F06 Ying Irisrecognition

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Iris Recognition

Ying SunAICIP Group Meeting

November 3, 2006


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Outline

Introduction of Biometrics

Methods for Iris Recognition

Conclusion and Outlook


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Biometrics Overview

Measures human body featuresUniversal, unique, permanent & quantitatively measurable

Physiological characteristicsFingerprints

FaceDNAHand Geometry/Ear ShapeIris/Retina

Behavioral characteristicsSignature/gaitkeystrokes / typingVoiceprint

Example applicationsBanking, airport access, info security, etc.


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Advantages of Iris Recognition

UniquenessHighly rich texture

Twins have different iris texture

Right eye differs from left eye

StabilityDo not change with ages

Do not suffer from scratches, abrasions, distortions

NoninvasivenessContactless technique

High recognition performance


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Comparison of biometric techniques


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Verification:One to one matchingIs this person really who they claim to be?

Identification:One to many matchingWho is this person?

Identification is more difficult!

Verification and Identification


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10,000 samples, to identifywhich one is correct.

Suppose being right on an individual test: 0.9999

To make a correct identification, have to be right onevery one of the 10,000 tests.

0.999910,000

= 0.37

Misidentifying:

1.0 0.37 = 0.63

63% chance of being wrong!

Identification


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Database of 1,000Chance of error:

1.0 - 0.99991,000

= 0.09


1.0 - 0.999910,000

= 0.63


1.0 - 0.9999100,000

= 0.99995

Misidentification increases with thesize of database


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Need Higher Identification

Confidence!

Iris Recognition Would Satisfythis Criteria.


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Iris Structure


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Procedure Employed in Iris Recognition

Iris localization (Segmentation)

Feature extraction

Pattern matching

Focusing on Daugman Method


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Iris Localization

Localize the boundary of an iris from the image

In particular, localize both the pupillary boundaryand the outer (limbus) boundary of the iris.(limbus--the border between the sclera and theiris), both the upper and lower eyelid boundaries

Desired characteristics of iris localization:

Sensitive to a wide range ofedge contrast

Robust to irregular borders

Capable of dealing withvariable occlusions


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Iris Localization

Image Segmentation

I(x,y): Raw image

: Radial Gaussian

*: Convolution

The operator searches over the image domain for themaximum in the partial derivative according to increasingradius r, of the normalized contour integral of I(x,y) alonga circular arc ds and center coordinates.

(active contour fitting method)


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Feature Extraction

Image Contains Both Amplitude and Phase

Phase is unaffected by brightness or contrast changes

Phase Demodulation via 2D Gabor wavelets

Angle of each phasor quantized to one/four

quadrants


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Gabor Wavelets

Gabor Wavelets filter out structures at differentscales and orientations

For each scale and orientation there is a pair of

odd and even wavelets A scalar product is carried out between the

wavelet and the image (just as in the DiscreteFourier Transform)

The result is a complex number


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Phase Demodulation

The complex number is converted to 2 bits

The modulus is thrown away because it issensitive to illumination intensity

The phase is converted to 2 bits depending onwhich quadrant it is in


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The iris code is a pattern of 1s and 0s (bits).

These bits are compared against a stored bit pattern.

Represent iris texture as a binary vector of 2048 bits


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Pattern Matching

bitsofno.Total

differentbitsofNo.

HD

Hamming distance (HD)

Calculate the percentage of mismatched bits

between a pair of iris codes. (0-100%)


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Binomial Distribution

If two codes comefrom different irisesthe different bits

will be random The number of

different bits willobey a binomial

distribution withmean 0.5


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Distributions of true matches versusnon matches

Hammingdistances oftrue matches

Hammingdistancesof falsematches

If an iris code differs from a stored pattern by

30% or less it is accepted as an identification


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Encoding difference

Probability of the encodingdifference between severalmeasurements of the same

person Probability of theencoding differencebetween differentpeople.

P

0T

False rejectionFalse acceptance

Threshold used to decide acceptance/rejection


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22Left eye: HD=0.24; Right eye: HD=0.31

Afghan Girl Identified by Iris Patterns

1984

2002


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Summary for Identification

Two codes come from different iris, HD~0.45

HD smaller for the same iris

If the Hamming distance is < 0.33 the chancesof the two codes coming from different irises is1 in 2.9 million

So far it has been tried out on 2.3 million testwithout a single error


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Future Work

Anti-spoofing

Liveness detection

Long distance identification

Iris on the move

SurveillanceWSN+Iris Recognition


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Gabor Wavelet

The complex carrier takes the form

a complex sinusoidal carrier and a Gaussian envelope

The real and imaginary part:

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F06 Ying Irisrecognition