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Outline
• Anatomy • Iris Recognition System • Image Processing (John Daugman) - iris localization - encoding • Measure of Performance • Results • Other Algorithms • Pros and Cons • Ongoing Work at WVU • References
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Anatomy of the Human Eye
• Eye = Camera
• Cornea bends, refracts, and focuses light.
• Retina = Film for image projection (converts image into electrical signals).
• Optical nerve transmits signals to the brain.
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Structure of Iris
• Iris = Aperture
• Different types of muscles: - the sphincter muscle (constriction) - radial muscles (dilation)
• Iris is flat
• Color: pigment cells called melanin
• The color texture, and patterns are unique.
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Iris Recognition System
LocalizationAcquisition
IrisCode Gabor Filters Polar Representation
Image
Demarcated Zones
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Image Processing
John Daugman (1994)
• Pupil detection: circular edge detector
• Segmenting sclera
0000
,,,, 2
),()(max
yxryxr
dsr
yxI
rrG
8/
8/]10,5.1[
),(2
max00
ddIrr
r
rrrr
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Rubbersheet Model
rr
0 1
θ
θEach pixel (x,y) is mapped into polar pair (r, ).
θ
Circular band is divided into 8 subbands of equal thickness for a given angle .
Subbands are sampled uniformly in and in r.
Sampling = averaging over a patch of pixels.
θ
θ
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Encoding
2
20
2
20
0
)()()(2exp),(
ba
rrirG
2-D Gabor filter in polar coordinates:
1
0
0
9.0
1
0
0
r
b
a
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IrisCode Formation
Intensity is left out of consideration. Only sign (phase) is of importance.
256 bytes2,048 bits
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Measure of Performance
• Off-line and on-line modes of operation.
Hamming distance: standard measure for comparison of binary strings.
k
n
kk yx
nD
1
1
x and y are two IrisCodes
is the notation for exclusive OR (XOR)
Counts bits that disagree.
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Observations
• Two IrisCodes from the same eye form genuine pair => genuine Hamming distance. • Two IrisCodes from two different eyes form imposter pair => imposter Hamming distance. • Bits in IrisCodes are correlated (both for genuine pair and for imposter pair). • The correlation between IrisCodes from the same eye is stronger.
Strong radial dependancies Some angular dependencies
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ObservationsRead J. Daugman’s statement with caution. Interpret correctly.
The fact that this distribution is uniform indicates that different irises do not systematically share any common structure.
For example, if most irises had a furrow or crypt in the 12-o'clock position, then the plot shown here would not be flat.
URL: http://www.cl.cam.ac.uk/users/jgd1000/independence.html
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Degrees of Freedom
Imposter matching score:
- normalized histogram
- approximation curve
- Binomial with 249 degrees of freedom
Interpretation: Given a large number of imposter pairs. The average number of distinctive bits is equal to 249.
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Histograms of Matching Scores
Decidability Index d-prime:
d-prime = 11.36
The cross-over point is 0.342
Compute FMR and FRR for every threshold value.
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Decision
Non-ideal conditions:
The same eye distributions depend strongly on the quality of imaging.
- motion blur - focus - noise - pose variation - illumination
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DecisionIdeal conditions:
Imaging quality determines how much the same iris distribution evolves and migrates leftwards.
d-prime for ideal imaging:
d-prime = 14.1
d-prime for non-ideal imaging (previous slide):
d-prime = 7.3
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Error Probabilities
HD Criterion Odds of False Accept Odds of False Reject
0.28 1 in 1210 1 in 11,400 0.29 1 in 1110 1 in 22,700 0.30 1 in 6.2 billion 1 in 46,000 0.31 1 in 665 million 1 in 95,000 0.32 1 in 81 million 1 in 201,000 0.33 1 in 11.1 million 1 in 433,000 0.34 1 in 1.7 million 1 in 950,000
0.342 Cross-over 1 in 1.2 million 1 in 1.2 million 0.35 1 in 295,000 1 in 2.12 million 0.36 1 in 57,000 1 in 4.83 million 0.37 1 in 12,300 1 in 11.3 million
Biometrics: Personal Identification in Networked Society, p. 115
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False Accept Rate
FMRNFMRFAR N )1(1
For large database search: - FMR is used in verification - FAR is used in identification
)(log01.032.0 10 NHDcrit
Adaptive threshold: to keep FAR fixed:
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Test Results
http://www.cl.cam.ac.uk/users/jgd1000/iristests.pdf
The results of tests published in the period from 1996 to 2003.
Be cautious about reading these numbers:
The middle column shows the number of imposter pairs tested (not the number of individuals per dataset).
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Performance Comparison
UK National Physical Laboratory test report, 2001.
http://www.cl.cam.ac.uk/users/jgd1000/NPLsummary.gif
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Best-of-3 error rates
UK National Physical Laboratory test report, 2001.
Performance Comparison
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References1. J. Daugman’s web site. URL: http://www.cl.cam.ac.uk/users/jgd1000/2. J. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148 – 1161, 1993. 3. J. Daugman, United States Patent No. 5,291,560 (issued on March 1994). Biometric Personal Identification System Based on Iris Analysis, Washington DC: U.S. Government Printing Office, 1994. 4. J. Daugman, “The Importance of Being Random: Statistical Principles of Iris Recognition,” Pattern Recognition, vol. 36, no. 2, pp 279-291. 5. R. P. Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proc. of the IEEE, vol. 85, no. 9, 1997, pp. 1348-1363. 6.
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