IREX III

Supplement I: Failure Analysis

NIST Interagency Report 7853

G. W. Quinn and P. Grother

Information Access Division

National Institute of Standards and Technology

April 19, 2012
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

IREX III Supplement I: Failure Analysis

Contents

1 Introduction 2

2 Methodology 2

2.1 Construction of the Failure Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.2 Manual Inspection Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.3 A note on Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Sources of Poor Image Quality 3

3.1 Poor Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3.1.1 Reflective Glare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3.1.2 Rotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.1.3 Occlusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.4 Blur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.5 Contact Lenses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.1.6 Off-Axis Gaze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.1.7 Dilation Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.1.8 Small and Large Iris Radii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 System Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2.1 Lossy Image Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2.2 Posterization (Quantization) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.3 Ground Truth Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.4 Bayer Filter Defect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.5 Low Contrast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.6 Broken LEDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3 Poor Inherent Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3.1 Abnormal Pupil Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3.2 Disease and Trauma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Poor Quality due to Multiple Problems 19

5 Final Comments 19

A Summary of Recommendations 20

1
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

1 Introduction

Iris recognition has the potential to be extremely accurate, but like many other biometrics, it is highly dependent on the

quality of the sample data. Occlusion of the iris, off-axis gazes, blurred images, and iris rotations are common problems

that can make recognizing individuals more difficult. A fraction of the Iris Exchange (IREX) III dataset consisted of poor

quality images that reduced overall performance in the evaluation. For single eye searches, the matching algorithms were

never able to achieve an identification rate better than 98.3% at any reasonable decision threshold. It is fair to assume that

some large fraction of those missed searches are the result of poor quality on the part of either the search image or its

corresponding mate in the enrollment set.

The poor quality images often stem from systemic problems with the capture and data handling process. The purpose of

this failure analysis is to identify common sources of poor sample quality and to provide best practice recommendations

for how to improve the quality of captured samples. The analysis began with the construction of a special ”failure set”

of images, which contained those images that were a common source of error for several matching algorithms. Strictly

speaking, the failure set contained a set of image pairs, where each pair consisted of a search image and a corresponding

mate that was used for enrollment. A thorough manual inspection was then performed on each image in order to identify

specific cause(s) of failure. Section 2 describes the construction of the failure set and manual inspection process. Section 3

explores each of the common sources of poor sample quality and provides recommendations on how to adjust the capture

protocol or data management practices to reduce the likelihood of their occurrence. Appendix A summarizes the best

practice recommendations provided in the previous subsections.

2 Methodology

2.1 Construction of the Failure Set

Approximately 17,000 iris images were randomly selected and searched against an enrollment set of 1.6 million images

of 1.6 million subjects. Each search image was known to have an enrolled mate, based on the ground-truth information

that was available for the dataset. Matching was performed using 8 algorithms selected from the 8 better performing

participants. The algorithms were the latest Class A submissions (i.e. the submissions that focused on speed) at the time

that the failure set was built. The failure set consists of those searches that were missed at rank 20 by at least 4 of these

matching algorithms. The failure set contains 1,013 pairs of images (1,013 search images with 1,013 corresponding mates),

which comprise about 6.0% of the 17,017 searches performed. Although each search in the failure set was missed by at

least 4 algorithms, most were still matched by at least one. Only 240 (or about 1.4%) of the searches were missed by all

algorithms. Upon further inspection, nineteen (0.1%) of these were the result of ground truth errors.

2.2 Manual Inspection Process

Each of the images in the failure set was manually inspected by an individual that has extensive experience with studying

the iris biometric. The inspector was tasked with identifying specific problems with each image (e.g. eyelid occlusion,

compression artifacts, rotation, ground truth error). The inspector used specific criteria when possible, but at times it was

necessary to use subjective judgement when deciding whether an image suffers from a particular problem. The criteria

used by the inspector are documented later in this report. Interesting or unusual cases were loaded into a specialized

investigational software suite called the IrisLab Toolset. Some of the figures in Section 3 are snapshots of the tool as it was

operating on images (see Figure 1). The top row in each of the snapshots shows the unaltered iris images. The bottom row

displays segmentation overlays, when available, generated by a commercial ”off-the-shelf” algorithm.

2
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

Figure 1: Example snapshot of the IrisLab Toolset.

2.3 A note on Recommendations

Many of our recommendations require additional involvement of the camera operator in the capture process. Ideally,

the operator will receive training on how to identify poor quality images and how to avoid their capture. While some

recommendations are common sense (e.g. keep the subject within the focal range of the camera), others require specific

knowledge of how iris recognition works. Camera developers can assume some of this responsibility by implementing

improvements to their capture devices. Examples would be to improve automated quality filtering or provide additional

feedback to the operator when there is a problem with the scene (e.g. if the subject is standing too close). Since camera

operators rarely receive extensive training in the collection of biometrics, instructions to the operators should be clear and

brief.

3 Sources of Poor Image Quality

Quality problems can occur for several reasons. Sometimes presentation by the subject is poor (e.g. a closed eye); sometimes the camera is damaged (e.g. burnt out LEDs); and sometimes the capture environment introduces problems (e.g.

reflections in the eye due to excessive ambient illumination). These examples occur at the capture stage, but other problems

can occur during subsequent processing and storage of the images. For example, an image might be severely compressed

or corrupted when it is transferred over a network. This section organizes the various causes of poor sample quality into

three categories: 1) those attributable to poor presentation, 2) those that result from problems with the capture system or

subsequent processing and storage, and 3) those that are due to unusual characteristics inherent in the individual (e.g.

congenital iris defects). In reference to the ISO/IEC 29794-2 standard on biometric sample quality [7], the first two categories affect the fidelity of the sample, while the last relates to the character of the source from which the sample is derived.

Some of the categorizations are ”soft” in the sense that reasonable people might disagree with respect to which category a

particular image falls into. An image may also suffer from problems in multiple categories, in which case it will fall into

more than one category.

3
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

Type Problem Number of

Images

Number of (paired)

Searches

Poor Presentation Occluded Iris 236 214

Rotated Iris 244 219

Off-axis Gaze 39 37

Specular Reflections 304 280

Highly Dilated 44 38

Highly Constricted 84 79

Blurred 91 88

Patterned Contact 6 6

Not an Iris 4 4

System Problems Ground Truth Bug 38 19

Compression Artifacts 743 557

Reduced Color Space 50 47

Poor Inherent Features Abnormal Pupil Shape 45 36

Total 1928 1624

Table 1: Number of times each problem was encountered during the manual inspection.

Number of

Problems

Number of

Images

0 505

1 1161

2 329

3 35

4 1

5 0

Table 2: Number of times an image had a specific number of problems.

Table 1 lists most of the problems identified during the manual inspection of the images as well as their frequency of

occurrence. A detailed description of each of these problems is provided in later sections. The right-most column lists

the number of searches where at least one of the paired images had the listed problem. Many of the images suffer from

multiple problems, which is why the sum of the number of problems encountered is greater than the number of images.

There were an average of 1.90 problems per image. The table shows that compression artifacts are the most common

problem. More than half of the searches in the failure set involve at least one image that is highly compressed. The second

most common problem is specular reflections (i.e. Purkinje images), which were conspicuous in 280 searches. Although

specular reflections adversely affect recognition, by themselves they were typically not enough to cause a search to miss.

Only 54 of the searches in the failure set were marked as having specular reflections as the sole problem.

Table 2 shows how often a search from the failure set suffers from a specific number of problems. If both the search image

and the enrolled image suffer from the same problem, it was counted twice. Twenty-seven searches were not marked as

having any problems, but this does not necessarily mean that the images involved in these searches were good quality.

Problems such as burnt out LEDs and Bayer filter effects could not always be identified by the inspector and are therefore

not represented in the table. 389 searches involved only one iris image that suffered from a marked problem while for 597

searches, both images were marked as having a problem. The fact that the greater dataset containing two million subjects

did not contain any detected cases of aniridia (the absence of an iris) suggests that either the images, capture protocol, or

sampling method, involved some type of filtering.

4
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

3.1 Poor Presentation

3.1.1 Reflective Glare

(a) (b)

Figure 2:

Specular reflections of the background scene could be seen in many of the iris images. These reflections, known as Purkinje

images, are especially apparent when a large fraction of the light that is reflected off of the eye originates from the scene

in front of the subject. An image of an eye can contain up to four Purkinje images (i.e. four kinds), where each image is

created by light reflecting off of a different surface of the eye. The reflections that can be seen in the iris images are Purkinje

images of the first kind because they are caused by light reflecting off of the anterior surface of the cornea. First Purkinje

images are quite common in the overall dataset, although in most cases their presence did not appear to cause much

difficulty for the automated matchers. The failure set contains 304 images involved in 280 searches where Purkinje images

are very conspicuous (examples are shown in Figures 2(a) and 2(b)). For 160 of the searches, the presence of Purkinje

images was the only problem identified with either image, suggesting that they may be the primary reason for the miss.

Purkinje images create false features on the surface of the iris that are difficult for automated matchers to distinguish

from true iris features. The ideal way to avoid capturing an image with Purkinje artifacts is to ensure that the camera is

the dominant source of illumination. Fluorescent bulbs and LED lamps emit less light in the infrared spectrum and are

therefore less likely to introduce Purkinje images. In an outdoor environment the ambient illumination is rarely under

control, and little can be done to avoid capturing images with Purkinje images. Since the reflections are typically of the

scene in front of the subject, the camera operator can position the subject to face a uniform and featureless background.

The operator can also position himself such that the sun is to his back to reduce the amount of solar illumination that

reflects off of the scene and onto the eye (although admittedly these options may sometimes be mutually exclusive). Some

iris cameras extend around the eye(s) to block ambient illumination.

Purkinje images sometimes reveal information about the environment in which the image was captured. For example,

the reflections on the iris in Figure 2(b) reveal that the image was captured outdoors in front of an archway, and that the

operator was using two hands to hold the camera. None of the Purkinje images in the data analyzed show enough detail

to cause privacy concerns (e.g. they never show distinct facial features or large amounts of fingerprint area).

Recommendations: The iris camera should be the dominant source of illumination. Operators should be trained to reduce ambient sources of illumination when possible and to recognize the presence of specular reflections of the ambient

environment. To minimize specular reflections in an outdoor environment, the camera operator should position himself

such that his back is to the sun and the subject is facing a featureless scene.

5
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

(a) (b)

Figure 3: Examples of images with rotated irides.

3.1.2 Rotation

The failure set contains 244 images where the iris is highly rotated. The iris was deemed highly rotated if the rotation is

greater than roughly 20 to 25 degrees according to a visual assessment. For 126 searches, rotation was the only problem

identified with either image. High amounts of rotation occur when either the camera is not held fully upright or the

subject is not positioned properly relative to the camera. Most of the iris recognition algorithms compare images over a

limited range of possible rotations. If the difference in rotation for a mated comparison is beyond this range, then the iris

images will fail to match. One way to solve this problem is to expand the range of rotational disparities that are tested,

but doing so increases the probability that a non-mated comparison will result in a false match. Daugman [3] predicts the

probability of a false match as

Fn(x) = 1 − [1 − F1(x)]n(1)

where n represents the number of orientations tested, and F1(x) represents the probability of a false match for any single

orientation at threshold x. When the false match rate is low, the relationship is approximately linear:

Fn(x) ≈ nF1(x) (2)

Varying the value for n introduces a trade-off, where higher values increase the probability that an individual will be

recognized, but also increase the probability that a non-mated comparison will result in a false match. Higher values of n

may also increase the time it takes to perform a comparison since more orientations are tested.

Most of the highly rotated iris images were captured by one-eye cameras. Two-eye cameras require the operator to hold

the camera approximately parallel to the subject’s face so that both eyes can be captured at once (this is more true for

binocular style camera than single-lens ones). They also enable easier measurement of the rotation that does exist from an

analysis of the orientation of the inter-pupilary line. Thus, a correction of the rotation is possible. This does not prevent

the operator from holding the camera upside down, but it does appear to eliminate high amounts of rotation in properly

acquired images. In any case, a rotated iris can be manually ”unrotated” post-capture.

Recommendations: A single-eye camera is much more likely to capture a highly rotated iris than a dual eye camera – and

introduces the potential for mislabeling the eyes (your right or mine). Hence, dual eye cameras are preferred, all other

things being equal. For cases in which a single eye camera is used, rotation can be dealt with in various ways. In order

of preference: 1) rotation can be minimized during the capture phase by ensuring that the camera is held upright and the

subject’s head is not tilted, 2) if the image is already captured and stored, the iris can be manually ”unrotated”, 3) the

matching algorithm can be configured to compare iris templates over a more relaxed range of rotation disparities.

There is some discussion in the literature on automated methods for measuring eye rotation in an image [13], although

none of the quality algorithms submitted to IREX II [19] attempted to measure it. If single eye cameras must be used, these

6
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

methods should be explored.

3.1.3 Occlusion

(a) (b) (c)

Figure 4: Examples of occluded irides.

Occlusion is one of the most common problems, which was found in 236 images in the failure set. An iris was deemed

highly occluded if the intrusion extends into the pupil region. For 108 of the searches, occlusion is the only problem

identified in either image. The occlusion typically comes from the upper eyelid and is often accompanied by further

occlusion and shadowing from the eyelashes. Other sources of occlusion are the lower eyelids, fingers, and parts of the

iris camera (see Figure 4(b)). Hotspots (i.e. diffuse reflections) also occlude parts of the iris, but are not addressed in this

section.

Occlusion introduces two difficulties for automated matching: 1) it makes correct segmentation of the iris more challenging, and 2) it obscures useful portions of the iris texture. The IREX I and IREX II reports both determined that high

amounts of occlusion increase mated comparison scores, and sometimes decrease non-mated scores. Iris recognition algorithms typically mask out occluded parts of the iris before generating a template. Toward this end, several methods of

occlusion detection have been proposed in the literature [4, 8, 15].

Although images with a high amount of occlusion are common in the failure set, the vast majority of images from the

overall dataset contain very little occlusion. In many of the images, the eyes were held open by a pair of fingers (see

Figure 4(c)). Most likely this was done to get the acquired image to pass the internal quality checks of the iris camera.

Many iris cameras perform real-time quality assessment of the images they capture and will not return an image if a high

amount of occlusion is detected.

Recommendations: Operators should be trained to instruct their subjects to open their eyes wide when their irides are

captured. IREX II found that automated algorithms for measuring the amount of useable iris area have potential. If future

cameras are designed with built-in occlusion detection, they could provide direction to operators to reacquire images that

are highly occluded.

3.1.4 Blur

The failure set contains 91 images involved in 88 searches where the iris is noticeably blurred. Examples are shown in

Figure 5. Blur occurs when either 1) the subject’s eye is outside of the ideal focal range, or 2) the eye or the camera is moving

when the image is acquired. While IQCE found that motion blur and defocus blur have similar effects on recognition

accuracy (and thus do not require separate metrics in the ISO/IEC 29794-1 standard), it’s still useful to distinguish between

the two to determine the underlying cause. Motion blur is more likely to occur if the camera has a long exposure time. It

can be difficult to visually distinguish motion blur from out-of-focus blur. One way of identifying motion blur is to see if

the hotspots in the image appear as streaks, as is the case in Figure 5(a).

7
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

(a) (b)

Figure 5: Examples of blurry images from the failure set.

IREX II found that blur detrimentally affects recognition accuracy. One prior study [16] showed that recognition algorithms may be able to tolerate moderate amounts of blur in the absence of other problems. However, blur was rarely the

only problem with the images in the failure set. For example, 59 of the 91 blurry images (almost two-thirds) contain an iris

that is also highly occluded. Although a blurry image is easy to detect, little can be done to correct for the blur once the

image has been captured and stored.

Recommendations: Operator training should include examples of blurred images so that they understand this issue. They

should be instructed to hold the camera as steady as possible when capturing images and to keep the subject within the

capture volume of the camera, which the operator must know. Operators should instruct subjects to stand still and look

straight ahead during the capture. Several automated methods for measuring blur in images exist, many of which were

evaluated as part of IREX II. There may be a benefit to incorporating automatic blur detection into iris cameras as a way

to provide real-time feedback to the operator.

3.1.5 Contact Lenses

(a) (b) (c)

Figure 6: Examples of irides obscured by patterned contact lenses (a and b) and a crinkled transparent lens (c).

Six iris images in the failure set contain patterned contact lenses. In one case, a patterned contact is present in both the

searched image and the enrolled image. The lenses were easy to spot because their patterns were printed in a dot-matrix

format that is quite distinct from natural iris patterns (see Figures 6(a) and 6(b)). These fake patterns obscure the true

features of the iris and make it extremely difficult, if not impossible, to accurately match the iris.

The problem of patterned contact lenses became apparent during the ICE 2006 Evaluation [14] when it was noticed that

some of the images in the challenge sets contained similar dot-matrix styled patterned contacts. The ideal way to handle

patterned contact lenses is to detect them before the iris is captured so that the subject can be instructed to remove them.

8
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

Daugman [3] suggests that contacts with the dot-matrix pattern can be detected by searching for areas of spurious energy

in the Fourier plane that correspond to the sizes and periodicities of the dots. Lee and Park [9] propose a method of

detecting fake iris patterns based on their 3D structure, but it requires the use of a specialized camera designed for the

purpose. Since most patterned contacts are mass produced, it is not beyond possibility that two subjects wearing the same

particular model of contact lens might, at some point, be compared to one another. No studies have been published on

whether this ever results in a low comparison score (the one case in this study resulted in a high dissimilarity score). Any

investigation into the issue should consider patterned contacts that are weighted to maintain a specific rotation (since

patterns are typically not radially symmetric).

A patterned contact is likely to present a level of difficulty proportional to the amount of iris texture that it covers. An

opaque lens that completely covers the iris makes proper identification impossible. Small parts of the true iris texture may

become visible if the contact is displaced (as in Figure 6(a)) or if the pupil constricts beyond the pattern of the lens (as

in Figure 6(b)). Many patterned contacts only occlude some of the underlying iris texture, which leaves some of the real

texture still visible.

Transparent contacts were present in the iris dataset, but their effect on recognition accuracy did not appear to be significant compared to other sources of error. Only one comparison in the failure set involved transparent contact lenses, and

the primary cause of failure was a high amount of motion blur in one of the images. There are specific situations where

transparent contact lenses can introduce difficulties. Sometimes the boundary of the contact lens may make proper localization of the sclera more difficult. Soft contacts occasionally become crinkled or partially dislodged, especially when they

are dry or immediately after the subject blinks. Figure 6(c) shows an example of a crinkled contact, although the image is

not in the failure set. Baker et. al. [1] suggest that the various artifacts and optical distortions introduced by contacts can

have a mild to severe effect on the Hamming distance, depending on the type of contact (hard, soft, etc.).

Tinted contact lenses are typically transparent in the infrared spectrum, where iris recognition systems work; hence they

normally produce no more difficulty than a standard transparent lens.

Recommendations: Correctly worn transparent (clear or tinted) contact lenses are generally not problematic for iris recognition; as noted later, lenses that are turned over/crinkled can be problematic. Patterned/opaque contacts are a problem

and should be removed before iris image capture. Many patterned/opaque contacts are reasonably obvious to a trained

operator. In the short term, operators should be trained in the detection of such contacts. For the longer term, there

are experimental methods proposed in the literature for automatically detecting patterned/opaque contacts. Automated

detection of patterned contacts would relieve camera operators from the burden of having to detect them.

3.1.6 Off-Axis Gaze

(a) (b)

Figure 7: Examples of images with off-axis gazes.

The failure set contains 39 images where the eye’s gaze is non-axial (see Figure 7). For this discussion, non-axial (sometimes

9
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

referred to as ”off-axis” or ”non-frontal”) means that the optical axis and the line joining the camera and the eye differ

considerably. Essentially, the subject was not looking at the camera at the time of capture. Images with off-axis gazes

were rare in the overall dataset. In addition to instructions provided by the operators, the cameras may have had internal

quality checks that forced a recapture if the gaze was off-axis. Recognition algorithms perform better on off-axis images

when deformation models are used to process them into on-axis views [17, 11]. The correction models often assume the

iris undergoes an affine transformation that is estimable from the gaze direction. In reality, the iris surface is subjected

to non-linear distortions from a variety of sources (the cornea, barrel distortion from the photographic lens, changes in

size with distance, etc). Furthermore, the iris surface is not entirely flat and can change appearance when viewed from

different angles. The IREX II report found that recognition algorithms are unable to correct for all of these phenomena.

Deformation models are useful for producing matchable templates from off-axis gazes, but the ideal solution is to capture

an on-axis view to begin with.

The inspectors found that the failed matches for these cases were often due to an improper localization of the iris. Most

methods of iris localization assume the iris is approximately annular in shape, but when viewed off-angle, the pupil and

sclera boundaries become much more elliptical in appearance. Several methods of localizing the iris in off-axis images

have been proposed [18, 21, 10].

Recommendations: The ideal option is proper field operator training. Camera operators should be trained to instruct the

subjects to look straight ahead when capturing an image. Operators should also be trained to review the images. The

IREX II evaluation found that automated algorithms for detecting off-axis gazes have potential. Consideration should be

made into incorporating them into deployed systems – to provide real time feedback to the operators. For images that

have already been collected, a human analyst can attempt a manual post-capture rectification of the off-axis image given

the appropriate tools [12].

3.1.7 Dilation Change

(a) Searched Image (b) Enrolled Image

Figure 8: An example of a search in the failure set where the difference in pupil dilation is large.

The failure set contains 84 highly constricted pupils, 44 highly dilated pupils, and 8 searches where an iris with a highly

dilated pupil was compared to an iris with a highly constricted one. In 5 of the 8 searches, the difference in dilation was

the only problem identified with either image. The IREX I and IREX II evaluations found that large differences in dilation

make recognition more difficult.

Although extremely dilated and constricted pupils can sometimes pose difficulties during iris segmentation, we suspect

that the failures were more often due to the non-linear deformations that occur to the iris surface when the pupil size

changes. Most iris matchers assume the deformations occur linearly along the radial direction of the iris. This is a fair

approximation, but it does not account for all of the changes that occur. For example, when the pupil dilates and constricts,

crypts often open and close in a way that introduces circumferential deformations.

10
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

Certain properties of the iris ensure that no deformation model can ever fully compensate for all of the deformations that

occur. First, variations in pupil size change both the direction and shape of the stromal fibers that compose the iris, altering

the way in which they overlap. Second, pupil dilation generates lateral pressure that causes some parts of the iris surface

to fold over other parts, especially at circular contraction folds. Both of these phenomena change the apparent texture of

the iris.

The amount of pupil dilation is commonly measured as a simple ratio [7]

D = Rp/Ri (3)

where Rp and Ri are circular estimates of the pupil and limbus radii respectively. The authors of the IREX I evaluation

report [5] proposed the following metric for measuring that amount of dilation change for a comparison

∆D = 1−

 

R

(2)

i

R

(1)

i

! R

(1)

i −R

(1)

p

R

(2)

i −R

(2)

p

!

= 1−

1−D(1)

1−D(2) (4)

where D(1) > D(2) is assumed without loss of generality. The first form of the equation contains a pair of ratios. The

first ratio is a scaling factor to compensate for possible differences in magnification. The second divides the annular

width of the first iris by that of the second, which provides a direct measurement of the amount of radial stretching that

occurs within the annular region. This may be a more accurate predictor of matching performance than simply taking the

absolute value of the difference in dilation ratios between samples.

The dilation amount depends primarily on the pupillary light reflex. In outdoor environments where lighting cannot be

controlled, little can be done to change the amount of pupil dilation. Iris images compare best when the pupils have

similar amounts of dilation. Since it is difficult to predict what lighting conditions will be like during future captures, it is

best to always try to perform the capture under normal lighting conditions.

Recommendations:

Operators should be trained to recognize the effect of lighting on pupil dilation: Dim ambient light will cause the pupil to

dilate while excessive lighting will cause the pupil to constrict. The operators should be instructed to capture the iris under

normal lighting whenever possible. If the subject’s iris is extremely dilated or constricted due to a non-physiological cause

(e.g. from certain drugs or opiates), then capture should be reserved for another day or time, if possible. For high-value

cases, an expert human analyst may be able to adjust the algorithm parameters or segmentation boundaries to ensure that

an accurate template is generated. Research into methods for modeling and compensating for changes in pupil dilation is

ongoing [2]. As these methods become available, they should be tested in operational environments.

3.1.8 Small and Large Iris Radii

Some of the segmentation algorithms had difficulty localizing the sclera-iris (limbus) boundary in images where the iris

was very large or very small. This was especially true for the images having pixel dimensions of 330-by-330, where

the limbus diameter is typically around 60% of the image width. Note that this is not the same problem as localizing a

partially cropped iris (which almost never occurred in the test dataset). The likely reason for the failed segmentations is

that the algorithms were simply not calibrated to search for limbus radii outside of a certain range. The problem is easy to

rectify by expanding the search range. Since very large and very small limbus radii are sometimes indicative of the subject

standing too close to or far from the iris camera, the preferred solution may be to make an adjustment at capture time. The

camera could provide feedback to the operator or subject indicating whether the subject’s eye is at a proper distance from

the camera.

Recommendations: Operators should be trained to know the capture volume of the camera – height, width, depth; and to

understand that it is important to place the subject’s eyes within that volume. Tests of iris segmentation algorithms on operationally relevant data would identify whether such algorithms are effective at providing useful feedback to operators.

11
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

On the back end, improvements in iris segmentation could enable operation over a larger range of radii.

3.2 System Problems

3.2.1 Lossy Image Compression

(a)

File Size

Frequency

0 10000 30000 50000 70000

0.00

0.02

0.04

0.06

0.08

0.10

0.12

mean

(b)

Figure 9: On the left are examples of highly compressed images. The graph on the right shown the distribution of file sizes

for the overall dataset.

Lossy compression was applied to all of the images in the dataset (since they are stored as JPEGs), but only some of them

were compressed enough to seriously degrade recognition performance. Figure 9(b) plots the histogram of file sizes for

the approximately 4 million images in the test set. The smaller peak on the left corresponds to those images that were

severely compressed, which comprise about 6% of the dataset. Examples of the highly compressed images are shown in

Figure 9(a). The iris textures in these images suffer from severe compression artifacts. Note as well that the images were

cropped such that the iris is always in the center of the image. The IREX I evaluation investigated the effect of image

compression on recognition performance and determined that cropped JPEG images compressed to such small file sizes

are unsuitable for automated matching. The images would have been suitable for matching had they been formatted in

one of the standard compression formats defined in the ISO/IEC 19794-6:2011 standard [6]. Since the highly compressed

images are unsuitable for operational use, they were excluded from the primary test set. They were, however, retained in

the 17017 images used to generate the failure set.

The failure set contains 743 images involved in 557 searches that are highly compressed. Despite their poor quality, the

matching algorithms were still able to correctly match them some of the time. Recognition performance over the highly

compressed images is provided in the main IREX III evaluation report. The images show evidence of being processed in

other ways as well. The iris radii are all very similar, suggesting the images may have been scaled. Specular hotspots from

the iris camera are also masked out. The masking was typically performed inside the pupil region where it is unnoticeable,

but in some cases perfectly circular dark spots can be seen over other areas in the images. The bottom image in Figure 9(a)

shows an example of one of these dark spots situated directly above the pupil (although it may be difficult to see in paper

12
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

print-outs). Interestingly, the dark spots do not consist of a single color as one would expect from a masking operation.

The masking may, in fact, be the result of a contrast adjustment applied over the hotspots. The irregularities may also be

artifacts introduced by compression.

Recommendations: If it is necessary to reduce the storage size of iris images, convert them to one of the compact formats defined in ISO/IEC 19794-6:2011. At this time, the standard does not recommend the removal of hotspots prior to

compression.

3.2.2 Posterization (Quantization)

(a)

Density

0 50 100 150 200 250

0.00

0.05

0.10

0.15

Pixel Intensity

(b)

Figure 10: On the left are examples of posterized images from the failure set (artifacts may not present well on paper

printouts). The graph on the right shows the histogram of pixel intensities for the bottom iris image.

Posterization (also referred to as quantization and reduced color space) occurs when the number of distinct colors/tones

represented in the image is reduced to a relatively small number, such that gradual changes in color tone appear as abrupt

transitions from one color to another. This process was originally developed for creation of posters from photographs,

whence comes the name. It is now a common option in digital image processing programs (such as PhotoShop, IrfanView,

and Gimp). The presence of posterization in an image can be deliberate, or an unintended side effect of an image processing step such as lossy compression. The failure set contains 50 images involved in 47 searches that display obvious signs

of posterization (see Figure 10(a) for examples). Fine details in these images are replaced with featureless areas of uniform

color. The inspectors believe that posterization is the primary reason that these images failed to match.

Figure 10(b) shows the brightness intensity histogram for a posterized image. Other posterized images had similar his13
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

tograms, but the peaks often differed in number and location. The peaks appear to ”bleed” into adjacent intensities due

to additional compression artifacts introduced when the posterized images were converted to JPEGs. JPEG is a lossy

compression format that changes the intensities of some pixels in order to store the image more compactly. The IREX

II report proposes a metric referred to as GRAY SCALE SPREAD that seems appropriate for quantifying the amount of

posterization in these images.

The images may have been posterized intentionally in an attempt to make them easier to compress. When fewer distinct

shades of gray are used, images can be stored at lower bit-depths, thus requiring less space to encode. However, the images

in the current dataset were always stored at 8 bits-per-pixel, regardless of how many distinct brightness intensities were

actually used. Posterization also failed to make the images any easier to compress as JPEGs since it did not substantially

reduce the amount of high frequency information in the images, which is expensive to encode. Guidelines for the efficient

compression and storage of iris images are provided in ISO/IEC 19794-6:2011.

Once an iris image is posterized, the texture information contained in the iris that is lost cannot be recovered without

capturing a new image.

Recommendations: Iris images should not be posterized. If it is necessary to reduce the storage requirements of iris

images, convert them to one of the compact formats defined in ISO/IEC 19794-6:2011. Automated detection of posterized

images is possible, as described in the IREX II report.

3.2.3 Ground Truth Errors

The inspector determined that for 19 of the searches, the search image and its corresponding enrolled mate were not of

the same person’s iris. In other words, at least one of the paired images was assigned an incorrect subject identifier. The

determination was made based on a visual inspection of the two irides and their surrounding ocular regions. If it was

clear that the two irides are from different people, the inspector marked the pair as having a ”ground truth” error. These

19 cases comprise 19/17017 ≈ 0.11% of the searches in the failure set.

3.2.4 Bayer Filter Defect

The image in Figure 11 shows a dithering pattern that most likely resulted from a misbalance in the gains for the Bayer

color channels. When a camera design for color images using the Bayer filter method is adapted for grayscale, the Bayer

filter is left off the sensor and the Bayer channels (R,B,GB,GR) must be balanced. Failure to properly balance the channels

will result in a noise pattern that repeats on a scale of 2 pixels. The effect occurs at a high spatial frequency in the underlying images, but we see it at a lower frequency in these images due to a Moire interaction between the sampling rate for

the display and the underlying sensor pattern noise.

The pattern noise may be outside of the passband of the matching algorithms, in which case it would have little effect on

matching performance. However, it IS noise and should be avoided. The presence of this problem in the image stream

indicates a lack of attention to overall quality of the images. Although it is possible that this noise was introduced by

other processes, the sensor is the most likely culprit. The prevalence of this problem in the failure set was not investigated

during the manual inspection.

Recommendations: Images that appear to display Bayer artifacts should be provided to the camera vendor for analysis

and remediation in the camera system. It is possible to post-process the images to mitigate this problem.

3.2.5 Low Contrast

Figure 12 shows a good quality search image (left) and an low quality enrollment image (right). The enrollment image

suffers primarily from low contrast. The occlusion of the camera lens by the operators finger in the upper left may have

contributed to the low contrast. The size of the pupil suggests that there is a fair amount of ambient illumination, but this

14
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

Figure 11: The dithering pattern in this image is likely introduced by a Bayer filter camera defect; see text for description.

does not necessarily mean that there is a sufficient amount of near-IR for a good quality capture. The Hamming distance

for this comparison is 0.37, which corresponds to a false match rate of about 1 in 600. This is a case where adjudication by

a human operator might provide actionable intelligence.

Recommendations: In general, camera operators should be trained in good image acquisition practices. In this case, they

should be trained to avoid capturing an image when their finger is partially blocking the camera lens. An automated

algorithm may be able to detect this by measuring contrast. For cases in which there is no apparent reason for a low

contrast image, an example should be provided to the camera vendor(s) for comment and explanation.

3.2.6 Broken LEDs

Figure 13 show two images captured by the same type of camera, but the left image has a lower number of light emitting

diode (LED) reflections. Our inference is that some of the LEDs were burnt out when the left image was captured. Burnt

out LEDs lead to insufficient illumination of the iris, which will make recognition more difficult. The prevalence of burnt

out LEDs in the failure set was not investigated during the manual inspection and would be difficult to ascertain in some

images. It may be possible to detect burn-out LEDs based on image properties. For example, ISO/IEC 29794-1 defines

requirements for contrast and gray-scale utilization that may not be met by a camera operating with broken LEDs.

Recommendations: Camera operators should be trained in the basic characteristics of good quality iris images so that

they can make rough, real-time assessment of the images they collect. In this case, the camera operators should be trained

to recognize the nominal specularity pattern of their camera(s) and to realize when the specularity pattern is symptomatic

of a problem.

15
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

(a)

Figure 12: Example of a case where the enrollment image is low contrast.

(a) (b)

Figure 13: Example of an iris captured by a camera with burnt out LEDs (left), and an iris capture by the same type of

camera with functioning LEDs (right).

16
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

(a) (b)

Figure 14: Example images from the failure set where the pupil shape is abnormal.

3.3 Poor Inherent Features

3.3.1 Abnormal Pupil Shapes

The failure set contains 45 images involving 36 searches where the pupil’s shape is abnormal (i.e. deviates significantly

from circular). Examples of abnormal pupils are shown in Figure 14. Abnormal pupil shapes are not restricted to the

failure set. However, an inspection of 1000 randomly selected images (not from the failure set) revealed only one where

the pupil’s shape is abnormal.

Occasionally, the abnormal shape was caused by a crinkled contact lens that created optical distortions. In all other

cases, the pupil’s coverage area appears to extend into regions normally occupied by the iris (as can be seen Figure 14).

The abnormalities are most likely due to congenital defects such as Coloboma. (Watters [20] identifies several medical

conditions that are likely to impact the performance of iris recognition.) Most of the time, the pupil shape was abnormal

in only one of the subject’s eyes (either the left or the right).

The irregular shape of the pupil can make it difficult to localize since most methods of boundary detection assume that the

pupil is approximately circular in shape. Another obvious disadvantage to the abnormal pupil shape is that there tends

to be a reduced amount of visible iris texture. It would be interesting to note how the iris texture changes in response

to changes in pupil size for irregularly shaped pupils, but unfortunately the dilation amount varied little across different

images of the same iris.

Recommendations: There is little that an operator can do if the subject’s eye contains a chronic abnormality. For acute

conditions (such as certain traumas) the subject can be instructed to return when the eye has recovered.

3.3.2 Disease and Trauma

The iris can suffer damage in response to diseases or blunt injuries to the eye. Additionally, the cornea can become

temporarily or permanently scarred, which may also alter the appearance of the iris. Figure 15 shows an iris that appears

to have suffered some type of trauma between captures. The dark blotting in lower left was enough to raise the Hamming

distance between the before and after images to 0.36. This corresponds to a false match rate of about 1 in 2600. This is a

case where adjudication by a human operator might provide actionable intelligence.

Recommendations: There is little that an operator can do with regard to disease or trauma of the subject’s eye (other than

to possibly delay iris capture until the eye has healed). Subsequent adjudication by a human analyst can be effective. With

appropriate tools, a human analysis can at least mask out the areas of the iris that show visible signs of injury.

17
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

(a)

Figure 15: An example of an iris that suffered trauma between captures.

18
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

4 Poor Quality due to Multiple Problems

(a)

Figure 16: An example of a search that failed due to multiple problems.

While each of the previous sections focus on a single source of poor quality, this section addresses the problem of compounding factors that lead to failure. It was previously noted that nearly two-thirds of the blurry images in the failure set

also have high amounts of occlusion. This suggests that blur exacerbates the detrimental effect of occlusion and vice versa.

Figure 16 shows another example of a search that likely failed due to multiple problems with the images. Both the search

image (left) and its enrolled mate (right) are mislabeled as left irides. In this case, two wrongs do make a right in-so-far

as making a correct match is concerned. However, the images suffer from other problems. The search image contains noticeable compression artifacts and the enrolled mate is posterized. Nevertheless, the Hamming Distance between the two

images is ∼0.34, just above a nominal cutoff of ∼0.33. The probability of a nonmated comparison producing a Hamming

Distance at or below this value is ∼1:40000. How well the comparison would have faired in the absence of one or more of

these problems is unknown. This is again a case where a human analyst could provide actionable intelligence.

5 Final Comments

Despite the quality problems in the operational dataset, the algorithms are usually robust enough to handle the images

(see accuracy results in the main IREX III report). This is a mixed blessing, since greater tolerance of poor image quality

can lead people to become less attentive to maintaining high standards of quality when handling image data. Poor quality

images will inevitably lead to higher false non-match rates and a higher incidence of cases where a potentially dangerous

19
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

individual is not recognized. Quality control procedures should be established to monitor the quality of the data streams

and to ensure that prompt corrective action is taken when those streams depart significantly from defined norms.

• In general, the iris recognition algorithms work on the operational data provided for use in this study.

• The failure cases investigated in this report highlight areas where modifications to operational procedures would

improve recognition performance.

• Some of the images from the operational dataset were originally stored in 24 bit RGB format. This is poor practice,

especially if there are bandwidth and storage space restrictions, and considering that iris cameras typically capture

images natively in 8 bits-per-pixel.

• If compression of iris images is necessary, use the compact formats defined in ISO/IEC 19794-6:2011 (and equivalently defined in ANSI/NIST ITL 1-2011). Images should not be posterized.

• If an iris image is cropped to the point that the medial canthus (the corner of the eye closest to the nose) is no longer

visible, it will likely be impossible to determine whether the eye is a left or a right. Considering the prevalence of eye

mislabelings in the dataset, we suggest that images not be cropped beyond the point at which the medial canthus

is no longer visible. This may not be possible if storage limitations require that the images be stored in one of the

compact formats defined in the ISO/IEC 19794-6 standard.

A Summary of Recommendations

This appendix consolidates the recommendations from each of the subsections in section 3.

1. Contact lenses: Correctly worn transparent (clear or tinted) contact lenses are generally not problematic for iris

recognition; as noted later, lenses that are turned over/crinkled can be problematic. Patterned/opaque contacts are a

problem and should be removed before iris image capture. Many patterned/opaque contacts are reasonably obvious

to a trained operator. In the short term, operators should be trained in the detection of such contacts. For the longer

term, there are experimental methods proposed in the literature for automatically detecting patterned/opaque contacts. Automated detection of patterned contacts would relieve camera operators from the burden of having to detect

them.

2. Occlusion Operators should be trained to instruct their subjects to open their eyes wide when their irides are captured. IREX II found that automated algorithms for measuring the amount of useable iris area have potential. If

future cameras are designed with built-in occlusion detection, they could provide direction to operators to reacquire

images that are highly occluded.

3. Rotation: A single-eye camera is much more likely to capture a highly rotated iris than a dual eye camera – and

introduces the potential for mislabeling the eyes (your right or mine). Hence, dual eye cameras are preferred, all

other things being equal. For cases in which a single eye camera is used, rotation can be dealt with in various ways.

In order of preference: 1) rotation can be minimized during the capture phase by ensuring that the camera is held

upright and the subject’s head is not tilted, 2) if the image is already captured and stored, the iris can be manually

”unrotated”, 3) the matching algorithm can be configured to compare iris templates over a more relaxed range of

rotation disparities.

There is some discussion in the literature on automated methods for measuring eye rotation in an image [13], although none of the quality algorithms submitted to IREX II attempted to measure it. If single eye cameras must be

used, these methods should be explored.

4. Off-axis gaze: The ideal option is proper field operator training. Camera operators should be trained to instruct the

subjects to look straight ahead when capturing an image. Operators should also be trained to review the images.

20
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

The IREX II evaluation found that automated algorithms for detecting off-axis gazes have potential. Consideration

should be made into incorporating them into deployed systems – to provide real time feedback to the operators.

For images that have already been collected, a human analyst can attempt a manual post-capture rectification of the

off-axis image given the appropriate tools [12].

5. Reflective Glare: The LEDs on the camera should be the dominant source of illumination. Operators should be

trained to re- duce ambient sources of illumination when possible and to recognize the presence of specular reflections of the ambient environment. To minimize specular reflections in an outdoor environment, the camera operator

should position himself such that his back is to the sun and the subject is facing a featureless scene.

6. Dilation Change: Operators should be trained to recognize the effect of lighting on pupil dilation: Dim ambient

light will cause the pupil to dilate while excessive lighting will cause the pupil to constrict. The operators should

be instructed to capture the iris under normal lighting whenever possible. If the subject’s iris is extremely dilated or

constricted due to a non-physiological cause (e.g. from certain drugs or opiates), then capture should be reserved for

another day or time, if possible. For high-value cases, an expert human analyst may be able to adjust the algorithm

parameters or segmentation boundaries to ensure that an accurate template is generated. Research into methods for

modeling and compensating for changes in pupil dilation is ongoing [2]. As these methods become available, they

should be tested in operational environments.

7. Blur: Operator training should include examples of blurred images so that they understand this issue. They should

be instructed to hold the camera as steady as possible when capturing images and to keep the subject within the

capture volume of the camera, which the operator must know. Operators should instruct subjects to stand still and

look straight ahead during the capture. Several automated methods for measuring blur in images exist, many of

which were evaluated as part of IREX II. There may be a benefit to incorporating automatic blur detection into iris

cameras as a way to provide real-time feedback to the operator.

8. Small and large iris radii: Operators should be trained to know the capture volume of the camera – height, width,

depth; and to understand that it is important to place the subject’s eyes within that volume. Tests of iris segmentation

algorithms on operationally relevant data would identify whether such algorithms are effective at providing useful

feedback to operators. On the back end, improvements in iris segmentation could enable operation over a larger

range of radii.

9. Lossy image compression: If it is necessary to reduce the storage size of iris images, convert them to one of the

compact formats defined in ISO/IEC 19794-6:2011. At this time, the standard does not recommend the removal of

hotspots prior to compression.

10. Posterization: Iris images should not be posterized. If it is necessary to reduce the storage requirements of iris

images, convert them to one of the compact formats defined in ISO/IEC 19794-6:2011. Automated detection of

posterized images is possible, as described in the IREX II report.

11. Bayer filter defect: Images that appear to display Bayer artifacts should be provided to the camera vendor for

analysis and remediation in the camera system. It is possible to post-process the images to mitigate this problem.

12. Low contrast: In general, camera operators should be trained in good image acquisition practices. In this case, they

should be trained to avoid capturing an image when their finger is partially blocking the camera lens. An automated

algorithm may be able to detect this by measuring contrast. For cases in which there is no apparent reason for a low

contrast image, an example should be provided to the camera vendor(s) for comment and explanation.

13. Broken LEDs: Camera operators should be trained in the basic characteristics of good quality iris images so that

they can make rough, real-time assessment of the images they collect. In this case, the camera operators should be

trained to recognize the nominal specularity pattern of their camera(s) and to realize when the specularity pattern is

symptomatic of a problem.

14. Abnormal pupil shapes: There is little that an operator can do if the subject’s eye contains a physical abnormality.

For acute conditions (such as certain traumas) the subject can be instructed to return when the eye has recovered.

21
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

15. Disease and trauma: There is little that an operator can do for chronic conditions. Subsequent adjudication by a

human analyst can be effective. With appropriate tools, a human analysis can at least mask out the areas of the iris

that show visible signs of injury.

References

[1] S. E. Baker, A. Hentz, K. W. Bowyer, and P. J. Flynn. Degradation of iris recognition performance due to non-cosmetic

prescription contact lenses. Computer Vision and Image Understanding, pages 1030–1044, 2010.

[2] A. Clark and A. A. Ross. Exploring nonlinear dynamics of iris deformation. In Biometric Consortium Conference, 2011.

[3] J. Daugman. Demodulation by complex-valued wavelets for stochastic pattern recognition. International Journal of

Wavelets, Multi-resolution and Information Processing, 1:1–17, 2003.

[4] J. Daugman. The importance of being random: Statistical principles of iris recognition. Pattern Recognition, 36:279–

291, 2003.

[5] P. Grother, G. Quinn, J. Matey, M. Ngan, W. Salamon, G. Fiumara, and C. Watson. IREX: Performance of Iris Identification Algorithms. Technical report, NIST, 2011.

[6] ISO. ISO/IEC 19794-6 - Biometric Data Interchange Formats - Iris Image Data. Geneva, Switzerland, 2005.

[7] ISO. ISO/IEC 19794-6 - Biometric Sample Quality - Part 1: Framework. Geneva, Switzerland, 2009.

[8] W. Kong and D. Zhang. Accurate iris segmentation based on novel reflection and eyelash detection model. In

Proceedings of the 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2001, pages 263–

266, 2002.

[9] E. Lee and K. Park. Fake iris detection based on 3d structure of iris pattern. International Journal of Imaging Systems

and Technology, 20(2):162–166, 2010.

[10] P. Li, X. Liu, L. Xiao, and Q. Song. Robust and accurate iris segmentation in very noist iris images. In image and Vision

Computing, volume 28, pages 246–253, Feb 2010.

[11] X. Li. Modeling intra-class variation for non-ideal iris recognition. In In Springer LNCS 3832: Int. Conf. on Biometrics,

pages 419–427, 2006.

[12] J. R. Matey, D. Ackerman, J. Bergen, and M. Tinker. Advances in Biometrics, chapter 7. Springer Verlag, 2008.

[13] D. M. Monro and S. Rakshit. Rotation compensated human iris matching. In Signal Processing Applications for Public

Security and Forensics, 2007. SAFE ’07. IEEE Workshop on, pages 1 –4, april 2007.

[14] J. Phillips, T. Scruggs, A. O’Toole, P. Flynn, K. Bowyer, C. Schott, and M. Sharpe. FRVT 2006 and ICE 2006 large-scale

results. Technical report, NIST, 2007.

[15] S. Ring and K. Bowyer. Detection of iris texture distortions by analyzing iris code matching results. In Biometrics:

Theory and Applications and Systems, pages 1–6, September 2008.

[16] N. Sazonova, S. Schuckers, P. Johnson, P. Lopez-Meyer, E. Sazonov, and L. Hornak. Impact of out-of-focus blur on

iris recognition. In Proceedings of SPIE 8029, April 2011.

[17] S. Schuckers, N. Schmid, A. Abhyankar, V. Dorairaj, C. Boyce, and L. Hornak. On techniques for angle compensation

in non-ideal iris recognition. In IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, volume 37,

pages 1176–1190, 2007.

[18] E. Sung, X. Chen, J. Zhu, and J. Yang. Towards non-cooperative iris recognition systems. In ICARCV’02, pages

990–995, 2002.

[19] E. Tabassi, P. Grother, and W. Salamon. IREX - IQCE performance of iris image quality assessment algorithms.

Technical report, NIST, 2011.

[20] E. Watters. Iris pathologies medical conditions that may impact the performance of iris recognition systems. In The

Biometrics Consortium Conference (BCC), September 2011.

22
IREX III SUPPLEMENT I: FAILURE ANALYSYS April 19, 2012

[21] J. Zuo and N. Schmid. On a methodoligy for robust segmentation of non-ideal iris images. In IEEE Transactions on

Systems, Man, and Cybernetics, Part B: Cybernetics, volume 40, June 2010.

23