Iris Recognition
Slides adapted from Natalia Schmid and John Daugman
Outline
• Anatomy • Iris Recognition System • Image Processing (John Daugman) - iris localization - encoding • Measure of Performance • Results • Pros and Cons • References
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.
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.
Individuality of Iris
Left and right eye irises have distinctive pattern.
Iris Recognition System
LocalizationAcquisition
IrisCode Gabor Filters Polar Representation
Image
Demarcated Zones
Iris Imaging • Distance up to 1 meter
• Near-infrared camera
Imaging Systems
http://www.iridiantech.com/
Imaging Systems
http://www.iridiantech.com/
Image Processing
John Daugman (1994)
• Pupil detection: circular edge detector
• Segmenting sclera
0000
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8/]10,5.1[
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ddIrr
r
rrrr
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
Encoding
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)()()(2exp),(
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rrirG
2-D Gabor filter in polar coordinates:
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IrisCode Formation
Intensity is left out of consideration. Only sign (phase) is of importance.
256 bytes2,048 bits
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.
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.
Observations
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
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.
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.
Decision
Non-ideal conditions:
The same eye distributions depend strongly on the quality of imaging.
- motion blur - focus - noise - pose variation - illumination
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
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
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:
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).
Performance Comparison
UK National Physical Laboratory test report, 2001.
http://www.cl.cam.ac.uk/users/jgd1000/NPLsummary.gif
Cons There are few legacy databases. Though iris may be a good
biometric for identification, large-scale deployment is impeded by lack of installed base.
Since the iris is small, sampling the iris pattern requires much user cooperation or complex, expensive input devices.
The performance of iris authentication may be impaired by glasses, sunglasses, and contact lenses; subjects may have to remove them.
The iris biometric, in general, is not left as evidence on the scene of crime; no trace left.
Pros
Iris is currently claimed and perhaps widely believed to be the most accurate biometric, especially when it comes to FA rates. Iris has very few False Accepts (the important security aspect).
It maintains stability of characteristic over a lifetime.
Iris has received little negative press and may therefore be more readily accepted. The fact that there is no criminal association helps.
The dominant commercial vendors claim that iris does not involve high training costs.
http://www.abc.net.au/science/news/stories/s982770.htm
Future of Iris
National Geographic: 1984 and 2002
Sharbat Gula The remarkable story of Sharbat
Gula, first photographed in 1984 aged 12 in a refugee camp in Pakistan by National Geographic (NG) photographer Steve McCurry, and traced 18 years later to a remote part of Afghanistan where she was again photographed by McCurry.
So the NG turned to the inventor of automatic iris recognition, John Daugman at the University of Cambridge.
The numbers Daugman got left no question in his mind that the eyes of the young Afghan refugee and the eyes of the adult Sharbat Gula belong to the same person.
John Daugman and the Eyes of Sharbat Gula
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.