1 iris recognition ying sun aicip group meeting november 3, 2006
TRANSCRIPT
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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: