© CITeR

Stephanie Schuckers, Brian Walczak

January 26, 2015

Iris—State of Industry

1
© CITeR

Outline

• Basic Science

Modeling

Dilation

Individuality

Permanence

Disease

2

• Security

Liveness

Contact Lens

• Beyond Iris

Ocular

Vascular

Eye Movement

• Database Sharing

• Performance

Cross sensor

Quality

On the move

Distance

Mobile
© CITeR

Outline

• Basic Science

Modeling

Dilation

Individuality

Permanence

Disease

3

• Security

Liveness

Contact Lens

• Beyond Iris

Ocular

Vascular

Eye Movement

• Database Sharing

• Performance

Cross sensor

Quality

On the move

Distance

Mobile
© CITeR

Modeling

• Generalized model of the eye –

called ORNL eye [1]

• Model used to reconstruct offangle eye to frontal view • This model takes into 

consideration the limbus effect

• Synthetic iris dataset created 

based on eye model, 1056 

images

4

[1] Santos-Villalobos, H, et al. "ORNL biometric 

eye model for iris recognition." BTAS, 2012. 

[2] Karakaya, M, et al. "Limbus impact on offangle iris degradation.“ ICB, 2013. 

Figure. 

Illustration of how 

visible iris region 

changes with 

frontal and offangle. Solid and 

dotted lines 

represent actual 

iris boundaries 

[2]
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Modeling

• Off Angle Estimation & Correction

• Dataset: Clarkson Angle, Q-FIRE, 90 

subjects, 24800 images [1, 2]

• Off-angle image is aligned with the 

model, reprojected to frontal view

• Dataset: 125 images at different 

camera angles, 25 subjects [3]

5

Figure. The flowchart of off-angle iris 

recognition using Corneal reflections and 

multiclass SVM

[1] Johnson, P. A., et al. "Quality in face and iris research ensemble 

(Q-FIRE)." BTAS 2010.

[2] Li, Xingguang, et al. "A feature-level solution to off-angle iris 

recognition.“ ICB, 2013. 

[3] Thompson, Joseph, et al. "Off-angle iris correction using a 

biological model.“ BTAS 2013. 
© CITeR

Modeling—Iris Curvature

• Iris curvature measured in 

order to model the iris shape 

[2]

• Found that differences in iris 

curvature degrade matching 

ability

• Dataset: 201 synthetically 

generated irises of a single 

subject

6

[1] Experimental Eye Research, Vol. 86 / Issue 2. S 

Dorairaja, et al. Accommodation-induced changes in 

iris curvature, pp. 220-225. 2008.

[2] Thompson, Joseph, et al. "Effects of iris surface 

curvature on iris recognition.“ Biometrics: Theory, 

Applications and Systems BTAS 2013. 

Figure. To generate an iris surface, cubic spline 

points (red) are defined on the iris surface curve 

(green) across a meridian. The spline is then 

revolved about the center of the iris to generate a 3-

dimensional surface [1,2]
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Dilation • Under alcohol consumption, pupil 

dilations [1]

• Results show 1 in 5 subjects under 

the influence may evade 

identification by iris recognition

• IIT-D Iris under Alcohol 

Influence database (55 subjects, 

220 pre & post alcohol images)

• Difference in pupil dilation can 

affect iris recognition [2]

• Database: 955 subjects, 49936 eye 

images

7

Figure. Examples illustrating constriction and 

dilation in pupils due to alcohol consumption.

[1] Arora, S, et al. "Iris recognition under alcohol 

influence: A preliminary study." ICB 2012. 

[2] Ortiz, E, et al "A linear regression analysis of the 

effects of age related pupil dilation change in iris 

biometrics.“ BTAS 2013. 
© CITeR

8
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Eye Disorders

• Conditions causing pupil or 

iris deformation, conditions 

causing pupil or iris occlusion, 

and no iris or reduced size are 

studied

• Database: 111 images from 

the Atlas of Ophthalmology

9

Examples from (a) Axenfeld-Rieger

syndrome, (b) cataract, (c) coloboma, (d) 

epithelial cyst, and (d) synechia

Examples from (a) anophthalmia, (b) 

coloboma, (c) ectropion, (d) synechia, (e) 

corneal dystrophy, and (f) uveitis. 

McConnon, George, et al. 

"Impact of common 

ophthalmic disorders on iris 

segmentation." ICB 2012. 
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Individuality

10

Daugman J, "Probing the uniqueness and randomness of IrisCodes: 

Results from 200 billion iris pair comparisons." 2006

Threshold False Accept

Iris

Recognition

Imposter

Distribution

• Ability to differentiate 

individuals

• “Individuality”, information 

content, or “capacity”

• Very little published work

• Need large databases which 

researchers do not typically 

have access to

• Errors in the “tails”

• UAE 632,500 genuine pairs, 

> 200B imposter pairs
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Scaling

• Identification 

performance 

(1:N) function of 

database size

• More likely to 

have a false 

positive at larger 

database size

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False Negative Identification Rate (FNIR)

False Positive Identification Rate (FPIR)

Gallery Database

Dashed: 10,000

Solid: 1.5 M

Quinn, et al, IREX IV, NIST Interagency 

Report 7949, 2013

Results for IREX IV

Connection across lines are same operating point
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Wilcoxon p-value

Temporal Stability

12

P. Grother et al, IREX VI Temporal Stability of Iris Recognition Accuracy, NIST Interagency Report 7948, 2013.

S. P. Fenker et al., “Analysis of Template Aging in Iris Biometrics”, 2012.

Permanence—Study of permanence of biometric trait

e.g., 1.1% of eyes give significantly higher late scores vs. early scores for p = 0.01 ECDF (Frac. eyes with p−values < X)
© CITeR

Outline

• Basic Science

Modeling

Dilation

Individuality

Permanence

Disease

13

• Security

Liveness

Contact Lens

• Beyond Iris

Ocular

Vascular

Eye Movement

• Database Sharing

• Performance

Cross sensor

Quality

On the move

Distance

Mobile
© CITeR

Cross Sensor

• Methods to match iris 

images captured by 

different sensors 

[1, 2]

• Notre Dame CSI 

Database (676 

subjects, 264,945 

images)

• CASIA CSI Database 

(350 subjects, 14000 

images)

• IIITD Multi-Sensor 

Iris Database (104 

subjects, 832 total 

images)

14

Figure. Iris Images captured by different sensors

[1] Xiao, Lihu, et al. "Coupled feature 

selection for cross-sensor iris recognition.“ 

BTAS 2013. 

[2] Arora, Sunpreet S., et al. "On iris camera 

interoperability." BTAS, 2012. 
© CITeR

Multi-Spectral

• Iris imaging in multiple spectrums, 

visible, infrared

• WVU Multi-spectral iris data (35 

subjects, 232 images)

• UMKC Visible spectrum, (50 

subjects)

15

Figure. Sample images obtained at 

wavelengths (a) 950nm, (b) 

1050nm,(c) 1150nm, (d) 1250nm, 

(e) 1350nm, (f) 1450nm [1]

Figure. Visible (top) and red )bottom image for 

light and dark irises

[1] A. Ross, at al, "Exploring Multispectral Iris 

Recognition Beyond 900nm," BTAS 2009 

[2] V. Gottemukkula, et al, "Fusing Iris and 

Conjunctival Vasculature: Ocular Biometrics in 

the Visible Spectrum," HST 2012.

[2] Zuo, J, et al "Cross spectral iris matching 

based on predictive image mapping." BTAS 

2010
© CITeR

Quality

17

Tabassi, E., P. Grother, and W. Salamon. "Irex IIIQCE iris quality calibration and evaluation." 

Interagency report 7820, 2011.

• Reacquisition from a 

user

• Selection of the best 

sample

• Preprocessing 

selection, 

• Fusion

• Standardization of 

quality
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Quality

• Iris videos obtained at distances of 5 to 25 

feet used to analyze at non-ideal conditions

18

Figure. Full database description

[1] Johnson, P. A., et al. "Quality in face and iris research 

ensemble (Q-FIRE)." BTAS 2010.
© CITeR

Quality

• Iris segmentation performance is impacted by image quality

• Sharpness (defocus blur), motion blur and interlace, contrast of iris boundaries, 

circularity of iris boundaries, gray scale spread, and usable boundary

• Database: BioSec (3200 iris images, 200 individuals)

19

Figure. Performance of segmentation based on different quality measures

Alonso-Fernandez and Bigun. "Quality factors affecting iris segmentation and matching." ICB 2013. 
© CITeR

Distance

• Iris images acquired under 

less constraints has noise 

highly correlated with the 

bit consistency [1]

• Computationally efficient 

iris segmentation approach 

for at-a-distance and less 

constrained images [2]

• Databases: CASIA V4-

distance (131 subjects, 

935 images), UBIRIS V2 

(151 subjects, 864 images)

21

Figure. Block diagram of the developed iris 

recognition scheme. (a) Stability maps learning 

phase, (b) Matching phase.

[1] Tan, CW, and Kumar A. "Adaptive and localized 

iris weight map for accurate iris recognition under 

less constrained environments." BTAS 2013. 

[2] Tan, CW, and Kumar. "Efficient iris segmentation 

using Grow-Cut algorithm for remotely acquired iris 

images.“ BTAS 2012. 

Figure. Initial assignment of labels. (a) 

Input image, (b) Assigned labels (Cyan -

foreground; Gray – background; Black -

otherwise). 
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Distance

• Ongoing work in designing, developing system with 

less cooperative acquisition (less constraint)

• Capture images of iris up to 8 meters away, 200 pixel 

resolution across diameter

• Capture distance of 12 meters with 150 pixel resolution

• Velocity estimation, focus tracking modules: 

acquisition of moving subjects

22

Figure. Subject approaches the system, crosses 

variable checkpoints A and B, estimates his/her 

speed. Focus position is then set to a position C 

to obtain an iris of required resolution . A 

number of in-focus images are then acquired by 

changing the focus continuously based on 

subject distances estimated.

Figure. (a) shows an iris image 

capture from a subject

standing still at a distance of 6 meters 

from system

(b) shows an image from the same 

subject at 7 meters. Both cropped 

from face images.

Venugopalan, Shreyas, et al. "Long range iris 

acquisition system for stationary and mobile 

subjects." IJCB 2011. 
© CITeR

On the Move

• Fluttering shutter: 

camera's shutter flutters 

between open/close while 

exposure is accumulated 

on the sensor

• High quality image can be 

recovered with low noise 

levels with blur estimation 

and de-blurring

• Dataset: Synthetic 

experiments using NIST 

ICE dataset, real 

experiments using 600 

collected flutter shutter 

images

23

Figure. ROC curves for synthetically de-blurred images 

from the ICE dataset, using a traditional shutter (solid 

lines) and a flutter shutter (dashed lines)

McCloskey, S, Wing S, and JJelinek. "Iris 

capture from moving subjects using a fluttering 

shutter." BTAS 2010. 
© CITeR

24
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Mobile

25
© CITeR

Outline

• Basic Science

Modeling

Dilation

Individuality

Permanence

Disease

26

• Security

Liveness

Contact Lens

• Beyond Iris

Ocular

Vascular

Eye Movement

• Database Sharing

• Performance

Cross sensor

Quality

On the move

Distance

Mobile
© CITeR

Contact Lenses

• Discrimination between wearing no lens, wearing clear prescription lens, wearing 

textured cosmetic lens

• Notre Dame Cosmetic Contact Lenses 2012 dataset, 3000 training images, 1200 

verification images, LG 4000 iris camera

27

Doyle, J et al. "Automated classification of 

contact lens type in iris images.“ICB 2013.
© CITeR

Contact Lenses

• Challenge of patterned contact 

lenses: variety of textures from 

many manufacturers

• Method: Lens detection, reject cases 

with obfuscated patterns

• Existing lens detection algorithms 

improve performance: edge 

sharpness, textural features, GLCM 

features, LBP and SVM based

• IIIT-D Contact Lens Iris Database, 

6570 images from 101 subjects; no 

lens, soft lens, colored lens

28

Kohli, Naman, et al. "Revisiting iris 

recognition with color cosmetic contact 

lenses." Biometrics (ICB), 2013 

International Conference on. IEEE, 2013. 
© CITeR

Examples from Iris LivDet Database

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Results-LivDet 2013 Iris

30

Clarks

on

Warsa

w

2

Datas

et Avg

Notre

3

Datas

et Avg

ATVS 10.99 26.28 21.95

Federico 48.37 21.15 28.85 28.25 28.56

Porto 29.67 5.23 12.18

0

10

20

30

40

50

60

70

Percent Error %

Rate of misclassified Live Iris 

Images (ferrlive) for submitted 

algorithms

Clarks

on

Warsa

w

2

Datas

et Avg

Notre

3

Datas

et Avg

ATVS 62.05 7.68 30.42

Federico 11.14 0.65 5.04 7.5 5.716

Porto 7.27 11.93 9.98

0

10

20

30

40

50

60

70

Percent Error %

Rate of misclassified Spoof Iris 

Images (ferrfake) for submitted 

algorithms

D Yambay, et al, LivDet-Iris 

2013 – Iris Liveness Detection 

Competition 2013, IJCB 2014 
© CITeR

Liveness

• Iris liveness detection scheme based on quality related measures

• Based on focus, motion, occlusion, contract, pupil dilation

• Database has 50 users of BioSec baseline, 800 printed iris images and its original 

samples

31

Figure. Examples of different 

focus quality features for real 

and fake irises

Figure. Power spectrum of 

a real and a fake iris on its 

primary direction

Figure. ROI used to 

estimate the iris occlusion

Figure. Process to calculate 

local contrast

Galbally, J, et al. "Iris liveness

detection based on quality 

related features." ICB 2012. 
© CITeR

Outline

• Basic Science

Modeling

Dilation

Individuality

Permanence

Disease

32

• Security

Liveness

Contact Lens

• Beyond Iris

Ocular

Vascular

Eye Movement

• Database Sharing

• Performance

Cross sensor

Quality

On the move

Distance

Mobile
© CITeR

Beyond Iris

• Ocular are used instead of or in addition to iris 

images [1, 2, 3]

• FOCS (face and ocular challenge set), 9588 

images of 126 subjects

33

Figure. True class deformation when 

correlating an authentic class query 

image [1]

[1] Boddeti, V et al. "A comparative evaluation of iris and ocular 

recognition methods on challenging ocular images.“IJCB 2011. 

[2] Padole, C., et al. "Periocular recognition: Analysis of performance 

degradation factors." ICB, 2012. 

[3] Ross, A, et al. "Matching highly non-ideal ocular images: An 

information fusion approach." ICB 2012. 

Figure. The three processing methods for non-ideal 

ocular images [3]

Ocular
© CITeR

Beyond Iris

34

Conjunctival Vasculature

Gottemukkula V, et al “Fusing Iris and Conjunctival Vasculature: Ocular 

Biometrics in the Visible Spectrum,” IEEE International Homeland 

Security Technologies Conference, 2012
 Explain the benefits of this technology

Conclusions

Ocular 

Biometrics

Oculomotor Plant Brain

Iris

Benefits:

Enhanced counterfeit resistance

Improved accuracy when iris accuracy

is low

Detection of person’s states, e.g., 

fatigue, concussions, intoxication.

Same hardware as iris recognition

Beyond Iris

Eye Movements

O. V. Komogortsev et al Biometric Authentication via Complex Oculomotor 

Behavior, BTAS, 2013.
© CITeR

Database Sharing

• Need frameworks 

for data sharing 

where biometric 

data is not 

revealed

• Algorithms are 

uploaded to data; 

common protocol, 

common data, 

reproducible 

research

36

Fingerprint Verification Competition Ongoing, 

https://biolab.csr.unibo.it/fvcongoing

Anjos, et al, Reproducible Biometrics Evaluation and Testing 

with the BEAT Platform, IBPC 2013
© CITeR

Outline

• Basic Science

Modeling

Dilation

Individuality

Permanence

Disease

37

• Security

Liveness

Contact Lens

• Beyond Iris

Ocular

Vascular

Eye Movement

• Database Sharing

• Performance

Cross sensor

Quality

On the move

Distance

Mobile

Thank you!