Iris Recognition under Biologically Troublesome Conditions -

Effects of Aging, Diseases and Post-mortem Changes

Mateusz Trokielewicz

1,2

, Adam Czajka

1,3,2

and Piotr Maciejewicz

Biometrics Laboratory, Research and Academic Computer Network (NASK),

Kolska 12, 01-045 Warsaw, Poland

Institute of Control and Computation Engineering, Warsaw University of Technology,

Nowowiejska 15/19, 00-665 Warsaw, Poland

Department of Computer Science and Engineering, University of Notre Dame, IN, Notre Dame, U.S.A.

Department of Ophthalmology, Medical University of Warsaw,

Lindleya 4, 02-005 Warsaw, Poland

Keywords:

Biometrics, Iris Recognition, Reliability, Aging, Ocular Pathologies, Post-mortem.

Abstract:

This paper presents the most comprehensive analysis of iris recognition reliability in the occurrence of various

biological processes happening naturally and pathologically in the human body, including aging, illnesses, and

post-mortem changes to date. Insightful conclusions are offered in relation to all three of these aspects. Exten-

sive regression analysis of the template aging phenomenon shows that differences in pupil dilation, combined

with certain quality factors of the sample image and the progression of time itself can signiﬁcantly degrade

recognition accuracy. Impactful effects can also be observed when iris recognition is employed with eyes

affected by certain eye pathologies or (even more) with eyes of the deceased subjects. Notably, appropriate

databases are delivered to the biometric community to stimulate further research in these utterly important

areas of iris biometrics studies. Finally, some open questions are stated to inspire further discussions and

research on these important topics. To Authors’ best knowledge, this is the only scientiﬁc study of iris recog-

nition reliability of such a broad scope and novelty.

1 INTRODUCTION

Well established position of iris recognition, includ-

ing several large-scale applications, such as India’s

Government program AADHAAR, or the CANPASS

system maintained for efﬁcient US-Canada border

crossings, is attributed to a high uniqueness of the in-

tricate pattern found in the iris tissue, as well as its

asserted temporal stability and immutability. This as-

sertion dates back to year 1987 with Saﬁr and Flom’s

patent, which ﬁrst laid out theoretical ground for iris

recognition: ’signiﬁcant features of the iris remain ex-

tremely stable and do not change over a period of

many years’ (Flom and Saﬁr, 1987). This is later sup-

ported by John Daugman in his 1994 patent, in which

he describes the iris pattern as ’unique for each indi-

vidual and stable over many years’ and ’essentially

immutable over a person’s life’ (Daugman, 1994).

These claims, being cited throughout the iris biomet-

rics literature, allowed a common belief to arise, that

a single enrollment could be sufﬁcient for a lifelong

successful recognition of one’s identity.

However, one may come up with several scenar-

ios and circumstances, in which actual iris biomet-

rics performance may tumble short of these perfect-

condition assumptions. Recognition accuracy can

be heavily inﬂuenced by factors related to biological

mechanism of the human body. These include natu-

ral aging as time progresses, occurence of medical

conditions and disorders, and, ultimately, death.

This paper is intended to summarize Authors’ re-

search activity related to the reliability of iris recog-

nition and its resilience against such conditions, as

well as to pose some questions regarding these ef-

fects’ negative impact on recognition accuracy. Sec-

tions 2, 3 and 4 present excerpts of comprehensive

analyses of effects inﬂicted by aging, medical disor-

ders affecting the eye, and a post-mortem iris recog-

nition study, respectively (with references provided to

author’s full papers devoted to the respective ﬁelds of

research). Section 5 contains conclusions drawn from

this study and states some open questions regarding

Trokielewicz M., Czajka A. and Maciejewicz P.

Iris Recognition under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes.

DOI: 10.5220/0006251702530258

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 253-258

ISBN: 978-989-758-212-7

253

iris recognition performance even under biologically

burdensome circumstances, potential gains, but also

downsides and risks.

2 IRIS TEMPLATE

NON-STATIONARITY

What is ’Template Aging’? The subject of ’tem-

plate aging’, or, as we think it should be referred to,

’template non-stationarity’ in relation to iris recogni-

tion can be deﬁned roughly as an increase in error

rates that is expected to appear when the time between

gallery (i.e., enrollment) and probe (i.e., veriﬁca-

tion/identiﬁcation) samples progresses. It consists of

multiple aspects that one need to consider for a com-

prehensive and insightful analysis, including: biolog-

ical aging of the eye and its structures; differences in

sample presentation originating in pupil dilation, eye-

lid droop, acquisition conditions, etc.; sensor interop-

erability and aging - when gallery and probe samples

are collected using different equipment and camera

components wearing out, respectively. The ISO/IEC

biometrics vocabulary deﬁnes this as follows: refer-

ence aging - the changes in error rates with respect

to a ﬁxed reference caused by time-related changes

in the biometric characteristic, its presentation, the

sensor and other components of the biometric tech-

nology.

Template non-stationarity is reported to play a

vital role in decreasing over-the-years iris recogni-

tion performance in number of publications (Tome-

Gonzalez et al., 2008), (Bowyer et al., 2009),

(Baker et al., 2009), (Baker et al., 2013), (Fenker

and Bowyer, 2012), (Fenker and Bowyer, 2011),

(Fairhurst and Erbilek, 2011), (Sazonova et al., 2012),

(Bowyer and Ortiz, 2015), including our own previ-

ous research (Czajka, 2013). NIST’s IREX VI report,

however, states the contrary (Grother et al., 2013),

and was later criticized by Bowyer (Bowyer and Or-

tiz, 2013), and a response to that critique was also

published (Grother et al., 2015). Recently, more

researchers have made efforts to better understand

the non-stationarity of templates, namely by isolating

as many factors as possible (Hofbauer et al., 2016),

studying the impact of segmentation quality (Wild

et al., 2015), but also the inﬂuence of sensor aging

(Bergmuller et al., 2014). This shows that despite

many research efforts having been put into solving

these issues, template aging still presents many chal-

lenges, and new solutions and experimental method-

ologies are much welcome.

Linear Regression Analysis. We chose to perform

an analysis that circumvents the underlying reasons

of these changes, and focuses solely on the outcome

of the underlying phenomena, manifesting in altering

the sample properties. One way to do so is to perform

a linear regression analysis that aims at predicting the

comparison score using covariates relating to some

predeﬁned qualities of the image. This is to show pos-

sible sources of the decrease in recognition accuracy

as time progresses (Trokielewicz, 2015).

For the experiments, a database of 583 samples

from 58 irises collected up to 9 years apart have been

used (to our knowledge this is one of the most ex-

tensive aging-related iris images databases in terms

of the timespan between samples). Linear regres-

sion analysis was employed in attempt to predict the

comparison score in terms of: 1) time elapsed since

gallery image acquisition, 2) selected quality mea-

sures (eyelid, eyelash and reﬂection occlusion per-

centage, local contrast, illumination intensity and im-

age sharpness) and 3) geometrical factors (iris and

pupil radii and their variability in a given image pair).

29 regression models built upon three different com-

mercial and academic iris recognition solutions al-

lowed us to formulate some interesting conclusions.

The time parameter proved to be statistically sig-

niﬁcant in every model, making it plausible that

the non-stationarity phenomenon may be autonomous

from quality and geometrical characteristics of iris

images. Nonetheless, those covariates should be

taken into account in future studies, as some combi-

nations of them turn out to also be statistically signif-

icant in predicting the comparison score, such as im-

age sharpness and local contrast. Notably, the pupil-

to-iris radius ratios are shown to be statistically sig-

niﬁcant in every tested model. This may indicate

that differences in pupil diameter are the most likely

sources of recognition accuracy decrease as time be-

tween sample acquisitions elapses.

3 IRIS BIOMETRICS AND

OCULAR DISORDERS

Recognition Accuracy Impact. Iris recognition

usually performs exceptionally well, provided that it

is applied to subjects with healthy eyes. However,

numerous medical conditions affecting the eye struc-

tures, especially the iris, anterior chamber of the eye,

and the cornea, have a potential of degrading its accu-

racy and reliability. Yet, due to the lack of appropriate

datasets and difﬁculties in creating them, limited re-

search is available, mostly centered around cataract

and cataract extraction procedure inﬂuence on iris

BIOSIGNALS 2017 - 10th International Conference on Bio-inspired Systems and Signal Processing

254

recognition performance: (Roizenblatt et al., 2004),

(Seyeddain et al., 2014), (Dhir et al., 2010), (Trok-

ielewicz et al., 2014), (Ramachandra et al., 2016)

(signiﬁcant negative impact of cataract and cataract

surgery reported by most researchers except for Dhir

et al.), impact of refraction correction procedures

(Yuan et al., 2007) (no impact reported), but also stud-

ies regarding multiple disorders (Aslam et al., 2009),

and their impact on segmentation (McConnon et al.,

2012). In the papers (Trokielewicz et al., 2015b),

(Trokielewicz et al., 2015a) we present the most thor-

ough and comprehensive analysis on the subject of

disease inﬂuence on iris recognition reliability to date,

including an extensive cataract inﬂuence study, and

a novel approach to eye pathology impact analysis,

based not on disease taxonomy (impact of certain dis-

eases), but rather on the type of damage that medical

disorders afﬂict on the eye.

Database of Iris Images Collected from Ophthal-

mology Patients. For the purpose of these studies,

a new database had to be collected. We had a rare

opportunity of a close collaboration with an ophthal-

mologist’s ofﬁce, which allowed us to gather an un-

precedented collection of iris images coming from

patients suffering from more than 20 different con-

ditions. This dataset consists of almost 3000 images,

both NIR-illuminated and high-resolution ones taken

in visible light (this is done to enable a close-up visual

inspection of the affected eye structures).

Cataract. Our research devoted to studying the in-

ﬂuence of cataract on the performance of various iris

recognition methods showed a degradation in match-

ers’ accuracy when images obtained from cataract af-

fected eyes are used, compared to the scenario when

images of healthy eyes are used. This decrease in per-

formance manifested itself with worsening the gen-

uine comparison scores by as much as 175% (for an

example commercial matcher), while impostor scores

remained mostly unaffected. This change in compar-

ison scores was able to elevate the FNMR values in

two out of three employed recognition methods. Ad-

ditional experiments were conducted to show whether

this decrease in performance could be attributed with

wrong execution of the image segmentation stage,

however, this hypothesis was not conﬁrmed. Hence,

we may suspect that some additional factors play a vi-

tal role in worsening the reliability of iris recognition

in cataract patients (Trokielewicz et al., 2014).

Disease Impact on Eye Structures. While this is

often true for cataract-affected eyes, most ophthal-

mology patients with severe eye illnesses suffer from

not one, but usually two or more conditions at the

same time. These conditions are often unrelated and

affecting the eye in different ways. This makes it

extremely difﬁcult to conduct an insightful analysis

for one disease at a time. Hence, we proposed a

new method of data analysis, which involves divid-

ing the dataset into ﬁve subsets, each of them repre-

senting a different type of impact afﬂicted on the eye

structures by the pathologies involved. The ﬁve par-

titions include: 1) healthy eyes, 2) disease-affected

eyes, but not revealing any visible impairments, 3)

eyes with geometrical distortions of the pupil, 4) eyes

with changes in the iris tissue itself, and 5) eyes with

changes in the cornea or the anterior chamber that ob-

struct the view of the iris below those structures. This

approach, combined with an exhaustive analysis in-

corporating four commercial and academic matchers,

allowed us to formulate four interesting and impor-

tant conclusions (Trokielewicz et al., 2015b), (Trok-

ielewicz et al., 2015a), (Trokielewicz et al., 2016b):

• the enrollment stage is highly sensitive to med-

ical conditions that introduce geometrical dis-

tortions to the pupillary area and obstructions

of the iris pattern

• even if no perceivable changes can be observed

in the diseased eyes, the performance can still

drop when compared to this achieved using

healthy eyes images

• all eye conditions that can afﬂict visible dam-

age to the eye structures are capable of degrad-

ing the comparison scores (across all tested

recognition methods), with geometrical de-

formations and iris pattern obstructions con-

tributing the most

• most of the observed recognition errors can be

attributed to the faulty execution of the image

segmentation stage

Database Contribution. Papers (Trokielewicz

et al., 2015b) and (Trokielewicz et al., 2015a) also

make a signiﬁcant contribution for the biometrics

community by offering two vast datasets of iris

images obtained from patients suffering from various

ocular disorders. We are not aware of any other pub-

licly available datasets that would offer a collection

of iris images representing disease-affected eyes.

Those datasets can be used for research and non-

commercial purposes by all interested researchers.

For details on how to access the data, please see

(Warsaw University of Technology, 2015a), (Warsaw

University of Technology, 2015b).

Iris Recognition under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes

255

4 POST-MORTEM IRIS

RECOGNITION

A Beneﬁt for Forensics, an Issue for Identity Man-

agement? The topic of post-mortem recognition in

human subjects has received considerably low atten-

tion in the biometric community. Due to the difﬁcul-

ties in data collection and the obvious unpleasantness

of such experiments, very little research has been pub-

lished, especially when human eyes are concerned,

with few exceptions, namely (A. Sansola, 2015) (the

paper concludes that post-mortem iris recognition

works ﬁne in about 80% of the cases for samples ac-

quired up to 2 days after death) and (Bolme et al.,

2016) (which mostly focuses on post-mortem face

and ﬁngerprint recognition, with few conclusions re-

garding irises). (Saripalle et al., 2015) present a study

of post-mortem iris recognition using cadaver eyes of

a domestic pig, reporting that the eyes lose their capa-

bility to serve as a biometric identiﬁer in 6 to 8 hours

post-mortem.

This aspect of iris biometrics is important for at

least two reasons. First, if post-mortem recognition

is viable, it could prove useful in forensics, namely

identiﬁcation and veriﬁcation of accident and crime

victims, and even in the battleﬁeld (when other fast

methods of identiﬁcation are not accessible, say, vic-

tim has lost his ﬁngers or face is disﬁgured). The

latter reason connects with the use of iris biometrics

for identity management and asset protection and an

associated fear of identity theft - ’will someone be

able to steal my iris after I die, and use it to gain

access to my identity?’ (Science Focus, 2016). Sev-

eral publications ﬁrmly mention that iris recognition

after death cannot be performed due to pupil dilation

and corneal cloudiness (BBC News, 2016), ’iris de-

cay’ (Szczepanski et al., 2014), ’iris features vanish-

ing with pupil dilation’ and ’muscle relaxation’ (Iris-

Guard, 2016)(IriTech, 2016). However, no experi-

mental evidence is presented in either of those pub-

lications.

Experimental Study: Short-term Analysis. In our

studies regarding the ﬁeld of post-mortem iris bio-

metrics (Trokielewicz et al., 2016c), (Trokielewicz

et al., 2016a) we have shown that the above claims

are mostly untrue. To be able to conduct these exper-

iments, a new database had to be collected, using iris

images obtained from deceased human subjects in a

hospital mortuary. The dataset comprises of iris im-

ages collected from 12 different irises over a period of

27-29 hours post-mortem. The ﬁrst session was con-

ducted approximately 5-7 hours after demise, with the

second and third sessions conducted after 11 and 22

hours. We managed to show that, contrary to claims

cited above, the pupils are not excessively dilated af-

ter death (but rather ﬁxed in a mid-dilated position),

nor is the iris structure ’vanishing’. With images cap-

tured a few hours post-mortem we were able to reach

perfect recognition accuracy with one of the four em-

ployed matchers, while the FNMR values for the re-

maining three were surprisingly low (from 1.4% to

8.3%). The decay of the eye structures indeed pro-

gresses as time after death elapses, yet these dynam-

ics are much less aggressive than previously stated in

literature. The FNMR values for the best perform-

ing matcher rose to 5.1% and 26.7% for images ob-

tained in the second and the third session, respectively

(images from these sessions were compared against

those obtained in the ﬁrst session). It is the third ses-

sion, with images collected approximately 27 hours

post-demise, where serious deterioration begins and

error rates spike, depending on the method employed,

to the range of 26.7%-86.7% of falsely non-matched

samples.

Experimental Study: Long-term Analysis. Fol-

lowing these studies, we have continued to collect the

data and were able to obtain a unique dataset of post-

mortem human iris images spanning as long as 407

hours (almost 17 days). These experiments revealed

that although after such a long period iris recogni-

tion is almost impossible, one may still expect to get

occasional correct matches (after 407 hours for Iri-

Core and MIRLIN methods, 260 hours for the Veri-

Eye method, and 124 hours for the OSIRIS solution).

However, apart from these exceptions, the rate of eye

degradation due to drying, wrinkling, and opaciﬁca-

tion of the cornea combined with a collapse of the

eyeball make iris recognition virtually impossible in

most of the attempts after such long periods after

death.

Database Contribution. Notably, here as well a

unique dataset of iris images obtained from deceased

subjects has been prepared and will be released to in-

terested members of the biometric community in the

fall, to encourage further research on this important

matter. To author’s best knowledge, this will also be

the ﬁrst publicly available dataset of this kind. On

how to get access to the data, please see (Warsaw Uni-

versity of Technology, 2016).

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5 CONCLUSIONS AND OPEN

QUESTIONS

This paper presents a comprehensive account on Au-

thors’ research regarding iris recognition’s still un-

tackled problems associated with biological processes

taking place in the human body. Aging, ocular

pathologies, and processes occurring after death are

shown to be capable of causing serious degradation

in the reliability of various commercial and academic

iris recognition solutions. Although the very exis-

tence of these issues should not by any means lead

to dismissing iris biometrics as a secure, efﬁcient and

accurate identiﬁcation method, certain steps should

be undertaken to defend against them. Thus, an im-

portant aspect of future studies should be to propose

appropriate countermeasures that would increase re-

silience of iris recognition against changes induced in

the eye by these phenomena.

The questions that arise from this research, are

thus as following:

• what contributes to the iris template non-

stationarity phenomenon? What methods can be

employed to examine these effects?

• how can we defend against decrease in recog-

nition accuracy caused by biologically-induced

damage to the eye, such as diseases and post-

mortem decay?

• can post-mortem iris recognition provide a new

method to improve over currently used toolboxes

of forensic examiners?

• on the other hand, should post-mortem iris recog-

nition pose concerns over biometric identity man-

agement security (are there vulnerabilities, such

as presentation attack risks)?

We hope that this paper, together with the avail-

able datasets, will inspire other researchers in this

ﬁeld to come up with their own experiments regarding

the interdisciplinary ﬁeld of biometrics and biology,

and solutions to problems discussed here, to further

improve iris recognition as a safe, fast, and reliable

biometric method.

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