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Iris Recognition

Iris Recognition. Slides adapted from Natalia Schmid and John Daugman. Outline. Anatomy Iris Recognition System

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Iris Recognition

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  1. Iris Recognition Slides adapted from Natalia Schmid and John Daugman

  2. Outline • Anatomy • Iris Recognition System • Image Processing (John Daugman) - iris localization - encoding • Measure of Performance • Results • Pros and Cons • References

  3. 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.

  4. 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.

  5. Individuality of Iris Left and right eye irises have distinctive pattern.

  6. Iris Recognition System

  7. Iris Imaging • Distance up to 1 meter • Near-infrared camera

  8. Imaging Systems http://www.iridiantech.com/

  9. Imaging Systems http://www.iridiantech.com/

  10. Image Processing • John Daugman (1994) • Pupil detection: circular edge detector • Segmenting sclera

  11. Rubbersheet Model Each pixel (x,y) is mapped into polar pair (r, ). 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.

  12. Encoding 2-D Gabor filter in polar coordinates:

  13. IrisCode Formation Intensity is left out of consideration. Only sign (phase) is of importance. 256 bytes 2,048 bits

  14. Measure of Performance • Off-line and on-line modes of operation. Hamming distance: standard measure for comparison of binary strings. x and y are two IrisCodes is the notation for exclusive OR (XOR) Counts bits that disagree.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. Decision The same eye distributions depend strongly on the quality of imaging. Non-ideal conditions: - motion blur - focus - noise - pose variation - illumination

  20. Decision Ideal 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

  21. Error Probabilities Biometrics: Personal Identification in Networked Society, p. 115

  22. False Accept Rate For large database search: - FMR is used in verification - FAR is used in identification Adaptive threshold: to keep FAR fixed:

  23. Test Results 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). http://www.cl.cam.ac.uk/users/jgd1000/iristests.pdf

  24. Performance Comparison UK National Physical Laboratory test report, 2001. http://www.cl.cam.ac.uk/users/jgd1000/NPLsummary.gif

  25. 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.

  26. 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.

  27. Future of Iris http://www.abc.net.au/science/news/stories/s982770.htm

  28. National Geographic: 1984 and 2002

  29. 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.

  30. John Daugman and the Eyes of Sharbat Gula

  31. References • 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.

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