f06 ying irisrecognition
TRANSCRIPT
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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
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 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: