Transcript Document

Iris Recognition
Ying Sun
AICIP Group Meeting
November 3, 2006
1
Outline
• Introduction of Biometrics
• Methods for Iris Recognition
• Conclusion and Outlook
2
Biometrics Overview
• Measures human body features
Universal, unique, permanent & quantitatively measurable
• Physiological characteristics
Fingerprints
Face
DNA
Hand Geometry/Ear Shape
Iris/Retina
• Behavioral characteristics
Signature/gait
keystrokes / typing
Voiceprint
• Example applications
Banking, airport access, info security, etc.
3
Advantages of Iris Recognition
• Uniqueness
Highly rich texture
Twins have different iris texture
Right eye differs from left eye
• Stability
Do not change with ages
Do not suffer from scratches, abrasions, distortions
• Noninvasiveness
Contactless technique
• High recognition performance
4
Comparison of biometric techniques
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Verification and Identification
• Verification:
One to one matching
Is this person really who they claim to be?
• Identification:
One to many matching
Who is this person?
Identification is more difficult!
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Identification
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.
10,000
0.9999
= 0.37
Misidentifying:
1.0 – 0.37 = 0.63
63% chance of being wrong!
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Misidentification increases with the
size of database
Database of 1,000
Chance of error:
1,000
1.0 - 0.9999
= 0.09
Database of 10,000
Chance of error:
10,000
1.0 - 0.9999
= 0.63
Database of 100,000
Chance of error:
100,000
1.0 - 0.9999
= 0.99995
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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
Hamming distance (HD)
Calculate the percentage of mismatched bits
between a pair of iris codes. (0-100%)
No. of bits different
HD 
Total no. of bits
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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 false
matches
Hamming
distances of
true matches
If an iris code differs from a stored pattern by
30% or less it is accepted as an identification 20
Probability of the encoding
difference between several
measurements of the same
person
P
0
T
False acceptance
Probability of the
encoding difference
between different
people.
False rejection
Encoding difference
21
Threshold used to decide acceptance/rejection
Afghan Girl Identified by Iris Patterns
1984
2002
Left eye: HD=0.24; Right eye: HD=0.31
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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 identification
Iris on the move
• Surveillance
WSN+Iris Recognition
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Gabor Wavelet
a complex sinusoidal carrier and a Gaussian envelope
The complex carrier takes the form
The real and imaginary part:
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