-IV/VI IREX-IV/VI Iridian[19] IREX-IV 

How Genuines 

Calc. 

1:1 Cross-Comp 1:1 Cross-Comp 1:N Fixed-enrol 1:N Cross-Comp 

Capture Dates 2004-2008 2008-2010 2003-2012 2004-2012 

Capture Setting Univ. lab Univ. lab Airports Field 

Num images 6802 11776 1042948 3544068 

Num eyes 46 434 521474 NA 

Num people 23 217 350566 622464 

Num people 1YR 23 199 214731 286387 

Num people 2YR 23 36 146104 138064 

Num people 3YR 23 0 96588 64554 

Num people 4YR 18 0 53880 21238 

Num genuine 1321236 356022 5710434 2301246 

Num genuine 1YR 350622 48136 3749774 1049422 

Num genuine 2YR 253112 3750 2515911 491246 

Num genuine 3YR 165314 0 1597711 210564 

Num genuine 4YR 46520 0 896368 59266 

Citations [8, 7] [28, 29] [34, 74] 

Table 1: Data: Summaries of the four I RE X V I data sets used in this paper. 

2 Experimental datasets 

The four datasets used in this study are summarized in Table 1, and described in the sections below. 

Table 1 summarizes the properties of the datasets. Column A represents the application of 2012 commercial recognition 

algorithms to the images collected by Baker et al. and a re-analysis of the scores from the CAM-2 algorithm that was used 

in their study. Columns B and D summarize the application of the new algorithms to different sets of images. Column C 

refers to textual data from the logs of operational systems. 

The various datasets are differentiated by the number of subjects, the frequency and regularity of image collection, the 

duration of collection (in years), whether or not the recognition algorithms were applied retro-actively to generate all 

possible genuine scores (columns A, B, D) or only a single score against an enrollment sample during collection (C). The 

details appear below. 

Note that one of the datasets is comprised only of text log files, instead of images most often used in biometrics research. 

Note that all of the data sets were collected without inquiries or tests of general or ophthalmological health. 

2.1 Border crossing data O P S -X I N G 

O P S -X I N G is the set of time-stamped genuine scores recorded in the logs of an operational border crossing system[5]. 

The scores come from the Iridian implementation of the Daugman algorithm[19]. The scores are Hamming distances 

(H Ds) from a 1:N search of a live image against the enrolled database. Neither the enrollment nor recognition images 

were provided to N I S T. This precluded re-matching with contemporary algorithms. The camera used in border crossing 

transactions was a Panasonic BM-ET 330 which, at the time it was deployed in 2007, was a leading iris camera. It remains 

in effective use today. Enrollment images were collected from 2003 to mid 2007 using an LG 2200 camera, and, from 

mid 2007 onwards using the Panasonic camera. The LG 2200 camera is identified in this report by the letter “L” in the 

report. The Panasonic cameras carries the letter “B”. Neither camera is marketed commercially today. Border crossing 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

14 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 15 

Enrol to First Capture (Days) Enrol to Last Capture (Days) Active: First to Last Capture (Days)

Number of Individuals (out of 349995 total)

0 20000 40000 60000

0 1000 2000 3000

tmin

0 20000 40000 60000

0 1000 2000 3000

tmax

0 20000 40000 60000

0 1000 2000 3000

tspan

Num. Recognition Events

Num.Individuals (out of 349995 total)

0.2

0.4

0.6

0.8

1.0

 1 2 5 10 20 50 100 200 500 1000

N(20) = 0.79

N(80) = 0.96

N(320) = 1.00

Figure 3: Population sizes: For dataset O P S -XI NG, the three panels show counts of individuals by the time elapsed from their enrollment 

to first capture (Tmin), time from enrollment to last capture (Tmax), and the duration of the active period (TA = Tmax − Tmin). The 

spikes at zero days are due to a training passage that each enrollee performs shortly after iris enrolment. The center panel, Tmax, is 

most germane to ageing studies. The rightmost graph shows the empirical distribution function of the number of recognition events 

per individual. 

transactions were logged from November 2007 onwards. 

Table 1 includes a summary of the O P S -X I N G collection. The data is advantaged by the size of its population, its extent (up 

to nine years), its controlled nature - fixed installations, a single and capable recognition algorithm that did not change, 

and only two camera models are used and logged. The population is a set of frequent travelers who paid money to 

enroll (currently $50), and who execute multiple transactions at irregular times with differing frequencies. Figures 3 show 

transaction volume statistics. The system remains operational and in heavy use. 

The logs for this data include iris and pupil radius estimates from which dilation can be computed - see section 3.2. The 

authors discarded some data using the following criteria. First enrolled persons without recognition transactions were not 

analyzed - and their numbers are not reported here. Second, individual eyes for which only one recognition transaction 

was logged were discarded because of their limited usefulness in analysis. Similarly individual eyes for which transactions 

only occurred on one day were discarded. Finally, eyes for which the maximum time between enrollment and recognition 

was less than one day were ignored. 

2.1.1 Camera interoperability 

Each transaction is accompanied by an L or B label indicating the camera used for initial enrollment. All recognition 

images were collected with instances of camera B, and thus any given comparison involves (L,B) or (B,B) camera pairs. 

As shown later, (L,B) Hamming distances are higher than (B,B). This systematic effect is due to imaging-system differences, particularly with respect to wavelength of the infra-red illuminant14 the angle of incident infrared light due to light 

emitting diode (L E D) placement, and the difference in the way subjects interact with the single-eye “L” and dual-eye “B” 

cameras. Particularly, when cmaera B was adopted for recognition transactions, and logging commenced, the L-enrollees 

had to switch to using camera B without any prior experience. Those enrolled on camera B were specifically trained. 

14Images of irises collected at different wavelengths have different appearance[42, 81] and this may be influential on feature extraction algorithms. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

15 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 16 

Elapsed Time (Days)

Dis−similarity

0.05

0.10

0.15

0.20

0.25

0.30

0 500 1000 1500

a=0.08 b=−6.9e−06 R2= 0.01

105867L

a=0.05 b=9.4e−07 R2= 0.00

109280L

0 500 1000 1500

a=0.09 b=2.7e−05 R2= 0.07

113405L

a=0.02 b=3.0e−05 R2= 0.06

125915L

a=0.13 b=1.1e−05 R2= 0.02

127483L

a=0.24 b=−3.9e−05 R2= 0.26

129535L

a=0.12 b=−1.1e−05 R2= 0.02

129603L

a=0.21 b=−3.0e−05 R2= 0.07

130016L

0.05

0.10

0.15

0.20

0.25

0.30 a=0.02 b=−1.3e−05 R2= 0.05 133405L

a=0.14 b=6.1e−05 R2= 0.28

133979L

a=0.08 b=8.8e−06 R2= 0.01

134374L

a=0.12 b=−1.8e−05 R2= 0.05

136186L

a=0.06 b=−3.0e−08 R2= 0.00

136950L

a=0.17 b=−1.7e−05 R2= 0.02

138274L

a=0.20 b=1.6e−07 R2= 0.00

141923L

a=0.07 b=−2.2e−05 R2= 0.10

72368L

0.05

0.10

0.15

0.20

0.25

0.30 a=0.09 b=2.4e−05 R2= 0.04 78022L

a=0.11 b=3.3e−05 R2= 0.12

80230L

a=0.09 b=3.9e−06 R2= 0.00

80915L

a=0.18 b=−1.1e−05 R2= 0.02

84492L

a=0.08 b=8.9e−06 R2= 0.01

84965L

a=0.06 b=2.7e−05 R2= 0.13

86344L

a=0.15 b=−3.3e−06 R2= 0.00

93965L

a=0.18 b=3.9e−05 R2= 0.14

94537L

(a) Enrollment Camera B 

Elapsed Time (Days)

Dis−similarity

0.05

0.10

0.15

0.20

0.25

0.30

1000 2000 3000

a=0.21 b=−1.5e−06 R2= 0.00

101365L

a=0.21 b=−2.4e−05 R2= 0.11

104804L

1000 2000 3000

a=0.20 b=−1.8e−05 R2= 0.03

106945L

a=0.16 b=5.1e−06 R2= 0.00

110487L

a=0.14 b=8.2e−06 R2= 0.02

114859L

a=0.22 b=3.4e−07 R2= 0.00

116002L

a=0.20 b=−1.5e−05 R2= 0.03

116687L

a=0.16 b=1.0e−05 R2= 0.02

117435L

0.05

0.10

0.15

0.20

0.25

0.30 a=0.21 b=−1.2e−06 R2= 0.00 123184L

a=0.17 b=−1.3e−05 R2= 0.03

124052L

a=0.18 b=−4.9e−07 R2= 0.00

58360L

a=0.17 b=5.9e−06 R2= 0.01

59376L

a=0.22 b=−1.4e−05 R2= 0.06

59905L

a=0.19 b=−3.1e−06 R2= 0.00

72649L

a=0.15 b=5.3e−06 R2= 0.00

80139L

a=0.13 b=−4.9e−06 R2= 0.01

81653L

0.05

0.10

0.15

0.20

0.25

0.30 a=0.14 b=1.5e−06 R2= 0.00 81917L

a=0.26 b=−4.0e−06 R2= 0.02

82062L

a=0.18 b=−1.0e−05 R2= 0.04

82900L

a=0.18 b=2.5e−05 R2= 0.17

88411L

a=0.13 b=−6.0e−07 R2= 0.00

93613L

a=0.09 b=6.6e−06 R2= 0.01

96893L

a=0.22 b=7.3e−06 R2= 0.02

97247L

(b) Enrollment Camera L 

Figure 4: Score trajectories: For dataset OP S-XIN G, the panels show Hamming distance scores for 24 randomly selected individuals 

executing identification attempts at least 60 times in five years. The horz. axis is time since enrollment. The gold line indicates the 

0.27 threshold of section 2.1.2. The blue dots indicate a single logged score and the grey text gives the intercept, gradient and R2 of 

the linear regression line shown in red. The regression is computed with time as the only covariate (dilation is not included). Note the 

varying intercepts, the intra-person and inter-person variance, negative gradients, and cases where positive gradients are nevertheless 

accompanied by low H Ds at the end of each period. Logging was enabled in 2007, so persons enrolled previously on camera L have no 

score data initially. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first
 

ND = Uni. of Notre Dame
 B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

16 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 17 

2.1.2 The use of thresholded data 

Subjects in the O P S -X I N G application are invested in an immediate (successful) outcome (i.e. border crossing). They make 

attempts until successfully recognized, or a timeout occurs. This photo-and-match approach yields a corpus of images that 

are matchable essentially by-definition, and in this case the system logs only contain scores from successful comparisons, 

at or below a dissimilarity threshold15. Rejections, false or otherwise, do not appear in the logs and their absence imparts 

a censoring of the right tail of the underlying score distribution. However, the data points that would be present in 

that tail, had they been logged, would typically occur because a camera’s quality assessment apparatus allowed a poor 

quality image (e.g of a closed blinking eye, or of an off-angle gaze) to pass to the recognition algorithm. This assertion 

ignores the possibility that an underlying ageing effect causes false rejections. However, assuming such ageing would be 

permanent, the individual would then have persistent rejections such that low Hamming distances would not be observed 

subsequently - this is often not the case. 

This operational system reduced the threshold as enrolled population size increased in order to maintain a low calibrated 

false match rate. This complicates our analysis because the distribution is truncated differently earlier in the period. C B S A 

confirmed the use of a threshold of 0.2797 in the earliest events (in 2007), reducing to 0.2704 at the end of the period (in 

2012). We therefore re-thresholded the data removing all points with Hamming distances above 0.27. The original logs 

contain 5710434 entries from 521474 eyes of 350566 people. The post-thresholded logs contain 5667363 entries from 519945 

eyes of 349995 people. The rethresholding affects 28900 eyes of 25610 individuals, with 1529 eyes of 571 people being 

dropped from the analysis entirely. Approximately two thirds of these were enrolled with camera L. The mean number of 

scores removed is 1.49 per affected eye. The re-thresholding establishes a time-independent censoring of the right tail of 

the genuine distribution, and this supports unbiased detection of trends in the data. Without this step, some transactions 

logged early in the period (e.g. 2007) would be expected in the interval [0.27,0.28] and these would not be present in more 

recent events. Note that most users’ H Ds are clustered well below 0.27 (see Figure 4 and Table 2). 

The absence of the right-tail of the genuine distribution does not indict this dataset for detection of iris texture ageing if 

we assume that there are no individuals who age so rapidly that their H Ds degrade and they can no longer be recognized. 

Our assumption is supported by noting that while some individuals have positive gradients they do not quickly approach 

the threshold. Note that in Figure 4 eye 133979L does increase over a 1500 day span, and the last transaction is high. This 

could be the result of the C L A S S B , C or D ageing processes defined later in section 5.3, or due to progressive ill health. 

2.1.3 Summary 

In conclusion, we assert that operational logs of successful recognition attempts are an invaluable resource, not least 

because of their large size. The use of previously-matched images in an ageing study is not biased toward a no-ageing 

result because the physical manifestation of iris-ageing is not (at least immediately) the occurrence of poor genuine scores 

in the tail of the genuine distribution (i.e. ones that would lead to outright recognition failure), but rather the increase in 

the broad dissimilarity distribution. 

However, by analyzing logs of successful transactions, defective images are explicitly omitted. This is not the case later in 

this report (Figure 16), where above threshold scores do occur and the genuine distribution is bimodal - this is characteristic 

of recognition (particularly segmentation) failure. We recognize that complete logs that include failed recognition attempts 

offer extended possibilities for other analyses. 

15One-to-many identification algorithms can realize speed gains by short-circuting a distance calculation when a partial match is already above a distance 

threshold 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

17 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 18 

2.2 Notre Dame data 

The University of Notre Dame has collected multimodal biometric data at least over the interval 2004 through 2011. The 

iris images of two partitions were made available to the authors. Sizes of these sets appear in Table 1. 

The first partition, N D04-08, was used in template ageing studies reported by Baker et al.[7, 8]. The images were collected 

with a modified LG2200 camera that is no longer marketed16 . Importantly the images that “did not pass the normal 

built-in quality control checks” of the camera were excluded because they degraded recognition accuracy[68]. For the 

ageing studies, Notre Dame manually screened these images, correctly excluding those with “noticeably poor quality” 

particularly those with an “out-of-focus iris, major portions of the iris occluded, interlace artifacts etc” 17 . The result is a 

set of 6802 images from the approx. 60000 parent set[68]. 

The second partition, N D08-10, was used in template ageing studies reported by Fenker et al.[28, 29]. Collected with a more 

modern camera, the LG400018, it has a larger population than the N D04-08 set, but shorter temporal extent. This dataset is 

the focus of the N I S T work reported in this paper because it was widely cited as giving rise to serious concerns regarding 

ageing effects [33]. 

These datasets are available to researchers. As such, the algorithms used later in this report could have been developed to 

process these images with better than normal capability. 

2.3 Field-collected data O P S -FI E LD 

The O P S -FI E L D set is the largest in terms of subjects and numbers of images, but not number of genuine comparisons. The 

set has two partitions. The first is one termed D E TA I N E E corresponds to 390,119 persons interdicted in military encounters 

- these subjects are imaged very few times on an irregular schedule. They produce 1,199,052 genuine scores. The second 

is labelled PA C S and is comprised of enrollment images of 232,345 persons who subsequently use a physical access control 

system. These people were re-enrolled on a regular schedule so there are multiple images per person. This partition yields 

1,102,194 genuine scores. 

The available images were matched using several very recent recognition algorithms. These are the latest commercial 

competitors submitted to the I R E X I V evaluation. The genuine scores are extracted from the L = 20 candidates returned 

from 1:N searches. The images were collected using several cameras, and several versions of each of those. The Securimetrics/L1 H I I D E and P I E R and Crossmatch iS C A N/S E E K cameras comprise most of the collection. A number of other cameras 

are used, some of which are unknown. 

3 Longitudinal variation in iris recognition 

This section commences with an overview of the problem, showing how measured biometric outcomes can vary. This is 

followed by a discussion of sources of change in iris recognition. This supports a proposed hierarchy of ageing types. The 

final section is dedicated to pupil dilation. 

16The modification was a bypass of the internal image quality checks.
 17These and other avoidable image quality problems have been catalogued[73].
 18Available still from the manufacturer, now named IrisID.
 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

18 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 19 

Time Elapsed Between Samples (Days)

Dissimilarity Score

0.1

0.2

0.3

0.4

0 1000 2000 3000

a=0.14 b=2.5e−05

OPS−FIELD Alg: IREX−IV I01P ID: 107186

a=0.15 b=1.3e−04

OPS−FIELD Alg: IREX−IV I01P ID: 1473852

0 1000 2000 3000

a=0.15 b=−2.7e−05

OPS−FIELD Alg: IREX−IV I01P ID: 1485368

a=−0.00 b=2.3e−05

OPS−FIELD Alg: IREX−IV I01P ID: 1486787

a=0.03 b=9.4e−06

OPS−FIELD Alg: IREX−IV I01P ID: 1600384

a=0.05 b=2.5e−05

OPS−FIELD Alg: IREX−IV I01P ID: 600028

a=0.13 b=3.0e−06

OPS−FIELD Alg: IREX−IV I01P ID: 635853

a=−0.00 b=4.0e−05

OPS−FIELD Alg: IREX−IV I01P ID: 643758

0.1

0.2

0.3

0.4 a=0.17 b=−1.2e−05

OPS−XING Alg: IRIDIAN c2003 ID: 107237L

a=0.06 b=−1.9e−05

OPS−XING Alg: IRIDIAN c2003 ID: 132665L

a=0.10 b=2.8e−05

OPS−XING Alg: IRIDIAN c2003 ID: 56595L

a=0.12 b=2.4e−05

OPS−XING Alg: IRIDIAN c2003 ID: 57379L

a=0.15 b=1.8e−05

OPS−XING Alg: IRIDIAN c2003 ID: 73522L

a=0.25 b=−9.6e−06

OPS−XING Alg: IRIDIAN c2003 ID: 80153L

a=0.15 b=2.1e−06

OPS−XING Alg: IRIDIAN c2003 ID: 86524L

a=0.17 b=6.7e−07

OPS−XING Alg: IRIDIAN c2003 ID: 98564L

0.1

0.2

0.3

0.4 a=0.14 b=1.9e−06

ND04−08 Alg: ICE CAM2 ID: 4385R

a=0.19 b=1.3e−06

ND04−08 Alg: ICE CAM2 ID: 4470L

a=0.06 b=8.1e−06

ND04−08 Alg: ICE CAM2 ID: 4537R

a=0.13 b=−2.9e−06

ND04−08 Alg: ICE CAM2 ID: 4888R

a=0.04 b=4.5e−06

ND04−08 Alg: ICE CAM2 ID: 4975L

a=0.09 b=1.4e−05

ND04−08 Alg: ICE CAM2 ID: 5015R

a=0.13 b=7.3e−06

ND04−08 Alg: ICE CAM2 ID: 5028L

a=0.19 b=1.8e−05

ND04−08 Alg: ICE CAM2 ID: 5044R

0.1

0.2

0.3

0.4 a=0.15 b=2.8e−06

ND04−08 Alg: IREX IV I02P ID: 4385R

a=0.14 b=9.3e−06

ND04−08 Alg: IREX IV I02P ID: 4470L

a=0.06 b=6.3e−06

ND04−08 Alg: IREX IV I02P ID: 4537R

a=0.12 b=−9.4e−07

ND04−08 Alg: IREX IV I02P ID: 4888R

a=0.03 b=9.0e−06

ND04−08 Alg: IREX IV I02P ID: 4975L

a=0.08 b=1.6e−05

ND04−08 Alg: IREX IV I02P ID: 5015R

a=0.12 b=5.3e−06

ND04−08 Alg: IREX IV I02P ID: 5028L

a=0.16 b=1.9e−05

ND04−08 Alg: IREX IV I02P ID: 5044R

0.1

0.2

0.3

0.4 a=0.02 b=6.2e−05

ND08−10 Alg: IREX IV I02P ID: 4385L

a=0.03 b=2.6e−05

ND08−10 Alg: IREX IV I02P ID: 5015L

a=0.05 b=5.4e−05

ND08−10 Alg: IREX IV I02P ID: 5293L

a=0.03 b=3.6e−06

ND08−10 Alg: IREX IV I02P ID: 5344L

a=0.07 b=1.7e−05

ND08−10 Alg: IREX IV I02P ID: 5489L

a=0.06 b=3.2e−06

ND08−10 Alg: IREX IV I02P ID: 5496L

a=0.04 b=7.6e−06

ND08−10 Alg: IREX IV I02P ID: 5501L

a=0.11 b=5.7e−05

ND08−10 Alg: IREX IV I02P ID: 5513R

Figure 5: Score trajectories by dataset: Hamming distances over time for eight selected individuals in each of the four datasets 

considered in this paper. The matching algorithms are identified in the panel header and all originate with Daugman or his commercial 

partners. The same individuals from the N D04-08 set are shown for the c. 2006 CA M2 and c. 2012 I02P University of Cambridge 

algorithms. The density of points in the ND panels is due to the use of O(N2) full cross comparison. The individuals were randomly 

selected from the subset who participated in the respective collections more than N times over at least TA days as follows: OP S -FI E LD, 

N ≥ 20, TA ≥ 1460; O P S -X I NG, N ≥ 40, TA ≥ 1460; ND04-08, N ≥ 20, TA ≥ 1095; and N D80-10, N ≥ 20, TA ≥ 600. The red lines are the 

result of fitting a linear model to HD=HD(t) and are useful as an exploratory indicator. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

19 
�

�

�

�

JU LY 11, 2013 I RE X V I - IR I S A G EI NG 20 

3.1 Overview 

Figure 5 shows recognition scores as a function of time for the four datasets considered in this paper. The following points 

are made to note the varied characteristics of time-series biometric data. 

Sampling rates: In most cases, subjects were sampled irregularly in time, with the exception that the Notre Dame 

protocol called for regular collection. Note that the graphs show time between collections so a pair of proximal points 

will sometimes correspond to images collected years apart (e.g. 2004-2005 and 2007-2008). The N D plots include 

many points as a result of full-cross comparison of images, while the O P S -X I N G points represent recognition against 

singular enrolled images. 

Averages vary: The mean H D values, and intercepts, vary between person, and between dataset. This variation exists 

as product of equipment, cooperation, habituation and recognition algorithm19. For the O P S -X I N G set, the mean H D 

will be dependent on the quality of the initial enrollment image - any collected defect there will elevate scores in 

perpetuity. 

Variances vary: The variation in H D is evident across datasets. Larger variation would occur if the collection was 

not controlled, if the camera did not enforce quality criteria, or if the subject was not cooperative. The laboratory 

set N D04-08 gives the highest ranges while the two large operational sets, O P S -FI E L D and O P S -X I N G, give somewhat 

lower variance. 

Gradients vary: The slope of the simple regression lines vary between persons, some increase, some decrease. The 

lines are strictly for exploratory purposes, more powerful models are included later. If C L A S S D ageing effects associated with the iris texture are present in these time-series, the problem of quantifying an ageing rate involves 

detection of signal within noise. 

3.2 Effect of dilation and dilation-change 

Given pupil and iris radius estimates, RP and RI , dilation is stated as the pupil-iris radii ratio, D = RP /RI where for the 

O P S -X I N G data the radius estimates come from the native operational algorithm, and for the N D sets radii are consensus 

values estimated as the arithmetic mean of values reported by the F02P, I02P and D03P algorithms submitted to I R E X I V: 

1 R = (RF02P + RI02P + R D03P) (2) 3

Radius estimates usually differ only by very small amounts. We did not compute radii for the O P S -FI E L D images.
 

Let a mated pair of images have dilation estimates, D1 and D2. If, without loss of generality D1 ≥ D2, then a dilation
 

difference measure can be stated as:
 

1 − D1 ΔD = 1 − (3) 1 − D2

which, with D = RP /RI , can be restated in terms of the radial iris thicknesses, t, as 

� � � � � � RI1 RI2 − RP 2 ΔD = = γ

t2 (4) RI2 RI1 − RP 1 t1

19The Figures all show H Ds from algorithms traceable to John Daugman at Cambridge but may differ in material details such as their image processing 

and feature extraction methods. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

20 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 21 

Enrollment Dilation, D_1

Recognition Dilation, D_2

0.2

0.4

0.6

0.2 0.4 0.6

E−cam B E−cam L

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

Figure 6: Score depends on paired dilation: For the OPS-X IN G dataset, H D as a function of the dilation of the search and enrollment 

images. The heatmap shows mean HD computed over [D − 0.02, D + 0.02] intervals value, incrementing D in steps of 0.02. Note that 

difference in dilation is very influential on score. Note also that low or high dilation in both images is also deleterious The left panel 

corresponds to enrollment and recognition camera pair (B,B), the right panel to (L,B). The latter scores are higher due to the use of the 

L camera, and the camera interoperability effects discussed in section 2.1.1. 

which has the more physical interpretation as the product of a camera magnification term, γ assuming the anatomical 

iris has constant size, and an iris radial thickness ratio t2/t1. This measure was used previously to assess sensitivity of 

algorithms to dilation change[36]. Other metrics have been reported in the literature[28, 39, 40] particularly 

ΔN = D2 − D1 (5) 

where a difference symbol is used for reference later. This metric lacks the physical intuitiveness of the thickness measure. 

The dependence on ΔD appears in Figure 7 and on D1 and D2 independently in the heatmap of Figure 6. For recent 

algorithms, I R E X I I I showed dependence of both false negative and positive identification rates on D1 and D2[34]. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

21 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 22 

Search Image Dilation

0.00

0.05

0.10

0.15

0.20

0.25

(0.13,0.17](0.17,0.21](0.21,0.25](0.25,0.29](0.29,0.33](0.33,0.37](0.37,0.41](0.41,0.45](0.45,0.49](0.49,0.53](0.53,0.57](0.57,0.61](0.61,0.65](0.65,0.69](0.69,0.73](0.73,0.77]

E−cam B

(0.13,0.17](0.17,0.21](0.21,0.25](0.25,0.29](0.29,0.33](0.33,0.37](0.37,0.41](0.41,0.45](0.45,0.49](0.49,0.53](0.53,0.57](0.57,0.61](0.61,0.65](0.65,0.69](0.69,0.73](0.73,0.77]

E−cam L

Dilation Difference

Dissimilarity Score (HD)

0.00

0.05

0.10

0.15

0.20

0.25

(0,0.02]

(0.02,0.04](0.04,0.06](0.06,0.08]

(0.08,0.1](0.1,0.12]

(0.12,0.14](0.14,0.16](0.16,0.18]

(0.18,0.2](0.2,0.22]

(0.22,0.24](0.24,0.26](0.26,0.28]

(0.28,0.3](0.3,0.32]

(0.32,0.34](0.34,0.36](0.36,0.38]

(0.38,0.4](0.4,0.42]

(0.42,0.44](0.44,0.46](0.46,0.48]

(0.48,0.5](0.5,0.52]

(0.52,0.54](0.54,0.56](0.56,0.58]

E−cam B

(0,0.02]

(0.02,0.04](0.04,0.06](0.06,0.08]

(0.08,0.1](0.1,0.12]

(0.12,0.14](0.14,0.16](0.16,0.18]

(0.18,0.2](0.2,0.22]

(0.22,0.24](0.24,0.26](0.26,0.28]

(0.28,0.3](0.3,0.32]

(0.32,0.34](0.34,0.36](0.36,0.38]

(0.38,0.4](0.4,0.42]

(0.42,0.44](0.44,0.46](0.46,0.48]

(0.48,0.5](0.5,0.52]

(0.52,0.54](0.54,0.56](0.56,0.58]

E−cam L

Figure 7: Score depends on pupil dilation and dilation difference: For the O P S -X I N G dataset and its two enrollment camera models, 

plots of Hamming distance H D as a function of the absolute dilation of the search image (above) and the dilation change measure 

of equation 4 (below). Poor HD values are produced when the search image dilation is absolutely low or high. Poor HD values are 

produced when pupils have different dilations. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

22 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 23 

Elapsed Time (60 day intervals)

Dis−similarity Score (HD)

0.1

0.2

0.3

0.4

123456789

10111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758

439609312067284356264628243412229948219806198479182210170718156779147070140746126980118406109197101951

952019105482905757176752359675523474639538146307482255014733

776438611204

3

B

25

727

53079419

125031413315297164461867921050236082519826139276633065735230384844161942091430084228943173439734506944915431864344245088462104678743261396163775136483351203454032583300902716525795240482201019350162841260610378

754458524156335827792514187117221334

766403200

L

Figure 8: Stability of score distributions: For the O P S -XI NG data, the time evolution of the H D distribution computed over all individuals enrolled on camera L (the LG2200), above, or camera B (the Panasonic BM-ET 330), below. All border crossing captures use camera 

B. The horizontal axis is divided into 60-day bins, spanning 3480 days. The matching algorithm is the c. 2003 Daugman variant used 

by Iridian. The distributions are shown as box plots whose whiskers extend to 1.5 times the interquartile range which is shown by the 

green box. The red crosses below the whiskers are outliers under this definition. The median is indicated by the horizontal line, and 

the notch size in the side of the box is an uncertainty statement inversely related to the number of transactions which appears as grey 

text above each box. There are no outliers indicated above the whiskers because the data was thresholded at HD = 0.27 as discussed in 

section 2.1.2. The first three boxplots for camera L are higher due to the effect of transition (from late 2007 until early 2008) of subjects 

who had previously only used camera L, to recognition with camera B. 

4 Results 

4.1 Overview 

The results are organized as follows: Section 4.2 reports results for the O P S -X I N G set of border crossing logs. The results 

include regression approaches supplemented by exploratory figures. Section 4.3 gives recognition results for the images 

of the University of Notre Dame (N D) datasets. This includes exploratory analyses, consideration of particular images, 

and quantitative cause-and-effect results. Section 4.4 gives exploratory results for the O P S -FI E L D images. 

4.2 Results for O P S -X I N G transactions 

Figure 8 presents the time evolution of the Hamming distance distribution as a function of the time elapsed between 

enrollment and recognition. These are computed over the numbers of recognition events given above each box - some 

60-day intervals see more than 100,000 transactions. For an explanation of the two panels, labelled “L” and ”B’, see section 

2.1.1. The centers of the boxplots show the median score has small deviations around the global median shown in yellow. 

Moreover, there is no evidence of the upward trend that would indicate ageing. In and of itself, this is an important result 

obtained from a population 1200 times larger than the largest prior study[29], and over time intervals about twice as long 

as those at Notre Dame[29] and as long as those in Warsaw University of Technology[16]. 

Nevertheless, this result is inadequate in two ways. First, it does not show whether more scores are appearing in the 

upper tail. To examine that, Figure 9 shows the proportion of scores above various thresholds, τ , i.e. the false nonF NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

23 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 24 

Elapsed time (Days). FNMR is computed over 60 day intervals)

FNMR i.e. Frac. scores with HD > T

0.1

0.2

0.3

0.4

0 1000 2000 3000

B L

Threshold

0.20

0.22

0.24

0.26

Figure 9: Stability of error rates: Time evolution of F NM R for four given thresholds applied to the O PS-X I N G data. The estimates are 

computed for all individuals enrolled on the LG2200 (camera L), above, and the others on the Panasonic BM-ET 330 (camera B), below. 

All verification captures use camera B. The matching algorithm is the c. 2003 Daugman variant used by Iridian. F N MR is computed over 

contiguous 60-day intervals. Thresholds values above 0.27 are not possible, per section 2.1.2. The spikes in the lower right and upper 

left panels are due to very small sample sizes. 

Enrollment on LG 2200 Enrollment on Panasonic BM-ET 330 

Quantity Q01 Q25 Med Mean Q75 Q99 Q01 Q25 Med Mean Q75 Q99 

Tmin 126.0 502.0 901.0 987.6 1364.0 2685.0 0.0 0.0 41.0 194.7 241.0 1393.6 

Tmax 295.0 1648.0 2032.0 1987.3 2476.0 3306.0 0.0 156.0 506.0 614.1 1003.0 1765.0 

TA 0.0 350.0 1181.0 999.7 1612.0 1808.0 0.0 0.0 235.0 419.4 716.0 1689.0 

ni 1.0 3.0 8.0 23.5 27.0 192.0 1.0 1.0 3.0 9.3 8.0 96.0 

HD mean 0.081 0.162 0.197 0.191 0.225 0.264 0.014 0.104 0.150 0.147 0.193 0.262 

HD sd 0.003 0.022 0.029 0.030 0.036 0.067 0.002 0.027 0.039 0.040 0.051 0.102 

D2 mean 0.270 0.372 0.415 0.420 0.463 0.604 0.279 0.390 0.440 0.444 0.494 0.634 

D2 sd 0.004 0.031 0.042 0.044 0.055 0.107 0.002 0.026 0.039 0.041 0.053 0.112 

D1 mean 0.272 0.377 0.435 0.442 0.500 0.655 0.258 0.350 0.398 0.407 0.456 0.618 

ΔD mean 0.010 0.061 0.092 0.107 0.139 0.307 0.006 0.058 0.092 0.104 0.138 0.299 

ΔD sd 0.005 0.036 0.051 0.053 0.067 0.131 0.002 0.032 0.048 0.051 0.066 0.138 

ΔN mean 0.006 0.037 0.056 0.065 0.084 0.185 0.003 0.035 0.055 0.063 0.083 0.181 

ΔN sd 0.003 0.024 0.034 0.035 0.045 0.088 0.001 0.020 0.030 0.032 0.041 0.086 

Table 2: Population statistics: For the O P S -X I N G dataset, population summary statistics (1, 25, 50, 75, 99 quantiles) for, in the left six 

columns those eyes enrolled on the LG2200, and on the right the BM-ET 330. The rows give, in order, Tmin, the time from enrollment 

until first use; Tmax, the time from enrollment until last use; TA, the time from first to last use; ni, the number of recognition results 

for a particular eye; the eye-specific mean H D; its standard deviation; the eye-specific mean dilation during recognition; its standard 

deviation; the dilation of the enrollment image; the mean dilation thickness difference (eq. 4); its standard deviation; the mean dilation 

difference (eq. 5); and its standard deviation. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

24 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 25 

Wilcoxon p−value

ECDF (Frac. eyes with p−values < X)

0.0001

0.0001

0.0002

0.0005

0.0010

0.0020

0.0050

0.0100

0.0200

0.0500

0.1000

0.2000

0.5000

1.0000

2.0000

1E−063E−061E−053E−051E−043E−041E−033E−031E−023E−021E−013E−011E+00

E−cam B E−cam L

B : 50850 eyes, N > 20, T_max > 365 days

B : 41828 eyes, N > 20, T_max > 730 days

B : 29407 eyes, N > 20, T_max > 1095 days

L : 18331 eyes, N > 20, T_max > 365 days

L : 18305 eyes, N > 20, T_max > 730 days

L : 18160 eyes, N > 20, T_max > 1095 days

Figure 10: Long vs. short time lapse: For the O P S -X I NG dataset, the graph shows the cumulative distribution function of p-values 

produced in a non-paired Wilcoxon test of H D increase. The test is applied to eyes for which more than N ≥ 20 comparison scores are 

available. It tests whether the median of the later scores is higher than that of the early scores. A low p-value indicates that the median 

is significantly greater. For those enrolled on camera B, the plots indicates that about 1.1% of eyes give significantly higher late scores 

vs. early scores if p = 0.01 is held to be significant. For those enrolled on camera L the fraction is smaller. 

match rate F N M R(τ ). Four different thresholds are used. For enrollment camera B, F N M R is flat, and for camera L, very 

slightly decreasing20. A decrease would be consistent with subjects presenting better images to the camera, as would occur 

with gradual habituation to the camera as explained in Figure 11. The overall lack of trend is consistent with absence of 

population wide iris-ageing. The second inadequacy is that there could be some individuals (a minority) who are actually 

ageing quite rapidly even though the population apparently is not. Two approaches are used to detect such possibilities. 

First is a simple test for increasing scores. Second is mixed-effect regression approach which additionally includes the 

effect of pupil dilation. 

Figure 10 shows the fraction of the population producing higher second half scores than first half. Specifically if recognition data is available on the interval [T, T + TA], we use a non-paired one-sided Wilcoxon rank sum test of whether the 

scores on [T + TA/2, T + TA] have higher median than those on [T, T + TA/2). A significant result would indicate an 

upward trend in an individual trajectory. 

The Figure shows the cumulative distribution function of p-values from this test. For eyes enrolled on camera B, about 

1% of the 50,850 eyes for which at least 20 recognition transaction exist over at least 1 year exhibit a significant increase, 

if p < 0.01 is held as significant. This high figure is similar when Tmax is extended to include only long-term users. On 

camera L the figures are lower possibly because personal standard deviations are lower - Table 2 shows the population 

average σ = 0.030 for camera L and σ = 0.040 for camera B - and this makes increase-detection more difficult. 

20The early and late fluctuations in F N M R are due to significance - see the transaction counts in Figure 8 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

25 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 26 

4.2.1 Individual-specific results for O P S -X I N G 

This section applies mixed-effects regression to time-series data of the kind shown in Figure 4. This is intended to detect 

iris ageing effects in the presence of measurement noise by regarding observed iris recognition dissimilarity scores from 

each eye as samples drawn from a potentially non-stationary process, i.e. one whose statistics vary. This approach has 

been noted previously[16]. We consider only wide-sense stationarity examining only the mean and variance. 

Figure 4 shows eyes for which 60 or more transactions succeed over five years. It is clear that these distributions are 

sometimes not stationary. The possible causes of this include 

changes in the illumination environment, in the anatomical 

source (iris and pupil), and in behavioral interaction with 

the camera (i.e. habituation[51] 21). Figure 11 is consistent 

with human acclimation to the system. It shows the genuine 

distribution as a function of the frequency of use. For those 

using the system once for year or less, the mean H D is 0.154; 

for those using it more frequently than once a month the average H D is 0.129. The default conclusion from this is that 

frequent travelers have a better conscious or unconscious 

understanding of what it takes to produce a high quality 

iris image. These subjects may, for example, remove their 

eye-glasses, open their eyes widely, reduce head motion, or 

intuit a better idea of where the focal plane lies. While the 

observed score effect could have a correlation with age, sex, 

or profession, the leading hypothesis, given quantitative[51] 

and anecdotal evidence, is that habituation is responsible. 

Longitudinal analysis addresses the case where many response variables (e.g. patients’ responses to a drug) are 

modeled given (many) fewer repeated measurements. It is 

advanced here for biometric ageing because it is capable of 

handling multiple responses (i.e. subjects or eyes) that are 

not balanced (subjects are imaged irregularly over time), and 

potentially correlated through time (a Hamming distance today may be correlated with later ones) and across units (e.g. 

between left-and-right eyes). In these respects, the generalized linear mixed effects model (GLMM)[30] is appropriate. 

The “generalized” part is needed for non-Normal responses 

(e.g. Bernoulli recognition decisions). The “mixed” part of 

the model refers to “fixed” and “random” effects, the former capture the population-wide variation, while the latter models individual-specifics. Examples of fixed covariates in iris recognition would be camera and type of ambient illumination. 

The random effects part gives subject-specific regression effects and these, when combined with the fixed effects give the 

mean response of an individual. 

21Kukula et al.[51] chart the time-evolution of comparison scores for a hand geometry authentication system, and conclude “that repeated use of the 

[hand geometry] device yields some increase in the performance”. 

Dissimilarity Score (HD)

0

2

4

6

8

0.05 0.10 0.15 0.20 0.25

Mean = 0.129

 SD = 0.066

B

[0,30]

Mean = 0.179

 SD = 0.051

L

[0,30]

Mean = 0.140

 SD = 0.067

B

(30,120]

Mean = 0.186

 SD = 0.051

L

(30,120]

0

2

4

6

8 Mean = 0.151

 SD = 0.066

B

(120,365]

Mean = 0.193

 SD = 0.050

L

(120,365]

Mean = 0.154

 SD = 0.066

B

(365,1e+03]

Mean = 0.199

 SD = 0.048

L

(365,1e+03]

Figure 11: Frequent travellers, better scores: Hamming distance 

distributions by frequency of use, and by enrollment camera. The 

vertical line is the mean. The [a,b] intervals indicate the average 

time, in days, between uses of the system - frequent travelers are 

at the bottom. Note the leftward shift with frequency. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

26 
�

�

�

�

�

�

JU LY 11, 2013 I RE X V I - IR I S A G EI NG 27 

Additionally covariates may be constant e.g. iris color or gender, or time varying. Particularly the time since enrollment 

will take on different values at each capture. These covariates might be either under experimental control (e.g. a different 

camera is used) or stochastic (governed by a random process). Pupil dilation falls into this category and the dilation 

difference (eq. (4)) is therefore also stochastic22 . The use of stochastic covariates as one of the explanatory variables can 

lead to difficulty in interpretation of regression results because the mean iris comparison score, conditioned on all the 

covariates, should depend only on the covariates at that time. This would not be true for a stochastic covariate where the 

response variable causes a change in the covariate (e.g. a high cholesterol measurement causes an altered fat intake) or 

if the response is dependent on historical values of the covariate. When the covariate and the response are confounded, 

regression coefficients (β) can be biased and lose their implied causal role. A time varying covariate is called external 

when its value is not predicted by past values of the response variable[30] - for dilation this would mean that the act of 

sensing an iris (and comparing it to a template) does not influence future dilation values. This assumption is not true on 

a timescale of seconds because some cameras specifically illuminate the iris with visible light. But at longer timescales we 

do not expect repeated use of an iris camera to induce changes in dilation, nor do we expect humans to exercise conscious 

(or otherwise) control over pupil dilation during camera usage. 

We apply mixed-effects approaches to the longitudinal evolution of the dissimilarity between the enrollment and search 

images. Mixed-effects models have been used in biometrics previously for failure analysis[9] but not longitudinal change. 

Given ni dissimilarity scores for the i-th eye, we model the j-th score 

+ β7C(r) + bi1 + bi2Tij + bi3ΔDij + bi4Dij + bi5D2 dij = β1 + β2Tij + β3ΔDij + β4ΔD (6) 2 + β5Dij + β6D2

ij ij ij + eij

where 

The βk values are fixed over the population, and the bik values are eye-specific. 

the sum of the fixed and random effects, β1 + bi1, is an eye-specific intercept corresponding to initial status after zero 

elapsed time. 

Given ni times between captures, Tij , the sum, β2 + bi2, is an eye-specific gradient expressing rate-of-change of 

dissimilarity with time. The value β2 is the population average rate that is the primary focus of this paper. This 

model uses a linear form for three reasons. First, we expect iris ageing to be a continuous monotonic process. 

Second, we have no prior (anatomical or physiological) justification for a more elaborate functional form. Third, 

model-parsimony dictates a simple intercept and gradient model. 

Dilation differences, ΔD, are included with linear and quadratic terms23 in the fixed part of the model, and with just 

a linear term in the random-effects part. This allows the dilation dependence of Figure 7 to be captured across the 

population with an eye-specific linear adjustment. 

The dilation of the search image, D, is represented by linear and quadratic terms. The dilation of the enrollment 

image is included only via the ΔD term. An alternative, to incorporate a model of the joint dilation shown in Figure 

6, was not considered, but may yield benefits in future work. 

C(r) is the contrast for the eye label, 0 for left and 1 for right. 

22Dilation is computed from consensus radius estimates for the first and second image (eq. 2). Dilation could itself be the response variable in a regression 

analysis, but without covariates such as ambient light, recent proximity to sunshine, recent pharmaceutical consumption, this cannot be pursued here. 

23These terms are actually included using orthonormal polynomials, which satisfy the requirement that covariates are linearly independent. The coefficients represented in eq. 6 and in the results tables are converted to a raw polynomial form a + bx + bx2 per [45]. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

27 
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JU LY 11, 2013 I RE X V I - IR I S A G EI NG 28 

ΔT TA Nmin Cam β1 β2 = Coef(ΔT) Coef(D2) Coef(D2

2) Coef(ΔD) Coef(ΔD2) Coef(Eye) Nscr Neyes 

5 182 5 B 0.13 -4.0E-07 ± 1.0E-07 -0.37 0.43 0.08 0.31 0.028 ± 0.000 3583505 150442 

5 182 7 B 0.13 -3.4E-07 ± 1.1E-07 -0.37 0.44 0.08 0.32 0.030 ± 0.000 3402362 122357 

365 182 20 B 0.13 -4.9E-07 ± 1.3E-07 -0.43 0.50 0.07 0.36 0.038 ± 0.001 2448914 48448 

730 182 20 B 0.13 -2.6E-07 ± 1.4E-07 -0.43 0.51 0.07 0.36 0.038 ± 0.001 2124322 39909 

1095 182 20 B 0.13 1.2E-07 ± 1.4E-07 -0.45 0.53 0.07 0.37 0.039 ± 0.001 1609932 28240 

1460 182 20 B 0.13 7.5E-07 ± 1.8E-07 -0.47 0.55 0.07 0.38 0.038 ± 0.002 925737 14601 

1460 182 40 B 0.13 7.2E-07 ± 2.2E-07 -0.53 0.62 0.06 0.41 0.048 ± 0.003 741702 8271 

1460 182 60 B 0.12 7.2E-07 ± 2.6E-07 -0.57 0.67 0.06 0.42 0.045 ± 0.005 584651 5104 

1460 182 80 B 0.12 7.4E-07 ± 3.2E-07 -0.61 0.71 0.06 0.43 0.043 ± 0.008 461792 3337 

1460 182 100 B 0.12 8.0E-07 ± 3.9E-07 -0.61 0.73 0.05 0.45 0.037 ± 0.009 360694 2213 

1460 1460 40 B 0.13 7.8E-07 ± 2.2E-07 -0.53 0.63 0.06 0.41 0.048 ± 0.004 715612 7876 

1460 1460 60 B 0.12 7.3E-07 ± 2.7E-07 -0.57 0.67 0.06 0.42 0.041 ± 0.005 569897 4945 

1460 1460 80 B 0.12 7.6E-07 ± 3.3E-07 -0.61 0.71 0.06 0.43 0.037 ± 0.008 452292 3255 

5 182 5 L 0.18 1.5E-06 ± 9.5E-08 -0.37 0.52 0.03 0.28 0.011 ± 0.000 1318689 35064 

5 182 7 L 0.18 1.7E-06 ± 9.7E-08 -0.38 0.53 0.03 0.28 0.012 ± 0.001 1291124 30800 

365 182 20 L 0.18 2.4E-06 ± 1.1E-07 -0.42 0.58 0.03 0.30 0.020 ± 0.001 1123839 17812 

1095 182 20 L 0.18 2.5E-06 ± 1.0E-07 -0.42 0.58 0.03 0.30 0.024 ± 0.001 1115230 17598 

1825 182 20 L 0.18 2.4E-06 ± 1.2E-07 -0.41 0.58 0.03 0.30 0.020 ± 0.001 1022768 15577 

2190 182 20 L 0.18 2.7E-06 ± 1.4E-07 -0.42 0.59 0.02 0.30 0.020 ± 0.001 750745 10952 

2555 182 20 L 0.18 3.0E-06 ± 1.8E-07 -0.44 0.61 0.02 0.30 0.018 ± 0.001 448513 6042 

2920 182 20 L 0.17 3.0E-06 ± 3.4E-07 -0.48 0.67 0.02 0.32 0.024 ± 0.004 94201 1360 

1460 182 40 L 0.17 2.6E-06 ± 1.4E-07 -0.47 0.66 0.02 0.31 0.025 ± 0.002 883057 9847 

1460 182 60 L 0.17 2.9E-06 ± 1.6E-07 -0.53 0.72 0.02 0.33 0.026 ± 0.002 699788 6145 

1460 182 80 L 0.17 2.8E-06 ± 2.0E-07 -0.57 0.78 0.02 0.33 0.024 ± 0.003 550440 3999 

1460 182 100 L 0.17 2.8E-06 ± 2.4E-07 -0.61 0.83 0.02 0.34 0.021 ± 0.004 439132 2756 

1460 1095 100 L 0.17 2.8E-06 ± 2.4E-07 -0.62 0.85 0.01 0.34 0.020 ± 0.004 435002 2725 

1460 1460 100 L 0.17 2.8E-06 ± 2.3E-07 -0.61 0.83 0.02 0.34 0.025 ± 0.005 423363 2644 

Table 3: Population average ageing rates: Regression results for subsets of the O PS -X I N G dataset. Each row specifies a particular set of 

eyes (i.e. unique combinations of person and left-or-right eye) for which there are Nmin transactions spread over at least TA days, and 

for which the last transaction occurs at least (ΔT) days after enrollment. Each row shows mixed-effects regression coefficients for the eq. 

(6) model, particularly the rate coefficient for the time between enrollment and search, ΔT, dilation change, ΔD (eq. 4), and for the right 

eye (left is zero). The comparison scores were produced by comparing recognition samples against a singular initial enrollment image. 

The key result, highlighted in yellow, is that population averaged changes in Hamming distance are small - the largest β2 value, 3 x 

10−6 corresponds to ΔH D = 0.011 over 10 years. The uncertainty estimates on these rates are square roots of the variance of eye-specific 

ageing rates, bi. 

eij are residuals. 

The model represented by eq. (6) was applied separately for enrollment cameras L and B, because the evident complex interaction between H D, dilation, and camera in Figure 7. A large number of models were considered, this one was selected 

on the basis of the signficance of the various terms, parsimony, and the Akaike Information Criterion[70] summary. We 

also considered auto-correlation of the response by using the exponential model of auto-covariance. This gave a change in 

the standard deviation of random effects in the fourth decimal place. It is computationally expensive so an uncorrelated 

within-subject response was used for all results with this dataset. This decision was supported by inspection of individual 

auto-correlation functions, which do not exhibit lagged correlations. Note that while ordinary least squares (O L S) estimators are unbiased predictors of true longitudinal change, the mixed-effects models estimates tend to shrink from the 

O L S values, when the observations are correlated. The models were fit with version 3.1 of the N L M E[70] package running 

under version 2.15.1 of R. Restricted maximum likelihood estimation was used. 

If this analysis is restricted only to left eyes, omitting the eye as a covariate in eq. (6), the intercepts and dilation coefficients 

change in the last decimal place. The population means for rate-of-change β2 = Coef ΔT are higher by no more than 

0.5 x 10−7. 

Table 3 gives population means produced by applying the mixed-effects model of equation (6) to various subsets of the 

O P S -X I N G logs. The notable observations are: 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

28 
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JU LY 11, 2013 I RE X V I - IR I S A G EI NG 29 

Rate of change: This paper seeks to estimate the time-dependence of iris recognition due to changes in the iris 

texture. This is quantified as a rate of change of dissimilarity score. Its calculation has been based on averaging 

results over as large a population as possible on the basis that random effects, not otherwise accounted for, will be 

unbiased. The sixth column of Table 3 tabulates β2 population means varying between −4 x 10−7 to 8 x 10−7 for 

persons enrolled on camera B and 1.5 x 10−6 to 3 x 10−6 for those using camera L. The values quantify change in 

Hamming distance per day. They vary slightly with the subset of the data used. Per equation (8), wide temporal 

distribution datasets offer better precision in the measurement, and thus the latter rows (with large TA values) in 

the Table are more interesting. However, the last columns show that fewer individuals eyes are engaged for those 

durations. Longer engagements of persons enrolled with camera L produce more precise estimates. 

For the two cameras, the rates differ by a factor between three and four: 0.8 x 10−6 for camera B vs. 3.0 x 10−6 for 

camera L. The reason for this is not obvious; it is not a degradation in the camera as the L/B label applies to initial 

enrollment and camera B is used for all recognition transactions. Instead the rate difference may be related to the 

higher overall H D values for camera L vs. B. Subjects enrolled on camera L have longer average time durations, 

Tmax, but this should not effect the rate estimate if ageing is a continuous linear process. Further investigation is 

warranted. 

Camera effect: The β1 values are H D-intercepts treated as a fixed effect. The estimate for the B camera is about 0.12, 

and that for the L camera is 0.17. Recall that these labels indicate the camera used for the initial enrollment image, 

and all recognition images were collected using camera B. Thus, the difference here is most likely the direct result of 

cross camera (B,L) interoperability effects - see section 2.1.1. This systematic difference in Hamming distances, ΔH D 

= 0.05, is an order of magnitude larger than any ageing-related effects projected over a decade. 

Dilation difference: The dependence of H D on pupil dilation differences is modelled as an individual eye-specific 

effect, using eq. (4). The population means are camera specific: the coefficient for camera B is 0.18, and for camera L, 

0.11. This means that if the enrollment and recognition images differ in pupil dilation by 0.1 (say D = 0.25 vs. 0.35), 

then this model indicates Hamming distance changes of ΔH D = 0.018 and 0.011, for cameras B and L respectively. 

Search image dilation: While dilation difference is the primary contributor to increases in Hamming distance, Figure 

7 shows that low and high values in both images will also produce an increase. The regressions over the various 

O P S -X I N G subsets produce somewhat varied dependences on dilation which limits the precision of the model. This 

part of the model is approximately ΔH D= −0.5D + 0.7D2 (via inspection of rows in the Table). 

Left vs. right eye: The eye itself, left or right, is influential: right eyes give higher H D values - over the whole dataset 

this is 0.03 for camera B, and 0.02 for camera L. This occurs because the right eye is used only if the left eye failed or 

was not acquired. The number of left eye events in the O P S -X I N G database is 4,920,638 vs. 725,300 for the right eye. 

We now address variation in eye-specific gradients. Figure 4 shows instances of upward and downward trend in H D 

values. Moreover, that Figure applies to people with at least 60 data points spanning TA ≥ 1460 days and, on that basis, the 

gradients are “robust”. Those plots are estimated using ordinary least squares (O L S) regression (for exploratory purposes) 

without dilation. O L S assumes independence and homoscedasticity of the residuals. However when these assumptions 

are violated the intercept and gradient estimates are still unbiased but have worse efficiency i.e. they’re noisy estimators 

of the underlying true values[85]. 

However it is known that if the residual eij is the difference between the H D for the j-th observation of the i-th individual 

and the value predicted by the O L S fit at that time, then the variability of H D around the O L S-estimate is summarized for 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

29 
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JU LY 11, 2013 I RE X V I - IR I S A G EI NG 30 

the i-th eye as ni 1 N 2 σ2 = eij (7) i N

j=1

whence the precision of the O L S-estimated gradient for person i is given by the sampling variance for that statistic[85]: 

σ2

 i Vi(m) = ni (8) 

j=1(tij − t

 ̄i)2

Thus the precision of the O L S-estimate will be increased if either the numerator ’s “measurement” error can be reduced, 

or the denominator ’s statement of temporal extent can be increased. The term “measurement” error here is the residual 

difference between the measured H D and an ideal H D which would arise if a flawless iris was presented consistently every 

time (no eyelashes, frontal gaze, constant dilation, etc). 

The nature of equation (8) applies to the mixed-effects results tabulated in Table 3, i.e. the long-term data gives a narrower 

range of iris rate-of-change estimates. Figure 12 shows the distribution of the β2 + bi2, i.e. the population-mean plus the 

best linear unbiased predictor (B L U P) for each eye. The notable observations are: 

Variance: The gradients for individuals enrolled on camera B have larger variance than camera L. The distributions 

for camera L are narrower because a) scores tend to vary less (mean personal σL = 0.03 vs. σB = 0.04 in Table 2), 

and b) the subjects are present in the dataset over longer time periods, i.e. average TA is larger than that for camera 

B. If the minimum active duration TA is reduced below the 1460 days shown in the Figure, the distributions become 

broader (following eq. (8)). 

Worst case upward trends: Some individuals age quickly with rates β +bi ≥ 2x10−5 day−1. This is not a significance 

artefact - instances of ageing at these rates are present in the data - see Figure 424 . 

The reasons for ageing on these timescales in populations this large will inevitably include some cases of disease. 

Other causes would include contact lens presence, changes in contact lens prescription, changes in use of eye-lash 

cosmetics and eye surgery. In addition, the regression may yield these gradients simply on the basis of the (noisy) 

data. But note that in many cases the individual is capable of producing a low H D score at the end of the interval. 

This indicates that the iris itself has not aged to the point that successful recognition is impossible. 

Downward trends: Note also that some individuals have downward trends. This, by definition, is consistent with a 

collection of an image similar to the initial enrolment, which would occur under habituation, e.g. the subject learns 

to open eyes widely. 

Normality: The Q Q-plots of Figure 12 indicate the gradient predictors are approximately Normally distributed 

through ±2σ but with heavier-than-Normal tails. Beyond the suggested causes above, we cannot further explain 

such outcomes because images are unavailable. 

24See individual 133979L but note there that the gradient value appearing above the points does not account for dilation changes. The mixed effects 

model does include dilation effects. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

30 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 31 

Gradient BLUPs (23899 of 367049 eyes)

Count

0

10000

20000

30000

40000

50000

−4e−05 0e+00 4e−05

n=7876 eyes

Mean=7.85e−07

SD=1.46e−05

E−cam B, T>1460, T_A>1460, N>40

n=3255 eyes

Mean=7.60e−07

SD=1.53e−05

E−cam B, T>1460, T_A>1460, N>80

−4e−05 0e+00 4e−05

n=8977 eyes

Mean=2.65e−06

SD=8.55e−06

E−cam L, T>1460, T_A>1460, N>40

n=3791 eyes

Mean=2.74e−06

SD=9.87e−06

E−cam L, T>1460, T_A>1460, N>80

Theoretical Quantiles (Normal Distribution)

Observed

−5e−05

0e+00

5e−05

−4 −2 0 2 4

n=7876

Mean=7.85e−07

SD=1.46e−05

E−cam B, T>1460, T_A>1460, N>40

n=3255

Mean=7.60e−07

SD=1.53e−05

E−cam B, T>1460, T_A>1460, N>80

−4 −2 0 2 4

n=8977

Mean=2.65e−06

SD=8.55e−06

E−cam L, T>1460, T_A>1460, N>40

n=3791

Mean=2.74e−06

SD=9.87e−06

E−cam L, T>1460, T_A>1460, N>80

Figure 12: Variable ageing rates: For various subsets of the OP S -XI N G data, the lower figure shows the distribution of the best linear 

unbiased predictors (BLUP) rates-of-change β2 + bi2 as computed for each eye in the model of eq. (6). The left side applies to the 

Panasonic BM-ET 330 camera (B), the right to the LG-2200 (L). Only those eyes for which at least the given N recognition transactions 

span at least TA > 1460 days are included. The vertical yellow and black lines are separated by the value of β2 since E[bi2] is zero. The 

upper figures are Q Q plots of the empirical values vs. quantiles of a Normal distribution. These probably constitute the best estimates 

given this dataset. The distributions of L are narrower than B because the TA values are longer on average. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

31 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 32 

# Images

ND04−08 Subject−Eye ID

5052L

5052R

4975R

5053R

5028R

5053L

4888L

4888R

5564L

4975L

5015R

5015L

5564R

5028L

5033R

4537R

5045R

5033L

4917R

4233L

4470R

4537L

4917L

5027L

4815L

5045L

5027R

4261R

4629R

4233R

4629L

4261L

4470L

5044L

4397L

5044R

4815R

4385R

4851L

4870L

4851R

4870R

4397R

4385L

2463L

2463R

50 150 250

# Comparisons

0 20000 40000

# FalseNM F02P

0 500 1500 2500

# FalseNM CAM2

01000 3000 5000

# FalseNM D03P

0 1000 3000

# FalseNM B02P

0 1000 3000

# FalseNM H01P

0 1000 3000 5000

# FalseNM I02P

0 500 1500 2500

# FalseNM K03N

0 2000 6000

Mean DilDiff

0.00.20.40.60.8

5052L

5052R

4975R

5053R

5028R

5053L

4888L

4888R

5564L

4975L

5015R

5015L

5564R

5028L

5033R

4537R

5045R

5033L

4917R

4233L

4470R

4537L

4917L

5027L

4815L

5045L

5027R

4261R

4629R

4233R

4629L

4261L

4470L

5044L

4397L

5044R

4815R

4385R

4851L

4870L

4851R

4870R

4397R

4385L

2463L

2463R

# Images

ND08−10 Subject−Eye ID

5344L

5344R

5366L

5366R

5407R

5408L

5513R

5517L

5517R

5288L

5310R

5513L

5555L

4394L

5301L

5489L

5489R

5308R

5531R

5308L

5310L

5407L

5415L

5450L

5486L

5408R

5501R

5559R

5015L

5293L

5401L

5415R

4327L

5541L

5559L

5401R

5555R

5337L

5541R

5288R

5337R

5486R

5301R

5293R

5463R

4233R

5496L

4233L

4385L

5321L

5321R

5531L

5496R

4385R

4397L

5450R

4394R

5501L

5015R

2463L

5033L

2463R

4397R

4470L

4261R

4327R

5409R

5456R

5463L

5379R

5409L

5379L

5033R

5432R

5456L

5432L

5455L

4261L

5455R

4470R

0 1020304050

# Comparisons

0 500 1000

# FalseNM F02P

0 100200300400

# FalseNM F03N

0 50 100

# FalseNM D03P

0 50 100 200

# FalseNM B02P

0 50 100 200

# FalseNM H01P

0 50 100 200

# FalseNM I02P

0 100 300 500

# FalseNM K03N

0 50 100 150

Mean DilDiff

0.00.20.40.60.8

5344L

5344R

5366L

5366R

5407R

5408L

5513R

5517L

5517R

5288L

5310R

5513L

5555L

4394L

5301L

5489L

5489R

5308R

5531R

5308L

5310L

5407L

5415L

5450L

5486L

5408R

5501R

5559R

5015L

5293L

5401L

5415R

4327L

5541L

5559L

5401R

5555R

5337L

5541R

5288R

5337R

5486R

5301R

5293R

5463R

4233R

5496L

4233L

4385L

5321L

5321R

5531L

5496R

4385R

4397L

5450R

4394R

5501L

5015R

2463L

5033L

2463R

4397R

4470L

4261R

4327R

5409R

5456R

5463L

5379R

5409L

5379L

5033R

5432R

5456L

5432L

5455L

4261L

5455R

4470R

Figure 13: Goats in the biometric zoo: For all eyes in the N D04-08 dataset (above) and the 3-semester subjects of the N D08-10 dataset 

(below), the barcharts show, from left to right: the numbers of images of the eye; the number of non-self comparisons involving those 

images; the F N MR values for various matchers whose thresholds have been set to give F NM R= 0.02 over the whole dataset; and the 

mean dilation difference between image pairs, per eq (4). The concentration of errors in individuals indicates “goats” in a “biometric 

zoo”[25, 99]. As shown by the vertical red lines, F NM R = 0 for the majority of subjects. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

32 
 

 

JU LY 11, 2013 I RE X V I - IR I S A G EI NG 33 

4.3 Results for N D recognition 

This section aims to confirm and explain the observations reported by the researchers at Notre Dame for image sets 

collected in their laboratory[8, 7, 28, 29]. The analysis proceeds by applying c. 2012 commercial recognition algorithms to 

the archived N D images. In addition, we analyze archived results from the application of the C A M-2 algorithm submitted 

by University of Cambridge to the 2006 Iris Challenge Evaluation (I C E)[68]. 

Figure 13, is exploratory in nature. Its first two columns indicate an imbalance in the number of images collected from 

each individual. This has a quadratic influence on the number of comparison scores available for each individual, due to 

cross-comparison. This method of generating genuine scores represents an efficient use of the data but is not usually an 

operational use-case. The O P S -X I N G collection matches an initial enrollment instance with multiple recognition captures 

N captured over many years. If this yields ni genuine scores for the i-th individual, the total number is ni. For cases i

where images are retained and matched in a laboratory, as with N D, the standard experiment makes efficient use of the 

ni 2 data by executing all genuine comparisons, yielding (n − ni)/2 scores. This has implications for significance tests i i

where different components of uncertainty scale as the number of comparisons, images, eyes, or people. This contrasts 

with many operational cases where specific care, review and re-collection are key aspects of the enrollment process. 

By plotting the count of false non-matches for each eye when thresholds are set to give a false non-match rate F N M R of 

0.02 over the entire dataset, columns 3 through 9 of Figure 13 show that multiple recognition algorithms produce false 

non-match errors but that these errors are concentrated in the eyes of relatively few individuals. For example, in the 2004­

2008 collection, algorithms K02P, H01P and CAM-2 give false non-matches essentially on only a single individual, 02463. 

Concentration of false non-matches in certain individuals renders them “goats” in the biometric zoo[25]. The presence of 

such subjects in the N D sets has not been previously published, and while this analysis is not explicitly time dependent, it 

adds a qualifier to the N D findings, particularly that F N M R increases by 82% in the period 2008-201025 . 

The two plots of Figure 13 also reveal a correlation between the individuals that participated most and their F N M R, particularly in the first 2004-2008 collection. The explanation of this is not known. One hypothesis is that repetition engenders 

ennui or blas e behavior and this manifests itself in the images, for example as poor axial gaze, or poor focus due to  ́

sub-optimal positioning relative to the depth of field. 

Time Evolution: Figures 14 and 15 show the evolution of the genuine score distributions as a function of time. Note 

that the scores in the 2004-2008 set are actually lower than in the later set26 . The lack of a clear trend away from the 

global median is consistent with a no-ageing result, but is insufficient to conclude that no ageing is occurring because it 

could be confined only to a minority of individuals. This prompts the production of four full-page figures 16- 19 showing 

eye-specific time-series for all individuals in the two N D partitions. 

Figures 16 and 17 show the time evolution of comparison scores for two recent I R E X I V algorithms (I02P and D03P) applied 

to the N D images collected 2004-2008. Similarly Figures 18 and 19 show the time evolution of comparison scores for two 

recent I R E X I V algorithms (I02P and D03P) for the 40 individuals who were imaged at Notre Dame over the two year period, 

Spring 2008 to Spring 2010, many of whom where also captured in Spring 2009. This is exactly one partition reported in 

Table 1 of Fenker[29]. These figures, and those for six other algorithms included in the Appendices27, include annotations: 

25The 82% figure is taken from Table 2 in [29] for a Neurotechnology algorithm. The paper notes a 95% confidence interval for F N M R of [38,150]%. 

Note that this confidence interval used the bootstrap method, sampling individuals and scores. It should also[53] have sampled images which are the 

causative agent of recognition failure, and thereby did not confer the robustness against outliers that the boostrap usually affords. 

26 The early N D data included many poor images because the LG2200 camera’s internal quality controls had been disabled. Noting this was inappropriate 

in an ageing study, Baker et al.[8] produced a dedicated subset of images that “were manually screened for image quality” and this is the set used 

in this report. Additionally, because the N D04-08 data has been made available to researchers it is likely to have been used by all providers whose 

algorithms are used here. 

27See the PDF file linked from http://iris.nist.gov/irex 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

33 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 34 

Time Between Captures (days)

Dissimilarity Score

0.04 0.06 0.08 0.10

0 45 90 135 180 225 270 315 360 405 450 495 540 585 630 675 720 765 810 855 900 945 990 103510801125117012151260130513501395144014851530157516201665

A02P

0.0005 0.0015 0.0025 0.0035

B02P

1500 2000 2500 3000

D03P

10.00 10.02 10.04

F02P

0 10 20 30

F03N

0.55 0.65 0.75

H01P

0.00 0.05 0.10 0.15

I02P

0.00 0.05 0.10 0.15

K03N

0.00 0.05 0.10 0.15 0.20

CAM2

Figure 14: Stability of score distributions over time: Time-evolution of genuine comparison scores for the ND04-08 images. The nine 

panels correspond to eight c. 2012 algorithms submitted to N IS T’s I RE X I V evaluation [74], and one, C A M-2 (top), submitted in 2006 to 

I C E[68]. The green boxes denote the interquartile range, their center lines indicate the median. The yellow line is the all-time median. 

Outliers, in the distribution tails, are shown as pink crosses. The K03N algorithm emits most genuine scores with value exactly 0. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

34 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 35 

Time (45 day intervals)

Dissimilarity Score

0.00 0.01 0.02 0.03 0.04

0 45 90 225 270 315 360 405 450 630 675 720 765

A01N

0.0005 0.0010 0.0015

B02P

1000 1500 2000 2500

D03P

10.0010.0110.0210.0310.04

F02P

0 10 20 30

F03N

0.500.550.600.650.700.750.80

H01P

0.00 0.05 0.10 0.15

I02P

0.00 0.02 0.04 0.06

K03N

Figure 15: Stability of score distributions over time: Time-evolution of genuine comparison scores for the ND08-10 images. The nine 

panels correspond to eight c. 2012 algorithms submitted to N IS T’s I RE X I V evaluation [74], and one, C A M-2 (top), submitted in 2006 to 

I C E[68]. The green boxes denote the interquartile range, their center lines indicate the median. The yellow line is the all-time median. 

Outliers, in the distribution tails, are shown as pink crosses. The K03N algorithm emits most genuine scores with value exactly 0. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

35 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 36 

Time Between Captures (Days)

Dis−similarity Score, Algorithm I02P, Tau = 0.24

0.1

0.2

0.3

0.4

0 500 1000 1500

a=0.08 b=−5.4e−07

2463L

a=0.10 b=−1.4e−06

2463R

0 500 1000 1500

a=0.06 b=9.6e−06

4233L

a=0.08 b=2.6e−06

4233R

a=0.02 b=5.4e−06

4261L

a=0.02 b=1.1e−05

4261R

a=0.14 b=2.3e−06

4385L

a=0.15 b=2.8e−06

4385R

0.1

0.2

0.3

0.4

a=0.05 b=3.9e−06

4397L

a=0.04 b=3.8e−06

4397R

a=0.14 b=9.3e−06

4470L

a=0.15 b=1.4e−06

4470R

a=0.09 b=1.4e−05

4537L

a=0.06 b=6.3e−06

4537R

a=0.14 b=2.4e−05

4629L

a=0.14 b=2.1e−05

4629R

0.1

0.2

0.3

0.4

a=0.08 b=8.0e−06

4815L

a=0.07 b=1.5e−05

4815R

a=0.17 b=8.9e−06

4851L

a=0.18 b=5.1e−06

4851R

a=0.09 b=1.1e−05

4870L

a=0.10 b=5.2e−06

4870R

a=0.12 b=6.9e−06

4888L

a=0.12 b=−9.4e−07

4888R

0.1

0.2

0.3

0.4

a=0.11 b=1.2e−05

4917L

a=0.09 b=6.2e−06

4917R

a=0.03 b=9.0e−06

4975L

a=0.08 b=1.6e−06

4975R

a=0.09 b=2.8e−05

5015L

a=0.08 b=1.6e−05

5015R

a=0.18 b=−2.5e−06

5027L

a=0.20 b=−6.8e−06

5027R

0.1

0.2

0.3

0.4

a=0.12 b=5.3e−06

5028L

a=0.16 b=−5.7e−07

5028R

a=0.14 b=1.3e−05

5033L

a=0.13 b=4.3e−06

5033R

a=0.10 b=3.4e−05

5044L

a=0.16 b=1.9e−05

5044R

a=0.03 b=6.3e−06

5045L

a=0.05 b=1.0e−05

5045R

0.1

0.2

0.3

0.4

a=0.14 b=1.2e−05

5052L

a=0.14 b=6.7e−06

5052R

a=0.13 b=2.6e−05

5053L

a=0.13 b=1.8e−05

5053R

a=0.13 b=8.5e−06

5564L

a=0.13 b=−5.5e−06

5564R

Figure 16: Individual score trajectories: For dataset N D04-08, the plots show individuals’ dissimilarity scores for the I02P algorithm. 

The x-axis indicates time between two samples. Each dot corresponds to a pair of images. Any given image, i, is involved in ni − 1

comparisons, i.e. one fewer than the number of images of the given eye. The green horizontal line shows the threshold that gives 

F N MR= 0.02 globally. The straight red line is the result of regressing d = d(T), the intercept and gradient of which appear as text. Note 

the bimodal score distribution in panels 5044R, 5027L, 02463L+R i.e. the occurrence of H Ds near 0.4. These are consistent with outright 

segmentation failure. Their use in ageing rate computations is not recommended irrespective of the time-lapse at which they occur. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

36 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 37 

Time Between Captures (Days)

Dis−similarity Score, Algorithm D03P, Tau = 3373.00

2000

3000

4000

5000

0 500 1000 1500

a=2435.35 b=6.4e−03

2463L

a=2588.55 b=4.0e−02

2463R

0 500 1000 1500

a=2320.96 b=1.0e−01

4233L

a=2316.80 b=3.7e−02

4233R

a=1976.66 b=5.4e−02

4261L

a=2172.67 b=1.4e−01

4261R

a=2751.32 b=3.3e−02

4385L

a=2679.42 b=3.7e−02

4385R

2000

3000

4000

5000 a=2204.12 b=6.4e−02 4397L

a=2191.92 b=8.2e−02

4397R

a=2767.10 b=2.2e−02

4470L

a=2756.79 b=1.6e−02

4470R

a=2232.47 b=1.6e−01

4537L

a=2146.67 b=8.1e−02

4537R

a=2587.97 b=1.5e−01

4629L

a=2597.06 b=9.8e−02

4629R

2000

3000

4000

5000 a=2375.31 b=4.3e−02 4815L

a=2279.16 b=8.8e−02

4815R

a=2977.78 b=8.5e−02

4851L

a=2890.68 b=7.0e−02

4851R

a=2251.24 b=1.3e−01

4870L

a=2325.67 b=3.8e−02

4870R

a=2753.98 b=8.1e−02

4888L

a=2777.07 b=1.8e−02

4888R

2000

3000

4000

5000 a=2504.95 b=6.4e−02 4917L

a=2445.43 b=1.0e−02

4917R

a=1972.73 b=5.2e−02

4975L

a=2249.41 b=−8.3e−02

4975R

a=2198.18 b=6.4e−02

5015L

a=2195.43 b=5.3e−02

5015R

a=2570.94 b=2.6e−02

5027L

a=2720.55 b=−2.5e−02

5027R

2000

3000

4000

5000 a=2404.45 b=3.6e−02 5028L

a=2422.76 b=2.1e−02

5028R

a=2403.55 b=8.0e−02

5033L

a=2277.91 b=6.5e−02

5033R

a=2336.32 b=2.6e−01

5044L

a=2624.46 b=1.7e−01

5044R

a=1968.45 b=6.7e−02

5045L

a=2063.85 b=1.0e−01

5045R

2000

3000

4000

5000 a=2385.11 b=1.8e−01 5052L

a=2365.49 b=6.7e−02

5052R

a=2351.06 b=1.3e−01

5053L

a=2392.16 b=6.6e−02

5053R

a=2445.47 b=4.9e−02

5564L

a=2514.22 b=−1.1e−02

5564R

Figure 17: Individual score trajectories: For dataset N D04-08, the plots show individuals’ dissimilarity scores for the D03P algorithm. 

The x-axis indicates time between two samples. Each dot corresponds to a pair of images. Any given image, i, is involved in ni − 1

comparisons, i.e. one fewer than the number of images of the given eye. The green horizontal line shows the threshold that gives 

F N MR= 0.02 globally. The straight red line is the result of regressing d = d(T), the intercept and gradient of which appear as text. Note 

the concentration of false non-matches in individual 02463, 05044R, 04385L. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

37 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 38 

Time Between Captures (Days)

Dis−similarity Score, Algorithm I02P, Tau = 0.35

0.1

0.2

0.3

0.4

0 200 400 600 800

a=0.01 b=2.0e−05

2463L

a=0.01 b=3.0e−06

2463R

0 200 400 600 800

a=0.05 b=5.1e−05

4233L

a=0.07 b=3.8e−05

4233R

a=0.03 b=4.4e−06

4261L

a=0.00 b=2.2e−05

4261R

a=0.05 b=1.5e−05

4327L

a=0.03 b=1.2e−05

4327R

0.1

0.2

0.3

0.4 a=0.02 b=6.2e−05 4385L

a=0.02 b=3.7e−05

4385R

a=0.03 b=5.7e−05

4394L

a=0.04 b=6.8e−05

4394R

a=0.01 b=5.8e−05

4397L

a=0.02 b=−4.3e−07

4397R

a=0.12 b=5.7e−05

4470L

a=0.16 b=2.1e−04

4470R

0.1

0.2

0.3

0.4 a=0.03 b=2.6e−05 5015L

a=0.04 b=6.9e−06

5015R

a=0.15 b=2.9e−05

5033L

a=0.18 b=2.9e−05

5033R

a=0.12 b=9.0e−06

5288L

a=0.14 b=−3.2e−06

5288R

a=0.05 b=5.4e−05

5293L

a=0.07 b=5.7e−05

5293R

0.1

0.2

0.3

0.4 a=0.05 b=2.5e−05 5301L

a=0.04 b=9.8e−06

5301R

a=0.07 b=1.0e−05

5308L

a=0.07 b=3.9e−05

5308R

a=0.03 b=4.1e−05

5310L

a=0.08 b=2.9e−05

5310R

a=0.05 b=3.3e−05

5321L

a=0.06 b=2.6e−05

5321R

0.1

0.2

0.3

0.4 a=0.15 b=4.3e−05 5337L

a=0.14 b=4.4e−05

5337R

a=0.03 b=3.6e−06

5344L

a=0.08 b=2.4e−05

5344R

a=0.11 b=2.5e−06

5366L

a=0.08 b=1.5e−05

5366R

a=0.05 b=5.2e−05

5379L

a=0.06 b=8.2e−05

5379R

0.1

0.2

0.3

0.4 a=0.15 b=3.0e−05 5401L

a=0.17 b=2.6e−05

5401R

a=0.06 b=4.8e−06

5407L

a=0.09 b=−1.9e−06

5407R

a=0.08 b=4.2e−05

5408L

a=0.11 b=4.0e−05

5408R

a=0.01 b=1.8e−05

5409L

a=0.01 b=1.5e−05

5409R

0.1

0.2

0.3

0.4 a=0.07 b=4.5e−07 5415L

a=0.04 b=3.6e−05

5415R

a=0.19 b=1.3e−04

5432L

a=0.15 b=1.3e−04

5432R

a=0.05 b=3.3e−05

5450L

a=0.03 b=3.5e−05

5450R

a=0.18 b=1.1e−04

5455L

a=0.23 b=4.0e−05

5455R

0.1

0.2

0.3

0.4 a=0.16 b=5.2e−05 5456L

a=0.13 b=8.8e−05

5456R

a=0.12 b=7.6e−05

5463L

a=0.09 b=1.1e−04

5463R

a=0.02 b=5.2e−06

5486L

a=0.02 b=3.9e−05

5486R

a=0.07 b=1.7e−05

5489L

a=0.05 b=5.6e−05

5489R

0.1

0.2

0.3

0.4 a=0.06 b=3.2e−06 5496L

a=0.05 b=4.6e−05

5496R

a=0.04 b=7.6e−06

5501L

a=0.03 b=4.2e−05

5501R

a=0.11 b=2.9e−05

5513L

a=0.11 b=5.7e−05

5513R

a=0.08 b=1.1e−04

5517L

a=0.09 b=5.5e−05

5517R

0.1

0.2

0.3

0.4 a=0.09 b=1.1e−04 5531L

a=0.09 b=8.9e−05

5531R

a=0.04 b=4.2e−05

5541L

a=0.02 b=2.9e−05

5541R

a=0.06 b=4.0e−05

5555L

a=0.04 b=9.8e−05

5555R

a=0.06 b=−2.2e−06

5559L

a=0.06 b=1.2e−05

5559R

Figure 18: Individual score trajectories: For dataset N D08-10, the plots show individual dissimilarity scores for the I02P algorithm. 

The x-axis indicates time between two samples. Each dot corresponds to a pair of images. Any given image, i, is involved in ni − 1

comparisons, i.e. one fewer than the number of images of the given eye. The green horizontal line shows the threshold that gives 

F N MR= 0.02 globally. The straight red line is the result of regressing d = d(T), the intercept and gradient of which appear as text. Note 

the bimodal score distribution in panels 5432L+R, 4470L+R and 5033L+R i.e. the occurrence of H Ds near 0.4. Their use in ageing rate 

computations is not recommended irrespective of the time-lapse at which they occur. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

38 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 39 

Time Between Captures (Days)

Dis−similarity Score, Algorithm D03P, Tau = 3143.68

2000

3000

4000

5000

0 200 400 600 800

a=1791.38 b=2.6e−01

2463L

a=1884.49 b=1.4e−01

2463R

0 200 400 600 800

a=1975.08 b=4.3e−01

4233L

a=2049.10 b=3.4e−01

4233R

a=1823.84 b=1.9e−01

4261L

a=1824.02 b=2.5e−01

4261R

a=1890.29 b=2.0e−01

4327L

a=1962.43 b=8.8e−02

4327R

2000

3000

4000

5000 a=1914.93 b=4.6e−01 4385L

a=1894.48 b=3.6e−01

4385R

a=2014.70 b=4.6e−01

4394L

a=2125.17 b=4.5e−01

4394R

a=1654.78 b=8.0e−01

4397L

a=1932.93 b=1.0e−01

4397R

a=2361.78 b=2.2e−01

4470L

a=2290.27 b=2.7e−01

4470R

2000

3000

4000

5000 a=1835.42 b=2.1e−01 5015L

a=1892.56 b=1.1e−01

5015R

a=2248.08 b=2.9e−01

5033L

a=2190.88 b=3.6e−01

5033R

a=2414.03 b=2.2e−01

5288L

a=2498.63 b=2.4e−01

5288R

a=1978.05 b=5.0e−01

5293L

a=2087.02 b=5.6e−01

5293R

2000

3000

4000

5000 a=1879.88 b=1.6e−01 5301L

a=1803.96 b=1.8e−01

5301R

a=1979.90 b=2.4e−01

5308L

a=2005.62 b=5.2e−01

5308R

a=1826.60 b=5.8e−01

5310L

a=1979.21 b=4.0e−01

5310R

a=2013.64 b=2.3e−01

5321L

a=2133.51 b=1.6e−01

5321R

2000

3000

4000

5000 a=2285.44 b=4.1e−01 5337L

a=2417.45 b=4.9e−01

5337R

a=1724.08 b=3.1e−02

5344L

a=1951.91 b=1.2e−01

5344R

a=1973.18 b=1.2e−01

5366L

a=2037.65 b=7.7e−02

5366R

a=2277.15 b=3.9e−01

5379L

a=2178.69 b=6.6e−01

5379R

2000

3000

4000

5000 a=2552.62 b=2.9e−01 5401L

a=2504.19 b=3.1e−01

5401R

a=1936.26 b=−8.2e−02

5407L

a=2085.57 b=3.6e−02

5407R

a=1996.10 b=4.6e−01

5408L

a=2257.92 b=3.2e−01

5408R

a=1904.06 b=3.3e−01

5409L

a=1829.54 b=2.0e−01

5409R

2000

3000

4000

5000 a=2228.83 b=1.3e−01 5415L

a=2041.91 b=3.0e−01

5415R

a=2406.44 b=1.1e+00

5432L

a=2493.43 b=8.4e−01

5432R

a=2253.20 b=1.5e−01

5450L

a=2092.57 b=2.9e−01

5450R

a=2507.55 b=6.9e−01

5455L

a=2851.77 b=3.2e−01

5455R

2000

3000

4000

5000 a=2416.65 b=5.1e−01 5456L

a=2169.86 b=7.5e−01

5456R

a=2461.88 b=4.4e−01

5463L

a=2292.89 b=6.3e−01

5463R

a=1813.43 b=2.2e−01

5486L

a=1951.83 b=3.3e−01

5486R

a=2257.05 b=1.7e−01

5489L

a=2243.87 b=5.8e−01

5489R

2000

3000

4000

5000 a=2118.85 b=1.3e−01 5496L

a=2095.41 b=3.5e−01

5496R

a=1903.30 b=1.7e−01

5501L

a=1969.43 b=3.0e−01

5501R

a=2096.86 b=2.1e−01

5513L

a=2121.07 b=4.5e−01

5513R

a=2113.38 b=6.8e−01

5517L

a=2128.90 b=6.4e−01

5517R

2000

3000

4000

5000 a=2175.83 b=6.1e−01 5531L

a=2155.13 b=6.2e−01

5531R

a=1952.42 b=2.0e−01

5541L

a=1973.26 b=2.3e−01

5541R

a=2065.57 b=3.5e−01

5555L

a=2049.08 b=7.3e−01

5555R

a=2306.05 b=6.8e−02

5559L

a=2343.07 b=1.3e−01

5559R

Figure 19: Individual score trajectories: For dataset N D08-10, the plots show individuals’ dissimilarity scores for the D03P algorithm. 

The x-axis indicates time between two samples. Each dot corresponds to a pair of images. Any given image, i, is involved in ni − 1

comparisons, i.e. one fewer than the number of images of the given eye. The green horizontal line shows the threshold that gives 

F N MR= 0.02 globally. The straight red line is the result of regressing d = d(T), the intercept and gradient of which appear as text. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

39 
�

�

�

�

�

JU LY 11, 2013 I RE X V I - IR I S A G EI NG 40 

Threshold: The fixed green horizontal lines indicate thresholds set to give F N M R = 0.02 over the entire dataset 

(including individuals for which less than two year data was available). The exact value is not important, the goal 

being to look for trends about that line. The threshold value appears in the y-axis label. 

Regression: The straight red line indicates the result of a simple regression of dissimilarity score against elapsed 

time. It is included here for visualization and is not intended as a definitive regression model. The text shows the 

intercept, a, and gradient, b. 

Inspection of the Figures 16, 17, 18 and 19 reveals several notable effects. 

A biometric zoo: As noted previously in Figure 13 false non-match errors are concentrated in fewer than about 10 individuals for the algorithms. Some individuals’ time-series show that scores are always low (e.g. 04233, 04379, 05015, 

05045 in N D04-08). Others have large numbers of false rejections. The term “biometric zoo”[25] and “menagerie”[99] 

are used to describe error heterogeneities in biometric errors, here the F N M R concentration is described by the label “goat”. Further, note the users specific means and variance which will be associated with variables such as 

eye-openness, eye-lash prominence, pupil dilation, and human-camera interaction factors. 

Bimodal score distributions: Further some subjects (04470, 05432 for algorithm I02P and 04261, 05379L for algorithm 

D03P in the N D08-10 set) exhibit a bimodal dissimilarity score distribution. The first, lower, distribution corresponds 

to correct matches, the second higher distribution is centered where the impostor distribution would appear. The 

cause of this could be either gross segmentation failure due to non-ideal images, or erroneous ground truth (e.g. 

mis-labeled left and right eyes). A key contention of this (N I S T) paper is that progressive ageing of the iris texture 

would manifest itself in continuously increasing scores, not catastrophic failure. This is consistent with prior studies 

that show aberrant images cause outright failure[73, 92]. We recommend such images not be included in ageing 

analyses. 

Ageing candidates: Many individual eyes show evidence of elevated scores after one and two year time lapses. Notable examples are 5555R, 5517L, 5455L, 5456R, 5310R and 5379R, and these occur even without any false rejections 

at the nominal threshold. This score dependence on time without clear false non-match signifies ageing of unknown 

cause. On the other hand, 5432L+R both show inceases in scores that do culminate in false non-matches. 

The first column of Figure 20 confirms the published N D08-10 result[29] that the distribution of genuine scores undergoes 

an adverse shift with time between captures. It plots F N M R(τ ) against threshold, τ , for eight algorithms comparing short, 

intermediate, and long separation images. These curves have the same form as those reported by Fenker et al. (Figures 

2-7 in [29]) for a Neurotechnology algorithm, and Baker et al. (Figure 2 in [8]) for a Cambridge University algorithm. Two 

exceptions must be noted. The first, for algorithm F03N shows a reversal of the ageing effect. The F02P algorithm includes 

score normalization (across candidates), F03N does not. This algorithm exhibits the ageing effect only in the right tail of 

the distribution. 

Three additional algorithms, A01N, D03P, K03N, show that the one-year-separated distribution (M) is nearly indistinguishable from the two-year(L), implying that ageing is limited only to the first year between captures. This non-physical result 

prompts the following re-plotting of the data. The second column Figure 20 plots the same data for intra- and intersemester sets of comparisons, 2008-2008, 2009-2009, 2010-2010, 2008-2009, 2009-2010, and 2008-2010. The plots reveal that the 

intra-semester genuine distributions differ as would occur from an ageing effect during a semester, or due to statistical 

fluctuation. Further the 2008-2009 distribution is much closer to that of the intra-semester comparisons than is the 2009-2010 

set. This would be consistent with a material change to the collection (camera, population, procedure) between the 2009 

and 2010 collections. Possible causes of the observed ageing effects follow in the next section. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

40 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 41 

Threshold

FNMR

0.010

0.020

0.050

0.100

0.200

0.00 0.05 0.10 0.15

A01N

0.010

0.020

0.050

0.100

0.200

0.001 0.002 0.003 0.004

B02P

0.010

0.020

0.050

0.100

0.200

2000 2500 3000

D03P

0.010

0.020

0.050

0.100

0.200

10.00 10.02 10.04 10.06 10.08 F02P

0.010

0.020

0.050

0.100

0.200

25 30 35

F03N

0.010

0.020

0.050

0.100

0.200

0.65 0.70 0.75 0.80 0.85

H01P

0.010

0.020

0.050

0.100

0.200

0.1 0.2 0.3 0.4

I02P

0.010

0.020

0.050

0.100

0.200

0.00 0.05 0.10 0.15 0.20 0.25

K03N

M

S

L

Threshold

0.010

0.020

0.050

0.100

0.200

0.00 0.05 0.10 0.15

A01N

0.010

0.020

0.050

0.100

0.200

0.001 0.002 0.003 0.004

B02P

0.010

0.020

0.050

0.100

0.200

2000 2500 3000

D03P

0.010

0.020

0.050

0.100

0.200

10.00 10.02 10.04 10.06 10.08 F02P

0.010

0.020

0.050

0.100

0.200

25 30 35

F03N

0.010

0.020

0.050

0.100

0.200

0.65 0.70 0.75 0.80 0.85

H01P

0.010

0.020

0.050

0.100

0.200

0.1 0.2 0.3 0.4

I02P

0.010

0.020

0.050

0.100

0.200

0.00 0.05 0.10 0.15 0.20 0.25

K03N

0_2008_2008

0_2009_2009

0_2010_2010

1_2008_2009

1_2009_2010

2_2008_2010

Figure 20: Score distributions by time-difference and by time: For dataset N D08-10, the eight rows plot F N M R(τ ) for eight IR E X I V 

algorithms. On the left side, each panel shows three traces for S, short (fewer than 120 days), , L long (greater than 600 days) and M, 

intermediate time lapses between comparisons. Short term corresponds to within semester (2008,2009,2010), intermediate to 2008-2009 and 

2009-2010 comparisons, and long term refers to 2008-2010. The right side panels make this explicit, plotting FN M R for intra- and inter-year 

comparisons. Broadly, the left side repeats the published N D result (F N M RS <F N MRM <F N MRL. The right side reveals that one-year 

comparisons of 2009 with 2010 images give false non-matches at about the same rate as over the two years 2008 to 2010. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

41 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 42 

Fraction Images by Capture Year

Subject−ID

5559555555415531551755135501549654895486546354565455545054325415540954085407540153795366534453375321531053085301529352885033501544704397439443854327426142332463

0.0 0.2 0.4 0.6 0.8 1.0

20082009

2010

Area Quartile vs. Days (since datum)

14000 14200 14400 14600

+ + + ++ ++++ +

+ ++ ++ + + + + + + ++ + + + + + ++ + +

++ ++ ++ + + ++ ++ +

+ + + ++

+++ + + + +

+ + +

++ + + + + ++++++

+ + + + ++ ++++ + + + + + + +

+ + + + + + ++

++ + + + + +++ +++ ++ + + +++ + + + + ++ + + + + + + + +++++ +++++

+ + +

+

++ +++ + + + + + + + + + + ++ ++

++ + + +

+

++++ + + + + + + + + + + + +

+ + + + +

++ + ++

+ +

+ + ++ +

+ + ++ + ++

+

+++

++++ ++++

+ ++ + + + + + ++

+ + + + + + + + + +

+ + ++ + +++ +

+ ++ +++ ++

++ + ++ + ++ ++ +

+ ++ + + ++ ++ + + ++

++ + + + + +++ + ++++ + ++ + + ++

+ ++ + + ++ + + + + ++ + + + +

+ + + + + + ++ + + + + ++ + + + + ++ + + + +

++

++ + ++

+ ++

+++ ++ + + ++ + +

+ + +

+ +

++ + + + + ++ + +

++ +

+ + + ++++ ++

++ ++ + + ++

+ ++

+ +++ + ++ + ++

++ + ++ + +

+ +

++ ++ + + +

+ + + +++++++ + + + ++ + +++++ ++++++ + +++ ++

+ ++ +

+ ++ + + +

++ ++ ++ ++ + + +

+++ + +++++

+ + ++++++ ++++ + + +++

+++++ ++++

+

++++ +++++

++ + +++ ++ + + + + +

+ + + + + ++ +

+ ++

++ +++

+++ ++++ +

+ +

+ ++ +

++++

+ ++++ ++ + + + ++ + + + ++ + +++ + + + ++ + + + + + + + + + + +++ ++ +

++

+ + + + ++ + + +

+ + +++ + + +

+

+ ++ + ++ +++++ + + + + ++

+ ++

+ ++ + ++

+ ++ ++ + + ++++

+ + ++ ++ + + + ++

+ +++ + + + +++ +++ ++ + + + + ++ + +

++ ++ + + + + + +++ ++ +++ +++ ++ + ++ +

+

++

++ + +

++

+

+ + +++ + ++ + + ++ + + + + +++

++++ + + + ++ ++ + + + ++ +

+ +

++ + + ++ +++++ + ++++ + + + + +

+ + +

+ + + + + + + + + ++++ ++ +

+++ +++++ +

+++ ++++ ++ +

++ +++++++ +++ + +++ + + +

+ + + ++ ++ + + + +

+++ ++ ++ +

+ + + + + ++ + ++++ ++ ++ + + ++ + ++ +++ + + ++++ ++++++ +

+

+ + ++ + +++ + ++ + + ++ + + + ++ ++ + +

++ + + + +++++ + + ++ + ++ + + +

++++ ++

+

+ +

+++

+ + + +

+ ++ ++ +++ +++

++++

++ +++++++

+

++ ++++ + +

+ ++ + + + ++ +

+ + ++

++++ + + + ++ + + ++ +++ +

+ + + +++++++ ++ ++ + +++

++ + ++ +++ ++

+ + + + +

++ + + + + +

++ + + + +++ ++ +++ + ++ ++ ++ + + +

+

++ + + + +

+ + + ++ + +++ + + ++ +

++ + + + + + +++ ++ + + + +

+ + ++ + + +

+ +++ + + + + + ++

+ + + +

+ + + + ++ + + +

+ + + + + +

++ + ++ + + + + ++++ +

+ + ++

++++ ++ + ++ +++ ++ +++ +++ ++ + + +++++

+

+ + ++ + + +

+ ++ +++ + + ++ + +++ + + + +

++ +++ ++ + + + ++ + ++ + + ++ + + +++ + +

+ +

+ + + +

+ +++ ++ + + + ++ ++++ +

++

+ ++++ + +++ + +++ +

+

+

+ + +

+ + + +++

+ + + + ++

+ + + + + + +

+ ++ + + +++ + + + + + +

+ + + + ++ +

+ + +

+ + + + + ++++ + +++ ++ ++ + +

+ + + +

++ + + ++ + + ++++ + + + +++ + + +++++ + + ++ + + + ++ + + + + + ++ + +

+ + +++ + ++

+ + + + + + +

+ + +++ + + + +

+ +

+ ++ + + +

+ + + + ++ ++ + + + + ++ + ++

++ +++++ ++++ +

++ + + +++ +++++ ++

+ + + ++ + +

+ + + ++ ++ + + + ++++ + +

+ +++ + + ++ + ++++ + + ++ + ++ +

+ + + +

++ +

+

++ ++++ ++

+++++ + + + + + ++

+ +

+ +

+ + ++ ++++ + + + + +

++++++ ++ + +

+++ + + + + +

+ +

+ + + + ++ + + + +++ +

++ +

+ ++ ++

+ + +

+

+

+ ++ + + +++ + +

+ +

+ +

+ + + + + + + +

+ + +

+ +++ + ++ +

+ ++ +++ ++

+ +

+ +

+ + ++++++

++ ++++++ +

+++++ ++

+++ + ++ + + +

++++ + +++ ++ +++

+ + ++ ++ +

+

+ +

+ ++ +++ ++++ +

+

++

++ ++++++ +++

++ + +++ + + +++++ ++ +++ +

+++

+ +++ + +

++ + + + +++ ++ + +++

+ ++ +

+ + + +++ ++ + +++

+++ + + +++ + ++ + ++ + + + ++++ + + + + + + + ++ + + ++ +

+ + +

++ + + + +++++ + +

++ ++ + + +++

++++++ + +

++ + ++ + +++++ + + ++ ++ +

+ + + ++ + ++ ++ ++++++++

+

+ ++++++++

++ + + + + ++++ + + + + + + + + + + + + +

+ + + + ++ + ++ + + + + +

++

+ + + + ++ ++++ + ++ + ++++ ++

+ + + + + + +++++++ ++ + +

++ + + + ++ ++ + +

+ + +

+ + +++ + + ++ + + +++ +++

++ ++ +

+ + + +

+ ++++ + + + ++ + +++ + + + + + + ++ ++++++++++++++ + + + + + + + +

+ ++++ ++ + + +

+ ++ + +

++ + ++ + ++ +

++ +

+++ ++ + + +

+ +

+ ++ + + ++ ++

++ ++

+++++

+ ++

+

++ + +++

+ ++ + +

+ + ++ + + + +++ ++++ ++ +

+ ++ + ++ + + + + + ++ + ++ + + ++ + ++ ++ ++ ++ +++ + + + +

++ + + + + + ++ + +++ ++

+ + ++ ++ + + + ++++++++++

+ ++

+

++ + ++ ++

+ +++ + ++

+ ++ +

+ + +++++ +++++

+ +++ + + + +++ + + +++ + ++ + +++ + + +++++ + + +++ ++

++ + + ++ + + + + + + ++ ++

+ + ++ ++

++ +++

+ + + + + + ++

+ ++ + +++

+ +++ + +

+

+ + + + + + ++

++ + + + + + + +

++ + + + + + + + + + + +++ + + +++ + + + + + + ++ + +

++ +

+ +++ +

+ + ++ ++ +

+ + + ++++++++ ++ + + + +++

+++++ +++++++++ +++++

+ + + +++++++ ++++ +

+ ++++ + + ++ +

++ ++ ++

+ + + + + +++++ + +++ + +

++

+ + + ++

+++ +

++ ++ +++++ + + ++ +

+ ++ ++ + + + + ++ ++ ++ + +

+ +

+ + + ++ + ++ + + + + +

+ + ++

+++ +

++ + + ++

+ + + + + +++ + + ++ +

+ + + ++ + ++ + ++

+ ++ +++ + ++ ++ ++

+++ + ++ ++ + +++ ++

++ + ++

++

++ + + + ++ + + + + ++ ++ + +

+ +++ + + + + ++++ +++++

+ +

+

++

+ +

+ +++ + ++

+ + + + + + + ++ + ++ + ++

+ ++ + + ++ + + + ++ + + + + ++

+ +

+ + + + ++ + + + +++ + + + + ++

+ + +++++ ++

[32,75)[75,85)

[85,91)[91,1e+02]

Dilation Quartile vs. Days (since datum)

14000 14200 14400 14600

+ + + + ++

+ +

+ ++ + + + + + + ++ ++ + + + + + + + ++ ++ +++ ++

+ + + + ++ + +++ + + ++ ++ + + + + + + + +++ ++ + + +++++ +

+ + + + + + + + + ++

+ ++ + ++ ++

+ ++

+ + + ++ ++ + ++ + + +

+ +++++++ ++ + + ++ + + +

+ + + ++ + + + ++

+

++++ ++

++ + +++ ++ + + + +++ + + ++ + ++ + +++

+ + + + + ++ ++ + + + + ++ + + + ++ + + + + + ++++ +++ ++ + + ++++

++

++ +++ + ++

+ ++ ++ ++

+ +

+++ ++++ +++ + ++ +++

+++ +++ +++ +

+ ++ +

++++++ +++++ +

+ +

+++

++ ++ +

+ + ++ + ++ ++++++ + + + ++ + +++ + + + + +++++

+ ++

+ + + + + ++ ++ + + ++ ++

+ + + + + +++++

+ ++ ++ ++++ + + + +++ ++++ + +

+ + ++ + + + + + ++ +++ +

+++ + + + +++ +

+ + + ++ + + +++ ++ + +++

+ +++

+ ++ + ++

+

+ ++ + + + ++ + ++ +++ + + + +

+ + +++ + + + + + + + + +

+ + + +++++ ++ +

+ + + ++

+ + + + +

++ ++ + ++ +

+++ ++

+ +

++++

+ + +++ + ++++++ + + + +

+++++ ++++++ ++++++ ++++++ ++++ ++++ + ++++ +

+ +

+ + + + +

+ + + +

+ + +

+ + + + +

+++++ + +++ ++ + + + + ++ + + + ++ + + +

+ +++ + + +

++++ ++

+ + + + ++ + + + + + + ++ + + ++ + + + + + + +++

+++++

++++ ++ +

+ + ++++++ + ++++ ++ ++++ ++++ ++ + +

+ + + ++ + +

+ ++

+ +

+ + ++ ++ +++ +++ + +

++ + ++ + +

+ + +

+ +++ ++ ++ + ++ + + + + ++ + ++ ++

+ + + +

+ + +

+ + + + + + +

++

+ ++ +++ + + + + + ++++ +

+ + +

+ + + ++ + + + ++ + + + + + + +++ + + + + + ++ + + ++ + + + + + ++

+++++ + ++ ++ + ++++ +

+

++ + +

+++ + +++++

+++ +++ + + + + +++ +

+

++

++ +

+ ++

+ + ++++

+++ ++ + +

++ + +++++ + ++ + + + ++ + + ++ + ++ + + ++ ++++ +

+ ++ + + ++ +

+ ++++++ + + + + ++ + + + + + + + + + ++ +++ + +++++

+ + ++ + ++ + + + +++ + +++++ ++ +

++ +++ +++ ++ +++ + + + +++ + ++ +++ + + +++ + + + + + ++ + ++ +

+ + ++++

++++++++ ++

++ ++++ +

+ + ++

+ +

+++ + + + + + + + ++ ++++++++ + + +

+ + + ++ + +++ + ++ ++ + + ++ + ++ + + + + + ++ ++ + +++ +

+ + + + + ++ ++ + + + ++ +

+ ++ ++ ++ + + + + + + +++ + + + + ++ ++ ++ + + ++ ++ ++ + + + + + ++

++ + + + ++ ++ + + + +

+++ + + + + + + +

+ + ++ + ++++ ++ +

+ + + + + + +

++ +++ + +

+ +

+ +++ +++ + + + + +++++ + ++ +

+ +

+

++

+ +

+ + + +

++

++ + + ++ ++ + +++++ ++

+ + ++ ++

+ ++ +++ + + ++ + + + ++ ++ + ++ ++++ +++ + +

+++ ++++ + +++ + + + + +

++ +

+ + + + + + + + + + +

+

++++ ++ +++++++ +

+++ ++++ + +

++ +++

+ ++ + + + + + + + + + + + ++ + + +

+++ + + + +++ + ++ + +++

+++ ++ + + ++ + ++++ + + + + ++ + + + +

++ + + + + +

+++ + +

+ +

+

+ ++++++ + + + + +

++ + + + ++ + + + + + + + + +++++ + + ++++ ++ +++ ++++++ ++

+

+ ++ ++ + + + ++ +++ ++ ++ + + + ++

+

+ ++ + + + +

+ + +

++

+ +++ ++ + + + ++++++ +++

+

+++

+ +

++ ++ + + +

+ + ++ +

+ + +

+ + +++ + +++

++++ + ++++ +

+ + + +

++ + ++++ ++ +

+

++ + + +

+ ++++

+++ + + + + + + + + ++ + + ++

+ ++ +++++++

++

++++++ + ++++ +++ + +++ + + + ++++ ++ + +

+ + ++ + + +

++ +

+ ++ + ++ +

+ + + + + ++ ++

++++ +

++ ++ +++ ++

+ +++ +

++++ +++++ + +++ + + ++ ++ + ++++

+++ +++ + + ++ + +++++ + ++ + ++ + ++++

++++ ++ ++ +++ +++ ++ + ++++ + + +++++++++ + +

+ ++ ++++ + ++ + ++ + +++ + + + + +

+ +++ ++ +++ ++++ +++ ++ + + +

++++

++ + ++ ++ + + ++ ++

+ ++ + + + +++ +++++++

+++ + +

++

+ + ++ + ++ + + + ++++ + + + + + + + ++ + + +

+ ++ + +++ + +

+ +

++

+ +++ +++ + + + + + + ++++ + ++

+++ + ++ + + + + ++ +

+

++ +++ +++++

++

+++ ++ + ++++ + ++++ +

++

+ +++

++

+++ + + ++ + + +

+ + + + + + + ++++ + + +

+ + ++ +

+ + + +++ + +

+ +

+ + + + + + ++ ++++ + ++

+ + + +++ +

+ + +++++

+

+ +

+ + +

++++ + + + + + + + + ++ ++ +

++ + ++

+ + + +++++

+ + + + ++ +

++ + ++ + ++

++ + +

+ +

+ + + + + + +++ + ++++++

++ + + + + + + + + + +

+ ++ + +

+ ++ +

+++++

+ + ++ ++ + + ++ + + ++ + + + + +++

++ ++ + ++ + + ++ + + + + ++ ++ + ++ + + ++ ++ + + ++++ + + +++ +

+

+

+ + +++++ +++ + + ++ +++ + + +

+ +

+ +

+ + + ++ +++ ++ + + ++ +

+++

+ + + + + +++ ++ ++ + + +++++++++ ++++

+ +

+ + ++

++ +

+ +++ ++++ ++ +

+ + +

+ ++

+ +

+++ ++ ++

++++ ++ ++ + + + + + + ++ + + + + + ++ ++ +

+

+ +++ ++ + +++

++

+++++

+++ + +

+++ ++++ +

+

++ +

+ ++ +

+ + +++

++ +++ + + ++++

+++++ +

+ + + + + +

++++ ++ + + + + ++ + ++ + + + +++ ++ + +

+ + ++ + +

+ + +++ ++++ + + +++ +

+ ++ + +

+++++ + +++ + + +

+ + +++ ++ ++++ + +

++++ ++++++++

++++

++ + + + +

+ ++ + ++ + + +

+++ + + ++

++++++

+ + + + + + + + ++ + + + + +

+ + +++ + + + + + +++ + + +

+ + ++++++++++ + + +++

++ +++ ++++ + +

+ +++

++ +

++ + +++

+ + + + + + ++ + ++ + + + + ++ +++ + + + ++

+++ + + + + + ++ +++ ++ ++

++ + ++ + +++

+ +++++ + +

+ ++++ + + + + + ++++ + ++

+++++ + + + + + +

+++ ++ +

+ + ++ + + +++ + ++ ++++++

+ ++ ++ + + +++ + + + + + ++ + + + + + +++ + + ++ +

+ +

+ + +++

+ ++ + +++ + + + + + ++ + + + ++

[0.17,0.36)[0.36,0.4)

[0.4,0.46)[0.46,0.69]

Stage−ID vs. Days (since datum)

14000 14200 14400 14600

+ + +

+ ++ + + + +

+

+++ + + + + + + +

+ ++ +++ +++ +++ + + ++ ++ +++ +++++ + + + + + + ++ +

+ ++ + + + + + + + ++ + +

+ + +

+ + + + + + + + +

+ + + +

+

+ ++ ++ ++

+ + +

+ ++ + + +++++ ++++ +++ + ++

+

+++ +++ +

++

++++ + + + + + + + + + ++ + + +

+ + + +

+ +++ ++ + ++ +++ + +

++

+++

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+ + ++++ + + ++ + + +++ +

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12

3

Figure 21: Dilation and area by year: From left to right the four panels give a) the fraction of a subject’s images collected in each year 2008-2010, b) quartiles for each image’sexposed iris area quality estimate, c) quartiles for pupil dilation, and d) the S TA G E-I D indicator. In panels 2-4, the x-axis is absolute time since 1/1/1970 corresponding to

Spring 2008, 2009, and 2010, and y-coordinates are jittered slightly to separate the points. Increased amounts of red in panel 3 shows that pupil constriction is more commonin 2010, an aspect shown more exhaustively in Figure 22. The un-documented S TA G E-I D value is different in 2008, 2009, and 2010 and is thus a proxy for time. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

42 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 43 

Day of capture (days since 01/01/1970)

Pupil dilation (pupil/iris ratio)

0.3

0.4

0.5

0.6

14000 14600

0.31 0.31 0.28

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2463

0.42 0.40 0.37

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4233

14000 14600

0.32 0.33 0.30

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4261

0.20 0.20 0.20

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4327

14000 14600

0.38 0.39 0.36

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4385

0.38 0.39 0.33

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4394

14000 14600

0.34 0.34 0.32

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4397

NA 0.42 0.33

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4418

14000 14600

0.38 0.38 0.35

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4470

0.46 0.52 NA

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4537

14000 14600

NA 0.30 0.27

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4621

NA 0.44 0.40

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4725

14000 14600

0.40 0.44 NA

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4815

0.42 0.46 NA

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4870

NA 0.47 0.42

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5011

0.40 0.40 0.35

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5015

0.43 0.45 NA

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5028

0.42 0.39 0.31

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●

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●

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● ● ●● ●

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5033

0.42 0.43 NA

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5044

0.45 0.44 NA

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●

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●

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●

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●

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5156

0.45 0.51 NA

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5176

0.44 0.43 NA

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5213

0.41 0.40 NA

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5244

0.43 0.43 NA

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● ●●●●●●● ●

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5248

NA 0.34 0.30

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●●●●

●

●

●

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●

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●

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5270

0.41 0.42 NA

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●● ●●●●●

●

●●●

●

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5271

0.38 0.37 NA

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5284

0.52 NA 0.49

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●

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5288

0.3

0.4

0.5

0.6 0.43 0.43 0.35

●

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●

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●

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●

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5293

0.34 0.35 NA

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● ●●

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5298

0.45 0.46 NA

●●●●

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●

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5300

0.41 0.40 0.37

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5301

0.44 0.42 NA

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5303

0.51 0.51 NA

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5304

0.44 0.46 NA

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5305

0.51 0.50 NA

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5306

0.46 0.52 0.39

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5308

0.44 0.42 0.36

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5310

0.44 0.44 NA

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● ●

●

●

●

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●

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●

●

●

●

●

●

●

●

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●

●

●●

●

●

●●

●●

●

5314

0.56 0.56 NA

●

●

●●

●

●●

●●

●

●

●●

●

●

●

●●

●

●

●●

● ●

●

●

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● ●● ●

●

●

●

●

●●●●●

●

●

●

●

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●

●●

●

●

5315

0.47 0.50 NA

●

●

●

●

●

●

●

●

●

●●

● ● ●

●

●●

● ●

●

●●

●

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●

●

●

5316

0.44 0.45 NA

●

●

●

●●

●

●

●●

●

●●

●●

●●

●

●●

●

●●

● ●

● ●

●

●● ●●● ●●

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●

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● ●

●

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●

●

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5317

0.35 0.37 0.32

●

●●

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●

●

●

●●

● ●

●

●●

●

● ●

● ●

●

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●

●

●

●

●

●

●

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●

●

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●

●

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●

●

●● ● ●●

5321

0.42 0.45 0.36

●●●

●

●

●●●

●

● ●●●

●

●

●

●

●

●●

●

●●

●

●

● ●

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●

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●

●

●

●● ● ●

●

●

●

●

●

●●

●

●● ● ●

●

●

5337

NA 0.49 0.41

●●

●● ●

●

●

●

●

●

●

●

●

●

● ●

●

●

● ●●

●

●

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●

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●

●

●

●

●

●

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●

●●

●●

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●

●●

●

●

●

●

5338

0.42 0.44 0.37

●●●●

●

●

●

●

●

●

●●

●●

●●

●●

●

●

●

●●●

●

●

● ● ●●●

●

5344

0.42 0.46 0.38

●

● ●

●

●

●

●

●

●

●

●

●●

●●●

●

●

●

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● ● ●

●

●

●

●

●

●

●

●

●● ●●

●

● ●

● ●

●●

●

●

5366

NA 0.32 0.26

●●●

●

●

●● ● ●●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●●

5367

0.48 0.49 NA

●

●●●●

●

●

●●

●

●●

●

●

●●

●

●●

●

●●

●

●

● ●●●●●●

●●

●●

●●●

●

●

●

●● ●●●

●

5368

NA 0.49 0.39

●

●

●

●●●● ●

●

●●●

●●●

●

●

●●

●

●

●

●

●

●

●●

●

●●

●

●

●●

●

5375

NA 0.50 0.45

●

●

●

●● ●

●

●

●●

●

●

●●

5377

0.63 0.64 0.50

●

●

●

●

●

●●

●

●

● ●

●

●

●

●

●

●

●●

●

●

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●

●

●

●

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●

●

●

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●

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5379

NA 0.46 0.40

●

●●

●

●●●●

●●

●

●

●

●

●

●●

●

●

●

●

5380

0.45 0.41 NA

●

●●

●

● ●●

●●●

●

●●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

5382

NA 0.27 0.27

● ● ●●

● ●

● ●●●

●●● ●

● ●●●●●●●

●

●●●

●●

5393

0.42 0.46 NA

●●

●

●●

●

●

●

●

●

● ●

●

● ● ●

●●

●

●

●●

●

●

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●

●●

●●

●

●

●

●

●

● ●●●●

●

●

5399

0.3

0.4

0.5

0.6 0.45 NA 0.36

●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

●●

●

●

●

●

●●

●

5401

NA 0.62 0.57●

●●

●

●

●

●

●

●

●●

●●

5403

0.51 0.52 NA

●

●

●●●●

●●

●

●

●

●

●

●●●

●

●

●●●●

●●

●

● ●

●

●● ● ●

●●

●●

●

5405

0.45 0.48 NA

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

● ●●

●

●

●

●

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●

● ●

●

●●

● ● ●

●

●

●

●

●

●

●

● ● ●

●●

●

5406

0.41 0.45 0.39

●

●

●

●

●

●●●●

● ●

●

● ●

●

●

● ●●

●

●

●

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● ●

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●

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●

●

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●

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●

●

●

●

●

●

●

●

●

●

●● ●

●

●

●

5407

0.50 0.49 0.44

●●●

●●

●

● ●

●

●

●

●

●●

●

●

●●●

●

●

●

●

●

●

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●

●

● ●

●

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●

●●

●

●

●

●

●

●●

●

●

●

5408

0.36 0.38 0.34

● ●

●● ●

●●

● ●●●

●

●

●

●

●●●●●

●

●

●

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●

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●

●

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●

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●

●

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●

5409

0.33 0.38 0.33

●●

●●

●

●

●

●●

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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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●

5415

0.52 0.52 0.39

●

●

●●●

●●

●

●

● ●●

●●

●●●

●

●

●●

●●●

●●

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●

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●

●

●

●

●

●●●●●●● ●● ●

●●●●● ●● ●● ●●●●●●

●

●

●

●●●

●

5432

0.44 0.47 NA

●

●●

●

●

●

●

●

●●

●●

●

●

●

●

● ●●

●

● ●

●

●

●

●

●

●

●●

●

● ●

●●●

● ●●

●

● ●

●

●

● ●

●●

●●

5442

0.50 0.48 NA

●

●

●

●

●

●

●

●

● ●

●

●●

●●

●●

●●●

●

●

●●

●●

●

●

●●

●

5445

0.42 0.45 NA

●

●

●●●

●

●●

●●●

●

●

●

●●●

●

●

●●

●

●

●●

●

●

●

●

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●

●

●

●

●●

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●

● ●●●

●

5448

0.42 0.42 0.40

●●●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

● ●

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●

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● ● ●

●

●

●

●

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●

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●

●

●

●

● ●● ●

●

●●

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●

●

●

●

●

●

●●

●

●

●●

●

●●

5450

0.62 0.63 0.54●

●

● ●

●

●●

●

●

●●

●●

●●

●●●

●

●

●

●●

● ● ● ●

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● ●

●

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●

● ●●

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●

●

●

●●

● ●

●●●

●

●

●●●

●

●

●● ●

●

●

●

5455

0.48 0.47 0.36

● ●

●

●

●

●●●

●

●

●

●

● ●

● ●

●

●●

●

●

●

●●

● ●●

● ●

● ● ●

●

●

●

●

●

●

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●

●

●

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●

●

●●● ●

●

● ●●● ●

●

●

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●

●

●●

●

● ●

●

●

●

●

●

●

●●●

●

●

● ●

● ● ●

●●

5456

0.43 0.41 NA

●

●●●●

●

●●●

●

●●

●●

●

● ●

●

● ●● ● ●●

5460

0.49 0.49 0.45

●●

●● ●

●

●●

●

●

●

●

●●●●

● ● ● ●

●● ●

●●

● ●●

●

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●

●

●

●●●● ●●●●

●

●

●

●

● ● ●

●

●

●●●●●

●●●

●

●

●

● ●

● ●●

●●● ●●●

●

5463

0.41 0.40 NA

●

●●

●

●

●

●●

●

●●

●

●

●

●

●

●●● ●

●

● ● ●

●

●

●

●

●

● ● ●●●

●

●

●●

●

●

●

●●

● ●

●●

● ● ●

●

●●

5465

NA 0.54 0.44

●

●

●●

●●

●●

●●●●

●

●

●

●●●●●●●●●

●

●●

5473

0.44 0.45 NA

●

●

●

●

●

●

●

●

●

●

●

●

●

●● ●●

●●

●

●

●

●

●

●

●

●

●

● ●

●

●

●●

●

●

●

●

● ●

●

●

●●

●●

5477

0.46 0.43 NA

●

●●

●

●

●

●

●

●

●

● ●

●●●

●

●

●

●

●

●

●

●

●

●

●

●●

● ●

●

●

●

●

●

●

●

●● ● ●

●

●

●●

5478

0.47 0.46 NA

●

●

●

●

●

● ●

●

●

●

●

● ●

●

●

●

●

5480

0.40 0.41 NA

●●●

● ●

●

●

●●●

●●

●

●

●

●●●

●

●

●

●●●●

●

●

●

●

● ●

●●

●

5481

0.44 0.40 NA

●●

●●● ●●

●

●

●●●●

●

●

●

●

●●

●● ●

●●●

● ● ●

●

●

●

●

●

●

●

●

●

5482

0.55 0.53 NA

●

●

●●●●

●

●

●

●●●

●●

●

●

●●

●

●

●●

●●

●

●

●

●

●

●

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●

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●

●

●

5484

0.43 0.49 NA

●●●

●●

●

●●

●

●●●

●

●

●

●

●

●●

●

5485

0.46 NA 0.36

●●

●●

●

●●

●●

●

●● ●

●

●

●

●

●●●

●

●

●

●●

●●

5486

0.40 NA 0.34

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●

●●

●●

●

●

●

●

●

●●

●

●●●

●

●●

●

5489

0.3

0.4

0.5

0.6 0.49 0.53 NA

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●●

●

● ●●●

●

●

5490

0.49 0.51 NA

●

●

●

●

●

● ●●

●

●

●

●

●

●●●

●●

●

●

●

●

● ●

● ●

●

●

●●

●

● ● ● ●

●

●

●●

5491

0.46 0.48 NA

●

●●

●

●

●

●●

●

●

●

●●

●●

●

● ●●

●

●

●●

●

●

●

5493

0.49 0.54 0.41

●

●●

●●

● ●●

●●

●

●

●●●

●

●

●

●●●●

●

●

●●

●

●

●

●

●●

●

●

●●

●

●

●

●●

●●

●●

●●

●

●

●

●

●

●

●

●●

●●

●●●●

●●

●

● ●

●

●

●

●

●

●●

●

5496

0.57 0.57 NA ●

●

●

●

●

●

●●

●

● ●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

● ●

●

●

●

●

● ● ●

●

●

●

5498

0.37 0.40 NA

●

●

●●

●

●

●

●

●

●

● ● ● ●

●●●

●●

●●

●

●

●●●

●

● ●●

●

●

●●

●●●

●

●

●

●

5500

0.39 0.40 0.35

●

●

●●

●● ●● ●

●

●

●

●

●●

●

●

●

●

●●

● ● ●

●

●

●

●

● ● ●

●

●

●●

●

●

●

●●

●

●

●

● ●●

●

●● ●

●

●●●

●

●

●

●

●

●●●

●

●● ●●

●

●

●●

●●●●

●

●

●

●●

●●

●

5501

0.48 0.48 NA

●

●

●●

●

●●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

●

● ●

●

●

●●

●

●●

●

●●

●

●

●

●

●

●

●

●

● ●

●

●

●

●●

●●

5502

0.46 0.43 NA

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●●●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●● ●

● ● ●

●●●

●

●

5511

0.41 0.42 NA

●

●●●

●

●●●●

●●

●

●

●

●●

●

●

●

● ● ● ●

●

●

●

●

● ●

●● ●

●

●

●

●

●

●

●

●

●

●

●

● ●

●

●

●●●

5512

0.50 NA 0.48

●

●●

●

●

●

●

●

●

●●

●●

●

5513

0.48 NA 0.40

●

●

●●

●●

●●●● ●●

●

●

● ●

●

●

●

●

●●

●●

●

●

●

●

●

●

5517

0.41 0.40 NA

●

●

●●

●● ●●

●●

●

●

●●

●●

●● ●

● ●

●●

●●

●●●

●

●

●

●● ●

● ● ● ●●●

●

●

5518

NA 0.50 0.44

●

●

●

●

●

●

● ●

●

●

●

●

●●

●

●●●

●

●

●●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●●

●

●●●●

●

●

●

●

●

●●

●●

●

●

5522

0.45 0.50 NA

●

●

●

●

●

●

●

●

●

●●

●

5525

0.42 0.46 NA

●●

●

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●●

●

5530

0.56 0.54 0.46

● ●

●

●

● ● ●●● ●

●

●

●

●● ●●

● ● ●

● ●●●

●

●

●

●

● ●

●

●

●●

●

●

●

●

●

● ●

●●●

●●

●

● ●●●

●

●●

●●

● ● ● ●● ●

●

●

●

5531

0.45 0.51 NA

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●●

●

●●

●

●

●

●

●

●

● ● ●●

●

●

●

●

●

●

●

●

●

●●

5540

0.46 0.48 0.40

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5541

0.41 0.40 NA

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5545

0.42 0.39 NA

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●

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5547

0.54 0.55 0.47

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5555

0.41 0.39 0.33

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5559

0.43 0.45 NA

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●

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5562

NA 0.52 0.46

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5601

NA 0.52 0.43

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5603

NA 0.62 0.48

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5611

NA 0.45 0.41

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5615

0.3

0.4

0.5

0.6 NA 0.48 0.43

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5616

NA 0.46 0.43

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5618

NA 0.41 0.35

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5619

NA 0.44 0.36

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5622

NA 0.43 0.34

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5623

NA 0.49 0.44

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5624

NA 0.58 0.53

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5626

NA 0.44 0.36

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5629

NA 0.38 0.35

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5635

NA 0.49 0.43

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5636

NA 0.44 0.39

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5638

NA 0.38 0.32

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5642

NA 0.57 0.44

●

●

●

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●

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5648

NA 0.51 0.40

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5656

NA 0.57 0.47

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5658

NA 0.47 0.40

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5659

NA 0.37 0.32

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5663

NA 0.51 0.45

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5664

NA 0.48 0.38

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5668

NA 0.45 0.40

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5669

NA 0.55 0.46

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●

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5671

NA 0.54 0.40

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●

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●

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5672

NA 0.47 0.38

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5673

NA 0.56 0.48

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●

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5674

NA 0.50 0.46

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5675

NA 0.57 0.43

●

●

●

●

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●

●

●

●

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●

●

5679

NA 0.51 0.42

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5680

NA 0.43 0.37

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5695

0.3

0.4

0.5

0.6 NA 0.51 0.45

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●

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5696

NA 0.52 0.44

●

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5697

NA 0.41 0.37

●

●

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●

●

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5704

NA 0.37 0.34

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5705

NA 0.57 0.52●

●

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5708

NA 0.51 0.39

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●

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5713

NA 0.41 0.32

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5714

NA 0.48 0.41

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5723

NA 0.46 0.41

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5724

NA 0.42 0.32

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5730

NA 0.45 0.35

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5731

NA 0.49 0.40

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5733

NA 0.48 0.40

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5734

NA 0.46 0.36

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5749

NA 0.43 0.38

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5752

NA 0.55 0.48

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5760

NA 0.50 0.41

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5761

NA 0.51 0.48

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5764

NA 0.41 0.34

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5766

NA 0.47 0.42

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●

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5774

NA 0.56 0.49

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5775

NA 0.44 0.35

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5786

NA 0.51 0.43

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5787

NA 0.42 0.37

●●●●

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5788

NA 0.54 0.45

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5794

NA 0.56 0.46

●

●

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5795

NA 0.52 0.42

●

●

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●

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5796

NA 0.52 0.44

●

●

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●

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●

●

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● ●

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● ●

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5797

0.3

0.4

0.5

0.6 NA 0.47 0.35

●

●

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● ● ●

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●●

●

● ●

●

●●

●

●

●

●

●●

●●

●

5801

NA 0.55 0.47

●

●

●

●

● ●

● ●

● ●●

●

5803

NA 0.41 0.36

●●●

●

●

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●

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●

●

● ●

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●

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●●

●

●

●

5804

NA 0.49 0.41

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●

●

●

● ●●

●

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●

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●

5805

NA 0.48 0.36

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●

●●

●●●●

●

●

● ●

●●●

●

●●

●●

● ● ● ● ●●

●●

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●● ●●

●

●●●

●

●

●

● ●

●●

●● ●

● ●

●

●●

●●

5808

NA 0.57 0.50

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●

●

●

●

● ●●

●

●

●

●● ●●

●

●

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●

●

●

●

●

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●

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●

●

●

●● ●

●

●

●

●

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5809

NA 0.47 0.41

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●

●●

●

● ●

●

●

●●●

●

● ● ● ●

●●●

● ●

●

●

● ●●

●

●

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●

●

●●

●

●●●

●

●●

●

●

5810

NA 0.60 0.49

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●

●●

●

●

● ●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

● ● ●

● ●

●

●

●

●

●

●●

●●

●

●●

●

●●●●

●●

5812

NA 0.59 0.46

●

● ● ●

●●●

●●

●

●

●

● ● ●●

●●●

●

●●●

●

●●● ●

●●

●

●

●

●

●●●

●● ● ●

●

●

●

●

●

●

● ●●●●

●●● ●

●●

●●●

●●●

5815

NA 0.49 0.42

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●

●

●

●

●

●●

●

●

●●

●

●

● ●

●

●

●

●●

●

●

●

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●

●●

●●

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●

●

●

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5824

NA 0.42 0.36

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●

●●

●

●

●

●

●

●

●

●

●●

●

●

●●

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●

5830

NA 0.44 0.34

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●

●

●●

●●

● ●

●

● ●●

● ●

●

●●● ●●

●

● ●

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●

● ●

● ●●●●

●

●

●● ● ● ●●

●

●

●●●

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●

●

●●

●

5831

NA 0.59 0.42

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●

●●

●

●

●●●

●

●

●

●

●●

●

●●

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●

●

●

●

●

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●

●

●

●

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●

●

●

●●

●

●

●

5832

NA 0.40 0.34

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●

●

● ●

●●● ●●

●

● ●●●●

●●● ● ●●

●

●●●

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●

● ●

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●

●●

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5833

NA 0.43 0.39

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●

●

●

●

● ●

●

●●

●●

●

●●

●

●

5835

NA 0.37 0.29

●●

●●

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●

●●●●

●

5837

NA 0.51 0.44

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●

●

● ●

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●

● ● ● ● ●

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●

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5842

NA 0.49 0.43

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●

●

●●

●● ● ●● ●

●

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●

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● ● ●

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● ●

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5844

NA 0.44 0.32

●●

●

●

●

●

●

●

●

●

●

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5845

NA 0.42 0.39

●● ●

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●●

● ● ●●

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5848

NA 0.59 0.47

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5849 ●

NA 0.52 0.42

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5850

NA 0.46 0.39

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5853

NA 0.39 0.34

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5854

NA 0.55 0.50

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5856

NA 0.52 0.38

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5857

NA 0.57 0.50

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●

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●

●

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●

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●

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●

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5858

NA 0.37 0.32

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5859

0.3

0.4

0.5

0.6 NA 0.52 0.42

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5863

NA 0.53 0.45

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●

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●

●

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5868

NA 0.55 0.42

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●●●●●

●●

●●

●

●●

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●

●

●●

●●

●

●

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●

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5871

NA 0.44 0.39

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● ●

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5875

NA 0.64 0.57●

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●● ●● ●●●●●

● ●●

●●●● ● 5880

NA 0.46 0.34

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●

●● ● ● ●

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5881

NA 0.42 0.39

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●

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● ●●

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5884

NA 0.52 0.40

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5892

NA 0.39 0.33

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5893

NA 0.40 0.37

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5906

NA 0.47 0.42

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5907

NA 0.39 0.34

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5912

NA 0.56 0.43

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5917

NA 0.47 0.44

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5919

NA 0.43 0.34

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5923

NA 0.48 0.33

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5930

NA 0.52 0.44

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5936

NA 0.57 0.46

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5937

NA 0.49 0.39

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5953

NA 0.53 0.39

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5956

NA 0.50 0.35

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5963

Figure 22: Dilation over time: (This figure is best viewed on a computer screen with software magnification). For all 217 individuals in 

dataset ND08-10, the panels show absolute pupil dilation for all images of each person against date of capture. The clusters correspond 

to collections in the Spring of 2008, 2009 and 2010. Some individuals only appear in two of those years. The dilations are consensus 

estimates per equation 2. The straight red line is the result of regressing D = D(T). The text in each box gives the median dilation over 

all images collected from an individual in that calendar year. Note both inter- and intra-personal variation. Population medians are 

highest in 2009 and lowest in 2010. The reasons for these systematic changes in dilation are unknown. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

43 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 44 

Time Between Captures (Days)

Dilation Difference

0.0

0.1

0.2

0.3

0.4

0 500 1000 1500

a=0.05 b=2.0e−05

2463L

a=0.04 b=−9.9e−08

2463R

0 500 1000 1500

a=0.10 b=1.4e−05

4233L

a=0.10 b=−7.4e−06

4233R

a=0.04 b=2.5e−06

4261L

a=0.04 b=−5.0e−07

4261R

a=0.06 b=6.1e−06

4385L

a=0.06 b=8.4e−06

4385R

0.0

0.1

0.2

0.3

0.4 a=0.06 b=−3.2e−07 4397L

a=0.04 b=2.4e−06

4397R

a=0.07 b=−5.8e−06

4470L

a=0.05 b=−4.6e−06

4470R

a=0.09 b=4.1e−05

4537L

a=0.07 b=3.5e−05

4537R

a=0.09 b=2.7e−06

4629L

a=0.09 b=−9.5e−07

4629R

0.0

0.1

0.2

0.3

0.4 a=0.07 b=1.2e−05 4815L

a=0.07 b=1.4e−05

4815R

a=0.14 b=−1.0e−06

4851L

a=0.11 b=−7.6e−06

4851R

a=0.09 b=1.2e−05

4870L

a=0.09 b=5.1e−07

4870R

a=0.07 b=1.7e−05

4888L

a=0.07 b=−4.7e−06

4888R

0.0

0.1

0.2

0.3

0.4 a=0.06 b=1.7e−07 4917L

a=0.06 b=4.5e−06

4917R

a=0.06 b=5.8e−07

4975L

a=0.07 b=−7.7e−06

4975R

a=0.07 b=2.5e−06

5015L

a=0.07 b=−4.9e−06

5015R

a=0.09 b=−1.2e−05

5027L

a=0.08 b=−1.4e−06

5027R

0.0

0.1

0.2

0.3

0.4 a=0.07 b=−2.7e−06 5028L

a=0.08 b=−1.0e−05

5028R

a=0.07 b=1.1e−05

5033L

a=0.07 b=9.4e−06

5033R

a=0.07 b=2.0e−05

5044L

a=0.09 b=1.3e−05

5044R

a=0.09 b=−1.0e−05

5045L

a=0.09 b=−1.4e−05

5045R

0.0

0.1

0.2

0.3

0.4 a=0.08 b=−2.3e−05 5052L

a=0.06 b=2.4e−05

5052R

a=0.06 b=9.8e−06

5053L

a=0.05 b=6.1e−06

5053R

a=0.09 b=−1.1e−05

5564L

a=0.10 b=−3.1e−06

5564R

Figure 23: Dilation differences over time: For dataset N D04-08, each panel shows dilation differences for all pairs of images of one eye 

vs. time between two samples. The dilations are consensus estimates per equation 2, and the dilation difference is the radial iris texture 

thickness ratio of equation 4. The straight blue line is the result of regressing ΔD = ΔD(T), the intercept and gradient of which appear 

as text. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

44 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 45 

4.3.1 Causes of longitudinal change in N D08-10 

For the 40 subjects in the N D08-10 population that participated in both 2008 and 2010, Figures 22 and 23 plot time-series 

of the eq. (2) dilation difference estimates. These data suggest an explanation of the trends observed in the dissimilarity 

score time-series: namely that changes in dilation cause changes in scores. The third panel of Figure 21 depicts dilation 

as a function of date, and this reveals that some individuals have consistently different dilations across the three year 

collection. For example, some pupils of individual 05033 constrict from D > 0.46 to D < 0.36. Moreover the pupils of 

most persons in 2010 are less dilated that those in 2008. This is evident in Figure 24 which shows all the images of the left 

eye of individual 05455; note the irises are all well centered with specular reflections located consistently inside the pupil. 

However, high dilation in 2008 and 2009 reduces markedly in 2010 (yet still remains in the top quartile in Figure 21). 

Another covariate affecting recognition is the S TA G E -I D taken from the N D metadata provided with the images. This value 

is not documented specifically but is used in a larger metadata framework for documenting biometric data collections to 

denote location or setting. However, we do not further consider this annotation because, as revealed in Figure 21, it is a 

perfect proxy for the year of capture. 

Finally we use quantitative statements of exposed iris area. These are values produced by the C4X iris image quality 

assessment algorithm28 submitted to I R E X I I[87]. This algorithm, from University of Cambridge, takes a single iris image 

and renders a quality score on [0, 100] with bigger-is-better semantics. While its internal definition is proprietary, I R E X I I 

and Figure 25 show this to have high correlation with observed dissimilarity scores and false rejection errors. We consider 

it to be monotonically related to actual exposed iris area. Given values, A1 and A2, for a mated pair of images we form a 

summary measure, 

min(A1, A2) ΔA = (9) 100

as the minimum of the two values scaled to [0, 1]. This is done on the basis that successful genuine comparison will be 

subverted by low exposed area in either of the two samples. This measure does not capture the overlap area common 

to both images. Referring to the images of individual 05456 in Figure 26, the irises are all well centered with specular 

reflections located consistently inside the pupil. However, area differences are evident for example in images 1001, 997, 

971, 900, and 60. Note that while Baker[8] excluded poor images from the 2004-2008 set by “manually screening for image 

quality ... e.g. out-of-focus irises, major portions of the iris occluded ...”, Fenker did not consider this necessary in 2008­

2010 given the LG4000 superiority. It is clear from the image-specific F N M R values above each image that errors and 

non-idealities 29 do occur. 

We therefore consider three quantitative adjustments to the scores to remove the effects of exposed iris area, dilation difference, and both. Specifically, given an observed dissimilarity score, dij , from the j-th comparison of the i-th individual, 

28Quality algorithms map an input image into either a summary scalar quality value, or into a vector of image quality measures. Operationally the 

summary measure is useful because it is interpreted as measure of suitability of the image for recognition by a specific or generic unspecified recognition algorithm. If quality is low, a recapture is initiated. A quality vector is comprised of specified image properties each of which is taken as a 

measure of recognition accuracy. The elements of an iris quality vector are being formally standardized as ISO/IEC 29794-6 along with prescriptions 

for their computation. Examples of such elements are axial gaze angle, exposed iris area, and focus. The precision and efficacy of quality assessment 

algorithms has been assessed in IREX II[87]. Quality measures are of interest here because they operate on single images and therefore do not make 

a statement of dissimilarity between time-separated irises. Their application can reveal longitudinal changes in salient image properties. Commercial 

quality assessment algorithms exist for face, fingerprint and iris images. 

29 04233L: This eye appears to be wide open in 2008 and 2009, but with the upper eyelid approaching the pupil in 2010. 04261L: This individual has 

contact lenses, and an overall 16.6% F N M R. Image 996 (2008) exhibits ptosis. Images 1030 (2008) and 1601 (2010) are rotated. 04385L: Prominent eye 

lashes, but low F N M R everywhere except image 2245 (2010) which has mild motion blur. 04397L: One image 2059 (2010) has off-axial gaze. 04870L: 

Variable, though not extreme, dilation throughout. 05028L: Has eye lash occlusion throughout, some off-axis, but low error rates. 05033L: Has lower 

pupil dilation in 2010 than in 2008 and 2009. High error rates due to eyelash occlusion. 05044L: Exhibits variable dilation and eye openness. Eyelid 

motion in image 1259. 05293L: Eyes more open in 2010. Reduced dilation in 2010. 05300L: Image 393 (2009) is far off axis with iF N M R is 0.655. 05310L: 

Lower dilation in 2010 than in prior years. But low F N M R. Amount of light reflected from skin below eye is less in 2010. 05321L: Amount of light 

reflected from skin below eye is less in 2010. 05337L: Reduced dilation in 2010 05366L: Reduced dilation in 2010 05379L: Very high dilation in 2008 and 

2009. Reduced in 2010. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

45 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 46 05455L : Global fnmr 0.173 K03N 0.187 A01N 0.291 F03N 0.000 I02P 0.231 F02P 0.165 H01P 0.160 D03P 0.188 B02P 0.165 52 0.106 54 0.096 56 0.160 57 0.096 59 0.048 61 0.054 75 0.106 77 0.247 79 0.311 457 0.071 459 0.071 461 0.042 487 0.199 489 0.205 491 0.545 517 0.186 519 0.397 521 0.179 570 0.170 572 0.035 574 0.122 612 0.163 614 0.163 616 0.167 835 0.324 837 0.131 839 0.157 865 0.071 867 0.122 869 0.138 893 0.407 895 0.160 897 0.109 923 0.391 925 0.090 927 0.436 953 0.096 955 0.042 957 0.173 983 0.141 985 0.167 987 0.183 Figure 24: One eye over time: Images of the left eye of person 05455 in the ND08-10 dataset. The pink shading indicates 2008, blue 2009 and green 2010. The images are ordered in time according to the sequence number which appears above each image. Three classes of false non-match rate F N M R are given, all computed when the threshold is set to give FN M R

= 0.02 over the entire dataset. The first row states FN M R for images of this eye (i.e. mean F NM R over eight recognition algorithms). The second row gives

eye-specific F N M R by algorithm. Above each image is image-specific error rate (iF N MR - the proportion of genuine comparisons involving an image that result in a false

non-match[88]. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

46 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 47 

Pairwise Minimum of Exposed Iris Area Quality Scores / 100

Dissimilarity Score

0.00

0.05

0.10

0.15

(0.227,0.292](0.292,0.356](0.356,0.421](0.421,0.486]

(0.486,0.55](0.55,0.615](0.615,0.68](0.68,0.744]

(0.744,0.809](0.809,0.874](0.874,0.938]

(0.938,1]

A01N

0.001

0.002

0.003

0.004

(0.227,0.292](0.292,0.356](0.356,0.421](0.421,0.486]

(0.486,0.55](0.55,0.615](0.615,0.68](0.68,0.744]

(0.744,0.809](0.809,0.874](0.874,0.938]

(0.938,1]

B02P

1000

1500

2000

2500

3000

D03P

10.00

10.02

10.04

10.06

10.08

F02P

0

10

20

30

40

F03N

0.5

0.6

0.7

0.8

0.9

H01P

0.0

0.1

0.2

0.3

0.4

I02P

0.00

0.05

0.10

0.15

0.20

0.25

K03N

Figure 25: Influence of exposed iris area: For the ND08-10 dataset, the dependence of algorithms’ comparison scores on iris area. 

Specifically the x-axis gives binned values of the lesser of Daugman’s exposed iris area quality scores computed from the two images 

using the C4x implmentation submitted to I RE X II IQ CE[87]. These quality scores are proprietary but are assumed to be a monotonic 

function of the exposed iris texture area as a fraction of its maximum. Another variable, area of overlap of the two irides, is not available. 

the adjustments subtract fixed proportions of the dilation change 

d' = dij − βi2ΔDij (10) ij

and the exposed iris area minimum ΔA, 

d' (11) ij = dij − βi3ΔAij

and their joint effect 

d'

ij = dij − βi2ΔDij − βi3ΔAij − βi4ΔDijΔAij (12) 

where the coefficients β could come from many sources (e.g. first principles, anatomic models, exhaustive search), or in 

this case from ordinary least squares (O L S) regression using the models 

dij = βi1 + βi2ΔDij +eij (13) 

dij = βi1 + βi2ΔAij +eij (14) 

dij = βi1 + βi2ΔDij + βi3ΔAij + βi4ΔDijΔAij +eij (15) 

where eij is an error term representing our failure to measure all causal reasons for comparison score variation. Here O L S 

is applied to each eye individually such that the βi are specific to one eye. Note the adjusted d' values are not predictions 

from regression models because they include the residuals. Rather, the approach is equivalent to using the constructed 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

47 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 48 05456L : Global fnmr 0.088 K03N 0.090 A01N 0.064 F03N 0.000 I02P 0.062 F02P 0.000 H01P 0.121 D03P 0.127 B02P 0.239 101 0.028 103 0.028 60 0.049 62 0.038 64 0.047 65 0.035 67 0.032 69 0.355 81 0.050 83 0.016 85 0.044 99 0.034 497 0.195 499 0.262 501 0.140 504 0.029 506 0.047 508 0.026 557 0.031 559 0.048 562 0.038 564 0.020 566 0.032 592 0.142 594 0.049 596 0.047 645 0.029 647 0.032 649 0.023 1001 0.070 843 0.154 845 0.108 847 0.064 870 0.071 872 0.074 898 0.067 900 0.198 902 0.084 937 0.070 939 0.259 941 0.113 967 0.049 969 0.073 971 0.541 997 0.035 999 0.052 Figure 26: One eye over time: Images of the left eye of person 05456 in the ND08-10 dataset. The pink shading indicates 2008, blue 2009 and green 2010. The images are ordered in time according to the sequence number which appears above each image. Three classes of false non-match rate F N M R are given, all computed when the threshold is set to give F NMR

= 0.02 over the entire dataset. The first row indicates FN M R for images of this eye over all eight recognition algorithms. The second row gives eye-specific

F N MR by algorithm. Above each image is image-specific error rate (iF N M R - the proportion of genuine comparisons involving an image that result in a false non-match[88]. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

48 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 49 

Threshold

FNMR A01N

0.2

0.4

0.6

0.8

1.0

0.00 0.01 0.02 0.03 0.04

ACTUAL

DIL

Threshold

FNMR B02P

0.2

0.4

0.6

0.8

0.0005 0.0010 0.0015

ACTUAL

DIL

FNMR D03P

0.2

0.4

0.6

0.8

1.0

1600 1800 2000 2200 2400 2600

ACTUAL

DIL

FNMR F02P

0.2

0.4

0.6

0.8

10.00 10.01 10.02 10.03 10.04

ACTUAL

DIL

FNMR F03N

0.2

0.4

0.6

0.8

25 30 35

ACTUAL

DIL

FNMR H01P

0.2

0.4

0.6

0.8

0.65 0.70 0.75 0.80

ACTUAL

DIL

FNMR I02P

0.2

0.4

0.6

0.8

0.00 0.05 0.10 0.15

ACTUAL

DIL

FNMR K03N

0.2

0.4

0.6

0.8

1.0

−0.02 0.00 0.02 0.04 0.06

ACTUAL

DIL

0_2008_2008

0_2009_2009

0_2010_2010

1_2008_2009

1_2009_2010

2_2008_2010

Figure 27: Score distributions with and without dilation effects: For dataset N D08-10, the eight panel pairs plot FN M R corresponding 

to the entire the genuine distribution, 0 ≤ F NMR(τ ) ≤ 1 for eight I R E X IV algorithms applied to the images of all eyes. The pairs 

show fractions of raw dissimilarities and the dilation-adjusted values (per eq. 10) that are above threshold. In each panel the six traces 

correspond to within semester (2008,2009,2010) (in pink-red), one year separated 2008-2009 + 2009-2010 (in blues), and two year separated 2008­

2010 (in yellow) comparisons. Clustering of lines indicates that the genuine distribution is stable over 0, 1 and 2 year intervals, consistent 

with a no-ageing effect. Column 2 shows dilation to be the major source of time variation. The K03N distribution is unusual because the 

algorithm emits many 0 scores. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

49 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 50 

Threshold

FNMR A01N

0.010

0.020

0.050

0.100

0.200

0.00 0.02 0.04 0.06 0.08 0.10

ACTUAL

DIL

0.00 0.02 0.04 0.06 0.08 0.10

AREA

DIL_AREA

FNMR B02P

0.010

0.020

0.050

0.100

0.200

0.0005 0.0010 0.0015 0.0020 0.0025 0.0030 ACTUAL

DIL

0.0005 0.0010 0.0015 0.0020 0.0025 0.0030 AREA

DIL_AREA

FNMR D03P

0.010

0.020

0.050

0.100

0.200

2000 2500 3000

ACTUAL

DIL

2000 2500 3000

AREA

DIL_AREA

FNMR F02P

0.010

0.020

0.050

0.100

0.200

10.03 10.04 10.05 10.06 10.07 10.08

ACTUAL

DIL

10.03 10.04 10.05 10.06 10.07 10.08

AREA

DIL_AREA

FNMR F03N

0.010

0.020

0.050

0.100

0.200

30 35 40

ACTUAL

DIL

30 35 40

AREA

DIL_AREA

FNMR H01P

0.010

0.020

0.050

0.100

0.200

0.70 0.75 0.80 0.85

ACTUAL

DIL

0.70 0.75 0.80 0.85

AREA

DIL_AREA

FNMR I02P

0.010

0.020

0.050

0.100

0.200

0.05 0.10 0.15 0.20 0.25

ACTUAL

DIL

0.05 0.10 0.15 0.20 0.25

AREA

DIL_AREA

FNMR K03N

0.010

0.020

0.050

0.100

0.200

0.00 0.05 0.10 0.15 0.20

ACTUAL

DIL

0.00 0.05 0.10 0.15 0.20

AREA

DIL_AREA

0_2008_2008

0_2009_2009

0_2010_2010

1_2008_2009

1_2009_2010

2_2008_2010

Figure 28: Score distributions with and without dilation and area effects: For dataset N D08-10, the rows plot FN M R(τ ), corresponding 

to the right tail (top 20%) of the genuine distribution. for eight I R E X I V algorithms applied to images of all eyes. The four columns show: 

the raw dissimilarities from the algorithms; the dilation-adjusted values per eq. 10; the iris area adjusted values per eq. 11; and the 

dilation-and-area adjusted values per eq. 12. In each panel the six traces correspond to within semester (2008,2009,2010) (in pink-red), one 

year separated 2008-2009 + 2009-2010 (in blues), and two year separated 2008-2010 (in yellow) comparisons. Clustering of lines indicates that 

the genuine distribution is stable over 0, 1 and 2 year intervals, consistent with a no-ageing effect. Column 2 shows dilation to be the 

major source of time variation. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

50 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 51 

Threshold

FNMR A01N

0.010

0.020

0.050

0.100

0.200

0.00 0.02 0.04 0.06 0.08 0.10

ACTUAL

DIL

0.00 0.02 0.04 0.06 0.08 0.10

AREA

DIL_AREA

FNMR B02P

0.010

0.020

0.050

0.100

0.200

0.00050.00100.00150.00200.00250.00300.0035 ACTUAL

DIL

0.00050.00100.00150.00200.00250.00300.0035 AREA

DIL_AREA

FNMR D03P

0.010

0.020

0.050

0.100

0.200

2000 2500 3000

ACTUAL

DIL

2000 2500 3000

AREA

DIL_AREA

FNMR F02P

0.010

0.020

0.050

0.100

0.200

10.03 10.04 10.05 10.06 10.07 10.08

ACTUAL

DIL

10.03 10.04 10.05 10.06 10.07 10.08

AREA

DIL_AREA

FNMR F03N

0.010

0.020

0.050

0.100

0.200

35 40 45

ACTUAL

DIL

35 40 45

AREA

DIL_AREA

FNMR H01P

0.010

0.020

0.050

0.100

0.200

0.70 0.75 0.80 0.85

ACTUAL

DIL

0.70 0.75 0.80 0.85

AREA

DIL_AREA

FNMR I02P

0.010

0.020

0.050

0.100

0.200

0.0 0.1 0.2 0.3

ACTUAL

DIL

0.0 0.1 0.2 0.3

AREA

DIL_AREA

FNMR K03N

0.010

0.020

0.050

0.100

0.200

0.00 0.05 0.10 0.15 0.20 0.25

ACTUAL

DIL

0.00 0.05 0.10 0.15 0.20 0.25

AREA

DIL_AREA

0_2008_2008

0_2009_2009

0_2010_2010

1_2008_2009

1_2009_2010

2_2008_2010

Figure 29: Score distributions with and without dilation and area effects: As Fig. 28 but with just those 80 eyes imaged in 2008 

and 2010. For dataset N D08-10, the eight rows plot F NMR(τ ) for eight IR E X IV algorithms, corresponding to the right tail (top 20%) of 

the genuine distribution. The four columns show: the raw dissimilarities from the algorithms; the dilation-adjusted values per eq. 10; 

the iris area adjusted values per eq. 11; and the dilation-and-area adjusted values per eq. 12. In each panel the six traces correspond 

to within semester (2008,2009,2010) (in red), one year separated 2008-2009 + 2009-2010 (in blue), and two year separated 2008-2010 (in yellow) 

comparisons. Clustering of lines indicates that the genuine distribution is stable over 0, 1 and 2 year intervals, consistent with a noageing effect. Column 2 shows dilation to be the major source of time variation. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

51 
�

�

�

�

JU LY 11, 2013 I RE X V I - IR I S A G EI NG 52 

O L S models with dilation differences (eq. 4) to be 0, all “stages” to be the same, and area minima (eq. 9) set to 1, and then 

adding back in the residuals. Prior applications of regression to iris-ageing[84] computed a model for the whole population 

not for individuals - this did not allow eye-specific intercepts for example. 

Importantly, note that time between image captures is not used in computing the adjusted d' values. Thus the method 

simply subtracts functions of image-pair area and dilation from each eye sequence. These quantities vary stochastically 

through time as described in section 3. 

The results of applying these corrections are shown in Figure 27. The curves correpond to the center of the genuine 

distribution by plotting F N M R(τ ) against threshold, τ , for eight algorithms. These curves have the same form as in Fenker 

et al. (Figures 2-7 in [29] with 0 ≤F N M R(τ ) ≤ 0.7) and Baker et al. (Figure 2[8] with 0 ≤F N M R(τ ) ≤ 0.07). The first 

column, for the raw observed dissimilarity scores, suggests the same conclusion that dissimilarity does indeed increase 

with elapsed time. However, in subsequent columns, with the adjustments of equations (10) - (12), this time dependence 

is substantially reduced. 

Two additional visualizations of this are plotted. First, Figure 28 corresponds to just the right tail of the genuine distribution function, with 0.01 ≤F N M R(τ ) ≤ 0.2. Figure 29 shows the result just for the 40 individuals imaged in 2008 and 2010. 

Second, all adjusted scores are plotted exhaustively, as time-series, for algorithms I02P and D03P in Figures 30 and 31 with 

other algorithms’ plots in the accompanying Appendix. The notable observations are: 

The figures show that many individual score trajectories have lower gradient after adjustment for dilation and area. 

For example, in the top row Figure 30, the first four panels show that the left and right eyes of individual 5455 give 

lower gradients. This holds in most cases. One exception is in the 12th row, for eye 5344R, where the plotted line has 

increased gradient. 

The scores overall tend to decrease because the transformations of eqs. 13-15 do not conserve means. Indeed, some 

individual values become negative. This is immaterial because only trend is of concern. 

Some scores increase. This occurs because the O L S coefficients can be negative, usually because ΔA is counterintuitively positively correlated with dissimilarity score. This arises when the eq. (9) area metric takes on a high 

value but the area of the overlapping texture in a pair of images is small. This would occur when upper and lower 

eyelids occlude different parts of the two irises. 

The conclusion is that dilation, and also area changes, account for many of the observed increases in dissimilarity. This 

begs two questions: Why does dilation undergo a step change in the N D data? And why did the two published N D 

treatments of dilation difference not yield the same conclusion? The first question may have a random component, 

and is largely un-answerable absent more detailed knowledge of causal factors particularly environmental illumination, 

medication[41] and fatigue levels. The second question is answered separately for Baker et al.[8] and Fenker et al.[28] as 

follows. 

N D04-08 Baker et al. quantified dilation differences as the difference in the mean of dilation differences between the 

sets of images used in long term and short term comparisons. The averaging of dilation values hides pairs of images 

with markedly different dilations, because the mean of dilation differences is the difference of the mean dilations. 

However it is pairs of images for which dilations are different that produce poor dissimilarity scores. 

Their analysis proceeds by computing IrisBEE Hamming distances for long-separation image pairs, and their mean, 

μL. They likewise compute the mean H D for short separation pairs, μS. They conclude that “changes in pupil 

dilation are not an appreciable factor ” in the “observed [ageing] result” on the basis that the Kendall tau correlation 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

52 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 53 

Elapsed Time (Days)

Dissimilarity Score, Algorithm I02P, Tau = 0.26

0.1

0.2

0.3

0.4

0 200400600800

ACTUAL

2463L

DIL_AREA

2463L

0 200400600800

ACTUAL

2463R

DIL_AREA

2463R

0 200400600800

ACTUAL

4233L

DIL_AREA

4233L

0 200400600800

ACTUAL

4233R

DIL_AREA

4233R

ACTUAL

4261L

DIL_AREA

4261L

ACTUAL

4261R

DIL_AREA

4261R

ACTUAL

4327L

DIL_AREA

4327L

ACTUAL

4327R

DIL_AREA

4327R

0.1

0.2

0.3

0.4

ACTUAL

4385L

DIL_AREA

4385L

ACTUAL

4385R

DIL_AREA

4385R

ACTUAL

4394L

DIL_AREA

4394L

ACTUAL

4394R

DIL_AREA

4394R

ACTUAL

4397L

DIL_AREA

4397L

ACTUAL

4397R

DIL_AREA

4397R

ACTUAL

4470L

DIL_AREA

4470L

ACTUAL

4470R

DIL_AREA

4470R

0.1

0.2

0.3

0.4

ACTUAL

5015L

DIL_AREA

5015L

ACTUAL

5015R

DIL_AREA

5015R

ACTUAL

5033L

DIL_AREA

5033L

ACTUAL

5033R

DIL_AREA

5033R

ACTUAL

5288L

DIL_AREA

5288L

ACTUAL

5288R

DIL_AREA

5288R

ACTUAL

5293L

DIL_AREA

5293L

ACTUAL

5293R

DIL_AREA

5293R

0.1

0.2

0.3

0.4

ACTUAL

5301L

DIL_AREA

5301L

ACTUAL

5301R

DIL_AREA

5301R

ACTUAL

5308L

DIL_AREA

5308L

ACTUAL

5308R

DIL_AREA

5308R

ACTUAL

5310L

DIL_AREA

5310L

ACTUAL

5310R

DIL_AREA

5310R

ACTUAL

5321L

DIL_AREA

5321L

ACTUAL

5321R

DIL_AREA

5321R

0.1

0.2

0.3

0.4

ACTUAL

5337L

DIL_AREA

5337L

ACTUAL

5337R

DIL_AREA

5337R

ACTUAL

5344L

DIL_AREA

5344L

ACTUAL

5344R

DIL_AREA

5344R

ACTUAL

5366L

DIL_AREA

5366L

ACTUAL

5366R

DIL_AREA

5366R

ACTUAL

5379L

DIL_AREA

5379L

ACTUAL

5379R

DIL_AREA

5379R

0.1

0.2

0.3

0.4

ACTUAL

5401L

DIL_AREA

5401L

ACTUAL

5401R

DIL_AREA

5401R

ACTUAL

5407L

DIL_AREA

5407L

ACTUAL

5407R

DIL_AREA

5407R

ACTUAL

5408L

DIL_AREA

5408L

ACTUAL

5408R

DIL_AREA

5408R

ACTUAL

5409L

DIL_AREA

5409L

ACTUAL

5409R

DIL_AREA

5409R

0.1

0.2

0.3

0.4

ACTUAL

5415L

DIL_AREA

5415L

ACTUAL

5415R

DIL_AREA

5415R

ACTUAL

5432L

DIL_AREA

5432L

ACTUAL

5432R

DIL_AREA

5432R

ACTUAL

5450L

DIL_AREA

5450L

ACTUAL

5450R

DIL_AREA

5450R

ACTUAL

5455L

DIL_AREA

5455L

ACTUAL

5455R

DIL_AREA

5455R

0.1

0.2

0.3

0.4

ACTUAL

5456L

DIL_AREA

5456L

ACTUAL

5456R

DIL_AREA

5456R

ACTUAL

5463L

DIL_AREA

5463L

ACTUAL

5463R

DIL_AREA

5463R

ACTUAL

5486L

DIL_AREA

5486L

ACTUAL

5486R

DIL_AREA

5486R

ACTUAL

5489L

DIL_AREA

5489L

ACTUAL

5489R

DIL_AREA

5489R

0.1

0.2

0.3

0.4

ACTUAL

5496L

DIL_AREA

5496L

ACTUAL

5496R

DIL_AREA

5496R

ACTUAL

5501L

DIL_AREA

5501L

ACTUAL

5501R

DIL_AREA

5501R

ACTUAL

5513L

DIL_AREA

5513L

ACTUAL

5513R

DIL_AREA

5513R

ACTUAL

5517L

DIL_AREA

5517L

ACTUAL

5517R

DIL_AREA

5517R

0.1

0.2

0.3

0.4

ACTUAL

5531L

DIL_AREA

5531L

ACTUAL

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Figure 30: Score trajectories pre and post dilation and area adjustment: For dataset N D08-10, the plots show individuals’ dissimilarity 

scores for the I02P algorithm vs. time between two samples. Each dot corresponds to a pair of images, grey indicates raw scores, pink 

indicates dilation-and-area adjusted scores per eq. (12). The green horizontal line shows the threshold that gives F NMR= 0.02 globally. 

The straight blue line is the result of regressing d = d(T). A reduced slope in adjacent panels indicates that dilation and area variations 

explain any ageing effect in the left panel. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first
 

ND = Uni. of Notre Dame
 B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

53 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 54 

Elapsed Time (Days)

Dissimilarity Score, Algorithm D03P, Tau = 3118.00

1500

2500

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0 200400600800

ACTUAL

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5559R

Figure 31: Score trajectories pre and post dilation and area adjustment: For dataset N D08-10, the plots show individuals’ dissimilarity 

scores for the D03P algorithm vs. time between two samples. Each dot corresponds to a pair of images, grey indicates raw scores, pink 

indicates dilation-and-area adjusted scores per eq. (12). The green horizontal line shows the threshold that gives F NMR= 0.02 globally. 

The straight blue line is the result of regressing d = d(T). A reduced slope in adjacent panels indicates that dilation and area variations 

explain any ageing effect in the left panel. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first
 

ND = Uni. of Notre Dame
 B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

54 
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JU LY 11, 2013 I RE X V I - IR I S A G EI NG 55 

coefficient of (ρL − ρS) with (μL − μS), computed over the 46 individual eyes, is 0.217 (see [8], Figure 3). Whether 

0.2 is small is not clear, but the method is clearly undermined by the use of averaging - the penultimate row of Table 

2 indicates that an average person has a mean |D1 − D2| of 0.06, corresponding to a change in radial thickness above 

0.1 and an increase in H D of at least 0.03 (via Table 2 and Figure 7 in this paper). This would be higher for small or 

high values of D. 

Our analysis of dilation in the N D04-08 set follows the score-adjustment approach of equation (10). The result, in 

Figure 32, shows that the increase in F N M R over short time periods (i.e. the blue line) is reduced once dilation and 

iris area are factored in. However, the adjustment is not as effective at stabilizing the genuine distribution for N D 

2004-2008 as it was for 2008-2010 suggesting other variations remain. But note also that this dataset was produced 

from its parent corpus by removing 90% of the poorest quality images. 

N D08-10 Fenker ’s approach to dilation was to retain comparisons in the F N M R estimate if the dilation difference 

ΔD = |D2 − D1| did not exceed 0.1. However, this rule for removal of image-pair comparisons does not exlude any 

individual from the analysis, and removes only a few images: For the 40 individuals appearing in both 2008 and 

2010, it removes 1 of 1035 images involved in 2008-2008 comparisons, 0 of 1033 in 2009-2009, 1 of 1044 in 2010-2010, 6 

of 1966 in 2008-2009, 93 of 1993 in 2009-2010, and 65 of 2097 in 2008-2010. Specifically, the number of images involved 

in short-term comparisons reduces from 3112 to 3110, medium term from 2920 to 2879, and 2097-2032 long term. 

Similarly, for all 217 the individuals appearing in 2008, 2009 or 2010, the rule removes 3 of 2300 images involved in 

2008-2008 comparisons, 10 of 5396 in 2009-2009, 1 of 3926 in 2010-2010, 1 of 4553 in 2008-2009, 575 of 8046 in 2009­

2010, and 65 of 2097 in 2008-2010. Specifically, the number of images involved in short-term comparisons reduces 

from 3112 to 3110, medium term from 2920 to 2879, and 2097-2032 long term. If ΔD had been capped to lower values 

a larger effect would have been observed. 

4.3.2 Discussion of the N D result 

The N D team’s collection protocol (off the shelf cameras, regular scheduled capture sessions, over several years) was 

intended to support a number of iris recognition investigations, among them identification of longitudinal changes in iris 

recognition. The N D08-10 collection protocol benefitted from the camera’s internal quality control mechanisms and from 

human review. This review discarded, for example, egregiously blurred images. However, as evidenced by measureable 

false non-matches (Fig. 13), elevated scores (Fig. 19), and changes in dilation and eyelid occlusion (Figs. 21 and 24), the 

collection did not attain the level of control needed to separate any iris texture changes from all others. 

The N D analyses attempted to address this by retroactively accounting for some variations. However, as described previously the two treatments of dilation were insufficient to suppress its influence. The end-result is that the N D studies do not 

specifically claim an iris-texture ageing effect changes in the core emitting feature - they suggest only that the observed 

longitudinal increase in recognition error rates and recognition dissimilarity scores is due to iris texture changes, or to 

some unknown factor. 

4.4 Results for O P S -FIE L D 

Despite its large population size (622464 subjects), the O P S -FI E L D database is of limited utility for detecting ageing effects. 

It was collected over several years but without any notion that it would be used to estimate iris ageing effects. The database 

is actually comprised of two partitions, roughly equal in size. The first is the set of images collected during enrollment of 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

55 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 56 

Threshold

FNMR CAM2

0.050

0.100

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0.10 0.15 0.20 0.25 0.30 0.35

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DIL_AREA

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AREA

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DIL_AREA

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FNMR H01P

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Short(T<120) Intermediate Long(T>1200)

Figure 32: Score distributions with and without dilation and area effects: For dataset N D04-08, the eight rows plot F N M R(τ ) for eight 

I RE X I V algorithms applied to the images. In each panel the three traces correspond to three sets of elapsed time bins, short is fewer than 

120 days, long is greater than 1200 days, and intermediate is for anything in between. Thus short term corresponds to within semester 

(2004,2005,2006,2007,2008), long term refers to 2004/05 - 2007/08 and intermediate corresponds to everything in between. The four 

panels show from left: the raw dissimilarities from the algorithms; the dilation-adjusted values per eq. 10; the iris area adjusted values 

per eq. 11; and the dilation-and-area adjusted values per eq. 12. Clustering of lines indicates that the genuine distribution is stable over 

0, 1 and 2 year intervals, consistent with a no-ageing effect. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

56 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 57 

Time elapsed between captures (180 day intervals)

FNMR = Frac. D > Quantile(D, 1−0.05)

0.00

0.05

0.10

0.15

500 1000 1500

DETAINEE PACS

B00P D00P F00P I01P K01P

Figure 33: Stability of error rate: For dataset OPS-FI ELD, the fraction of mates not identified at better than a fixed threshold vs. binned 

time between captures. Each trace corresponds to an algorithm submitted to N I S T’s I R E X I V evaluation. The threshold for each is set to 

achieve F NI R= 0.05 over all mated searches using the entire O P S -FIE L D dataset. The upper panel refers to the cooperative enrollments 

for physical access control (PACS); the lower to variably non-cooperative detainees. The figures include results for images acquired 

with cameras from one manufacturer, but with some variety of models. These amount to about 80% of the corpus. Temporal changes 

here will depend on un-reported systematic changes to camera design, to standard operating procedure and collection environment 

and geography, and possibly to ethnic composition and demography. 

subjects for physical access controlPA C S. The second consists of images collected from subjects detained during military 

operations D E TA I N E E. The two partitions will clearly differ in the cooperativeness of the subjects. Less obviously they 

differ as a consequence of the capture environment. Particularly the detainee set includes many images collected in nonideal circumstances sometimes in improvised structures in close proximity to strong reflected sunlight[73]. Additionally, 

the data does not include large numbers of individuals with multiple encounters. 

Note, however, that the database does include many excellent images: In I R E X I I I identification trials with enrolled populations of 3.9 million subjects, false negative identification rates (“miss rates”) are below 1.5% at thresholds set so that false 

matches occur at rates below 10−13[34]. 

Given the above discussion and the lack of control on any given capture, the only results we report are population aggregates. Figure 33 shows that none of the algorithms exhibit an increase in false negative error rates over the 1900 day (5 

year) interval. The difference between the PA C S and D E TA I N E E partitions manifest themselves in separation of false negative identification rates, as depicted in Figure 33: The more controlled cooperative-subject PA C S images produce about 

half as many errors as the D E TA I N E E images. Note that the figure cannot be used for algorithm comparison because the 

thresholds used to attain F N M R= 0.05 are widely varying such that false positive rates are very different. 

Figure 34 shows that for all algorithms the dissimilarity score distributions are quite stable - there is no obvious trend in 

the median and the interquartile range is not increasing. The extreme values, indicated by the boxplot whiskers decline 

due to reductions in the sample size. 

In conclusion, despite its inherent limits, the O P S -FI E L D dataset reveals no evidence of an ageing effect. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

57 
JU LY 11, 2013 I RE X V I - IR I S A G EI NG 58 

Time elapsed between captures (60 day intervals)

Mate score dist

K01P

0.2 0.4 0.6 0.8 1.0

10 20 30

181654

69782581984857242250390503544828766233802005017576159901446411684

9802844870406098517247204472355231542436175013141212

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324202579425076245923475629880247622499623190230642360218468160321523014392143541271210108

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908650422234

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8222

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Mate score dist

B00P

0.000 0.002 0.004 0.006 0.008 0.010 0.012

10 20 30

181654

69782581984857242250390503544828766233802005017576159901446411684

9802844870406098517247204472355231542436175013141212

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Mate score dist

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D00P

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10 20 30

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69782581984857242250390503544828766233802005017576159901446411684

9802844870406098517247204472355231542436175013141212

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324202579425076245923475629880247622499623190230642360218468160321523014392143541271210108

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Figure 34: Stability of score distributions: For dataset OP S-FI E LD, the panels show the distribution of scores from each of five algorithms applied to all mated pairs of images, plotted as a function of the time elapsed between samples. The x-axis is labelled in multiples 

of 60 days - the plots extend to 2160 days (nearly 6 years). Thereafter the number of samples (shown as grey text) are far too small to be 

significant. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

58 
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JU LY 11, 2013 I RE X V I - IR I S A G EI NG 59 

5 Discussion and conclusions 

The following sections list a) considerations for future ageing tests, b) considerations for operational mitigation of ageing 

effects, c) a classification of temporal effects, and d) some areas of future work. 

5.1 Considerations for tests of biometric ageing 

We advance the following guidance for consideration by experimenters tasked with measuring biometric ageing effects. 

The content may be applicable to all modalities, not just iris. 

Comparison scores are primary response variable: While biometric system owners are primarily concerned with 

adverse recognition outcomes (i.e. false rejections and false acceptances), ageing effects should be quantified in 

terms of comparison scores. Under the defintion that ageing in healthy individuals is a continuous monotonic 

process, its effects will manifest as decreases in biometric similarity before they result in categorical recognition 

failure. Recognition failures are defined by very high dissimilarity scores and these often occur as a result of 

a poor presentation to the capture device (e.g. motion blur, non-frontal imaging, misplacement of a finger), 

or poor character (e.g. face occluded by hair, irises occluded by eye lashes). For this reason, comparison 

scores should be adopted as the response variable in biometric ageing studies rather than binary recognition 

outcomes. Score distributions should be reported as cumulative distribution functions (i.e. false non-match 

rate, or its completement) over a wide range (e.g. 0 ≤F N M R≤ 0.7) rather than in just the extreme tail. 

Chart individual responses: Biometric ageing studies should include inspection of individual-specific effects, 

particularly time-series plots. Visualizations, with appropriate response variables - primarily comparison 

scores and other quantitative measures of causal factors (e.g. iris area, facial head pose[62]) - can reveal magnitudes, trends, biometrics zoo heterogeneities, and anomalies. Pertinent variables should be plotted against 

absolute time. For example, in a fingerprint acquisition a plot of humidity against time could be generated. 

Paired data can be plotted against time difference, the prime example being comparison scores. 

Improved capture controls: Detection of iris texture ageing would best be supported by datasets exhibiting 

minimal variation in exposed iris area, pupil dilation, gaze angle, and other covariates. This can be achieved 

by design, and by review: 

Iris collection and design: Some prior iris recognition studies have used tight image acquisition controls[63, 

81, 76]. These used adjustable height cameras, chin and forehead rests, and calibrated L E Ds to contol 

illumination, centering, magnification, focus, motion blur, and specular reflection position. Quantitative ophthalmalogical studies[41], such as those for measurement of intraocular pressure (tonometry), 

routinely use: a) a head positioning brace that at least fixes head pitch, b) a fixed calibrated lighting 

environment, e.g. to scotopic (dark-light) or photopic adjustment, c) instructions to subjects to avoid 

stimulant and sedative intake in the prior (24) hours, d) recording of such, e) removal of contact lenses, 

and f) steps to ensure that the eyes are wide open. 

Some current commercial cameras control pupil size via application of visible light; while this is may 

not be under closed-loop control30, it is intended to reduce dilation differences by pushing pupil size 

down to a canonical person-specific value. A passive form of pupil dilation control applies a nominally 

fixed level of visible illumination. If this is moderately higher than the expected ambient illumination, 

the dilation will be stabilized against modest variations in the ambient. When the ambient has large 

variations, a shroud may be employed to block the ambient illumination so that the only visible illumination comes from the camera and is nominally constant. The binocular Crossmatch S E E K, Cogent 

Fusion and IrisID T10 are extant examples of this approach. 

Some commercial iris recognition algorithms recognize eyelid occlusion and compensate for its effects 

on the impostor distribution (eq. (4) in [43]). However, a non-occluded image or minimally occluded 

image is preferable. It is possible to incorporate the eyelid occlusion algorithms into the image capture 

30Pupil diameter can be measured in an video stream of iris images[14], in real time, with latency of a few frames. Using size information to control 

visible-light irradiance would afford active control of pupil dilation. One C O T S camera, the IrisGuard IG-AD100 claims to implement essentially this 

approach - see https://www.prbuzz.com/technology/63941-irisguard-acknowledges-but-refutes.html retrieved 2013-03-04. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

59 
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JU LY 11, 2013 I RE X V I - IR I S A G EI NG 60 

loop and to only accept iris images that have some minimum level of exposed iris area. Both the P I E R 

and the I R I S -I D-4000 can be demonstrated to provide such control by noting that attempts to capture 

images with intentional eyelid occlusion will cause the cameras to refuse to capture if the occlusion 

level is above some internal threshold. 

Online (capture-time) sample review: Aberrant images can be detected by requiring images to pass 

quality tests[87] or one-to-one recognition against a reference with a moderately high threshold. Many 

biometric studies proceed without such checks and without any feedback to the subject. Temporal separation of collection and experimentation limits the detection of unexpected or aberrant data. Checks 

could be manual or automated, but should in any case be incorporated into the collection protocol. 

A protocol that simply captures and stores images without review is not recommended, even with 

nominally cooperative subjects. 

Maintaining partication and motivation: Experimenters should establish mechanisms to maintain a level of engagement, investment or reward for continued participation in a biometric ageing collection. This recommendation is made to maintain adherence to the protocol over extended time periods by mitigating the effects of 

ennui or blas e presentation, i.e. casual compliance to the imaging protocol. For example, a subject might keep  ́

talking during collection and this could alter facial expression in a face study, or change eyelid occlusion in an 

iris study. 

Operators, involved in continued and orderly biometric collection should similarly be incentivized or invested 

in some eventual outcome. In an academic study, the operator might be charged with analysis during collection, for example, on a weekly basis and this might reveal avertible artifacts. 

Considerations peculiar to university-collected data: Studies confined to university populations are often characterized by small populations, due to constraints associated with duration of the study, funding, catchment 

area, incentives and the voluntary nature of participation. University populations are often different in their 

ethnic and demographic composition from typical operational populations. Finally, specific protection against, 

or consideration of, inter-subject interaction may be necessary. Correlated behaviour may appear between 

subjects who spend time together - e.g. how to use a fingerprint sensor. 

Cost benefit of ageing studies: As age-related effects can be avoided by periodic re-enrollment, dedicated studies 

of ageing should be conducted after review of potentially high costs. These include the costs associated with: 

retention of a dedicated large test population over a period extending most usefully up to a decade; maintaining obsolescent equipment; employment of necessary staff; and diversion of resources from other more 

beneficial tests. 

Health Condition: A longitudinal study should be scoped with clear goals. For example, a study intended to 

look at the effects of eye disease would state a delineation between the target population and any healthy control group it might use. Similarly studies intended to quantify ageing of the core biometric source in healthy 

individuals would only include individuals for which medical conditions are absent, or for which the biometric trait is considered to be unaffected by known medical conditions. Such studies would report their 

scope and procedures used to establish health condition. Alternatively, a protocol that records health condition at each capture might explicitly include surgical and acute-disease onset events in analyses that model 

step changes[30]. 

5.2 Considerations for operational mitigation of biometric ageing 

Pupil dilation: The adverse effects of pupil dilation differences remain evident in state-of-the-art recognition 

algorithms. While these algorithms are proprietary black-boxes, linear “rubber sheet” scaling to the fixed polar 

representation is not a correct model of reality for sufficiently large dilation differences. It remains important 

operationally to enroll iris images without unusual dilation values. 

Instrumenting operational systems: Operators of biometric systems should instrument their collection and recognition equipment in order to log pertinent performance-specific variables. The variables would include capture duration, all processing times, template sizes, sample qualities, comparison scores and candidate lists, and 

recognition outcomes. While this measurement recommendation supports logging of information beyond that 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

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needed for operational use, it supports higher level surveys, including of possible ageing effects. Operational 

systems often enroll large numbers of individuals and these afford enhanced analytical opportunities. 

Measure individual responses: Operators of biometric systems will almost always care whether some fraction of 

the user population is subject to processes which lead to recognition failure. For this reason, biometric ageing 

studies should include subject-specific and/or image-specific analyses. This recommendation is applicable to 

more general performance analysis, e.g. for investigation of effects due to age or ethnicity. The corollary of 

this recommendation is that population aggregated metrics should be treated with caution and are not, by 

themselves sufficient for quantifying ageing effects. 

Operational mitigation of ageing: In long-term applications, suspected biometric ageing effects can be mitigated 

by re-enrollment of subjects. For example passport expiration enforces 5 or 10 year re-capture of facial images31. Requirements to re-enroll should be specifically planned and documented, and these should be based 

on longitudinal data and change measures. In applications where a credential is issued (e.g. a passport, or a 

driver ’s licence), the re-enrollment can be scheduled by instituting an expiry date for the credential. In applications where biometric data is retained on a central server, additional metadata should include collection dates 

and expiration dates. Old biometric data might be retained for investigation purposes. 

We do not recommended so-called automated template-update or template-replacement strategies without a 

thorough consideration of the security context. The default guidance is to re-enroll only in the presence of a 

trained operator who should inspect for the presence of contact lenses, artifical fingerprints, and face masks. 

Negative identification systems: In some applications, particularly negative identification ones such as detection 

of visa or benefits fraud, mitigation of ageing is not automatically possible because opportunities for second 

captures are not under control of the system operator. While in some cases, second encounters will be rare 

(e.g. counter-terrorism), other applications see second encounters more frequently - for example recidivism 

rates in criminal law enforcement can exceed 50%[10]. In negative identification applications, all collected data 

should be retained and annotated with capture date, and used in search applications. System operators should 

refrain from matching against only recent samples because any given sample can be affected by sample quality 

problems whose effects dominate ageing-related degradation. For example, face recognition against the full 

historical capture record has been shown to be superior to just recognizing the most recent image [35]. 

Use of contemporary and capable algorithms: Ageing studies conducted with algorithms that are not competitive with likely fielded capability have limited relevance to operational use. This recommendation is made 

for all modalities for two reasons. First, is to capture the latest developments against ageing. For example in face recognition there is ongoing research to model age progression[75] and to achieve age-invariant 

representations[67]. Second, because biometric samples are variously noisy or degraded it is beneficial to use 

high performance localization algorithms to effectively separate the computer vision problem (e.g. for finding 

faces or eyes or iris boundaries) from the problem of measuring change in the core biometric trait. In iris recognition, the commercial and academic consensus is that segmentation is both important and hard. It is a very 

often-studied problem[50] with confounding effects on detection of ageing[27]. 

Disease: Acute and chronic medical conditions can have severe effects on biometric recognition. Most conditions 

are rare, but the effect will depend on the population incidence and prevalence, and the severity of the condition. Dedicated studies have been conducted for fingerprints[26] and iris[92, 79, 23] recognition, and there are 

many more general publications in the medical literature. 

Effects on Impostor Accuracy: Ageing studies are primarily of interest in assessing how dis-similar persons are 

to themselves after various periods of time. This intra-person effect is most effectively quantified in terms of a 

genuine matching score emanating from a recognition algorithm. The question of whether individuals become 

more or less distinctive (with respect to other individuals) as time passes is relevant if the demographic age 

structure changes. It should only be examined if resources are available because, to first order, false matches 

occur when two samples are biologically similar, by definition, and the expectation over a population is that 

subjects who are similar to other persons in the population now, will a) not become more similar to those 

persons, and b) will not increase in similarity to others. In cases where false match probabilities depend on 

31 Modern e-Passports are increasingly populated with other biometric modalities - the ICAO 9303 specification defines containers for face, fingerprint, 

iris and a number of other modalities. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

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age, the time-dependence of false-match may require modeling of the changing demographic composition 

of the enrolled population. For example, if a biometric trait was characterized by its gradual appearance or 

disappearance in a population over time (e.g. if all babies’ faces initially appeared identical, and then became 

individualized), then this would warrant quantification. 

Considerations peculiar to operational log files: Operationally collected data may represent very large populations with wide ethnic and demographic variation. Additionally operational systems may be used continuously, and at any time, such that diurnal or seasonal effects are present. Another distinguishing characteristic 

of some operational data is that the subjects do not interact with each other, for example in a border crossing 

system, vs. a school population. Operational settings may not support use of the experimental controls necessary to relate cause and effect. Note a formal standard dedicated to testing of operational systems has been 

published[54]. 

5.3 Hierarchy of longitudinal effects in biometrics 

Given the need in this paper to separate core iris-ageing effects from extraneous variation, we propose the following 

categorization. 

C L A S S A - Time variation in biometric accuracy associated with the system. Example causes would be gross dirt 

accumulation on a fingerprint imaging platen, failure of an infra-red LED in an iris camera, or focusing mechanism 

in a face camera. This definition also includes environmental variations (e.g. in illumination) that are not handled 

by the system; for example diurnal variation in lighting levels in a face imaging system. 

C L A S S B -Temporary or remediable subject-specific variations. These changes are due to the presentation of the 

subject to the system. Examples are: occlusion of the face by hair, scarf, heavy glasses; occlusion of an iris by 

mascara or eyelid; dryness of, or injury to, a finger; 

C L A S S C -Permanent and not-remediable subject-specific variations unrelated to the biometric source. Examples 

would be arthritic fingers that impede presentation of plain impressions on a fingerprint sensor, the increase in 

fingerprint scars associated with periods of manual labor, the growth of a full beard altering facial appearance, 

and pupil size reduction in iris recognition. Examples would be an increase in the incidence of broken ridges in 

fingerprints (inducing false minutiae), the loss of hair in males can defeat face finding algorithms, and in iris the 

condition arcus senilis. 

C L A S S D - Time variation associated with the anatomical, physiological or behavioral aspects of the human subject. 

Examples are the growth of the volar pad in childrens’ fingers, changes in the facial anatomy[98], and material 

change in the iris texture itself. 

5.4 Future work 

This work, and ageing studies more broadly, can be progressed as follows. 

Bilateral analysis This study treats each eye as if it were from a separate person. This loses the potential power 

of identifying bilateral ageing processes, i.e those that would affect both eyes. The exploratory and quantitative 

analyses should be augmented to bind eyes to people. For example, the mixed effects regression technique can be 

augmented to allow the eye label to be nested within a subject, and to capture correlations between left and right. 

Other data sets The authors advocate for the use of larger populations, and these are almost only ever found in 

operational circumstances. They balance drawback of potential lack of experimental control, with the benefit of 

large populations. 

Improved modelling Future work should better model the effects of known or measureable covariates. Particularly 

the dependence of dissimilarity scores on dilation and dilation change is non-linear and strongly influential on 

accuracy. The limited data in this paper and in I R E X I I I[34] is the largest multi-algorithm set available to support 

construction of dilation models. 

F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

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F NM R = False non-match rate A = Uni. Bath D = 3M Cogent H = Delta ID K = Morpho Tmin = Time enroll to first 

ND = Uni. of Notre Dame B = Neurotech. F = MorphoTrust I = Uni. Cambridge Tmax = Time enroll to last 

TA = Active, first to last 

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