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Iris Recognition

Ying SunYing Sun

AICIP Group MeetingAICIP Group Meeting

November 3, 2006November 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 characteristicsFingerprintsFaceDNAHand 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 textureTwins have different iris textureRight eye differs from left eye

• Stability Do 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 identify which one is correct.

Suppose being right on an individual test: 0.9999

To make a correct identification, have to be right on every 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

Database of 10,000Chance of error:

1.0 - 0.999910,000

= 0.63

Database of 100,000Chance of error:

1.0 - 0.9999100,000

= 0.99995

Misidentification increases with the size of database

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Need Higher Identification Confidence!

Iris Recognition Would Satisfy this Criteria.

Need Higher Identification Confidence!

Iris Recognition Would Satisfy this 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 boundary

and the outer (limbus) boundary of the iris. (limbus--the border between the sclera and the iris), both the upper and lower eyelid boundaries

• Desired characteristics of iris localization:

• Sensitive to a wide range of edge contrast

• Robust to irregular borders• Capable of dealing with

variable 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 the maximum in the partial derivative according to increasing radius r, of the normalized contour integral of I(x,y) along a 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 different scales 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 Discrete Fourier 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 is sensitive to illumination intensity

• The phase is converted to 2 bits depending on which 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

bits of no. Total

different bits of No.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 come from different irises the different bits will be random

• The number of different bits will obey a binomial distribution with mean 0.5

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Distributions of true matches versus non matches

Hamming distances of true matches

Hamming distances of false matches

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 encoding difference between several measurements of the same person Probability of the

encoding difference between different people.

P

0 TFalse rejectionFalse acceptance

Threshold used to decide acceptance/rejection

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 chances of the two codes coming from different irises is 1 in 2.9 million

• So far it has been tried out on 2.3 million test without a single error

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Future Work

• Anti-spoofing Liveness detection

• Long distance identificationIris 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: