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Iris Biometrics

University at Buffalo CSE666 Lecture Slides

Resources:• John Daugman’s webpage: http://www.cl.cam.ac.uk/~jgd1000/

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Eye anatomy

http://www.biometrics.dod.mil/bio101/7.aspx

J. Daugman “Iris Recognition”, American Scientist, v.89

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Eye anatomy

• The structure is formed during first two years of person’s life and does not change (except some rare eye illnesses)

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Examples of iris images

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Examples of iris images

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Retina Biometrics?

• Low intensity infrared light reveals the structure of blood vessels on the back of the retina• More intrusive than iris scan – the camera should be positioned very close to the eye, enrollee training and cooperation required• Limited to indoor use (low light – larger pupils – better image)• Earlier deployment (CIA, FBI, NASA)

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Algorithm steps

T. Tan et al.”Iris recognition: Fundamentals and State-Of-the Art”

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Iris localization

( )( ) ( )

∫∂∂

0000

,,,, 2

,*max

yxryxr

dsr

yxI

rrG

πσ

Daugman:search for values which give ( )00 ,, yxr

( ) 2

2

2

1 σσ σπ

r

erG−

=, where

Explanation:

( )∫

00 ,, 2

,

yxr

dsr

yxI

πaverage grey-scale intensity on the circumference of a circle of radius r and center ),( 00 yx

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•If center is properly located there will be two big changes in intensities – when pupil changes to iris and when iris changes to sclera•Convolution with searches for a peak at r with gaussian of size

Iris localization

( )∫∂

∂

00 ,, 2

,

yxr

dsr

yxI

r πChange in average grey-scale intensity on the circumference of a circle when r is increasing

),( 00 yx

)(rIr avg∂

∂

)(rIavg

( )rGσ σ

( )rGσ

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Example of localization

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Zones for calculating features

Inner boundary (with pupil)

Outer boundary (with sclera)

“Unwrapping” the iris

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Gabor Features

( ) 220

2200

00

/)(/)()(,,;, , βφθαρφθωωβαθ φρ −−−−−−= eeeG ri

r

Daugman: use Gabor filters

Oscillation in direction of φ ρ Spread in

direction of φSpread in direction of

to extract features

0:1?)0)),(),(((Re),,;,( ,,;,00Re 00>= ∫ ∫

ρ φωβαθ φρρφρφρωβαθ ddIGrh r

0:1?)0)),(),(((Im),,;,( ,,;,00Im 00>= ∫ ∫

ρ φωβαθ φρρφρφρωβαθ ddIGrh r

Total 256*8 bit features are extracted for each iris

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Examples of Gabor Filters

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Matching

maskBmaskA

maskBmaskAcodeBcodeABAHDraw

∩

∩∩⊗=),(

Hamming Distance between two binary iris codes A and B:

maskBmaskA, - specifies which bits are valid

It turns out additional normalization is required to compensate for the number of actually compared bits:

911),(

2

1

2

1),(

maskBmaskABAHDBAHD rawnorm

∩

−−=

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Performance

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Performance

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NIST evaluation (NISTIR 7408)

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Examples of NIST evaluation images

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Examples of NIST evaluation images

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• Bits in iris code seem to be independently randomly generated • Model the distribution between two irises as a Hamming Distance between two randomly generated binary strings of length n• n=249 seems to give a theoretical distribution of HD close to real-life impostor distribution• 256*8 total –249 random = “not needed”?

- spatially close bits might be dependent

Individuality of Irises

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Deployment

• Largest deployment – more than 6,500 passengers entering UAE each day are screened against a database of 420,000 persons previously expelled from country• Time of each search – 1 second

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Deployment

http://www.securimetrics.com/solutions/

Operation Iraqi Freedom

US Correctional Facilities

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Deployment

Employee access control at airports

Condominium entrance

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Deployment (near future)

Advertising

Law enforcement

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Afghan girl

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Afghan girl