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Iris Recognition

Iris Recognition. BIOM 426: Biometrics Systems. Instructor: Natalia Schmid. Outline. Anatomy Iris Recognition System

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Iris Recognition

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  1. Iris Recognition BIOM 426: Biometrics Systems Instructor: Natalia Schmid March 22, 2004

  2. 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 March 22, 2004

  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. March 22, 2004

  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. March 22, 2004

  5. Individuality of Iris Left and right eye irises have distinctive pattern. March 22, 2004

  6. Iris Recognition System March 22, 2004

  7. Iris Imaging • Distance up to 1 meter • Near-infrared camera • Mirror March 22, 2004

  8. Imaging Systems http://www.iridiantech.com/ March 22, 2004

  9. Imaging Systems http://www.iridiantech.com/ March 22, 2004

  10. Image Processing • John Daugman (1994) • Pupil detection: circular edge detector • Segmenting sclera March 22, 2004

  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. March 22, 2004

  12. Encoding 2-D Gabor filter in polar coordinates: March 22, 2004

  13. IrisCode Formation Intensity is left out of consideration. Only sign (phase) is of importance. 256 bytes 2,048 bits March 22, 2004

  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. March 22, 2004

  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. Strong radial dependencies Some angular dependencies March 22, 2004

  16. Observations Read 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 March 22, 2004

  17. Measure of Performance 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. XOR: Example: 1 0 0 0 0 1 1 0 0 0 1 1 1 1 1 1 0 1 0 1 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 1 1 0 0 0 0 0 0 1 0 March 22, 2004

  18. Training Sets M users (2 iris images per user) ex.: M = 10,000 Genuine Set: (userm_iris1;userm_iris2), m = 1,…,M. Compute M genuine Hamming distances. Imposter Set: Formed from combination of irises from different users (userk_iris1,userl_iris1) (userk_iris1,userl_iris2) (userk_iris2,userl_iris1) (userk_iris2,userl_iris2) k is not equal to l, k,l = 1,…,M. Compute … imposter Hamming distances. March 22, 2004

  19. 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. March 22, 2004

  20. 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. March 22, 2004

  21. Decision The same eye distributions depend strongly on the quality of imaging. Non-ideal conditions: - motion blur - focus - noise - pose variation - illumination March 22, 2004

  22. 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 March 22, 2004

  23. Error Probabilities Biometrics: Personal Identification in Networked Society, p. 115 March 22, 2004

  24. False Accept Rate For large database search: - FMR is used in verification - FAR is used in identification Adaptive threshold: to keep FAR fixed: March 22, 2004

  25. 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 March 22, 2004

  26. Performance Comparison UK National Physical Laboratory test report, 2001. http://www.cl.cam.ac.uk/users/jgd1000/NPLsummary.gif March 22, 2004

  27. Performance Comparison Best-of-3 error rates UK National Physical Laboratory test report, 2001. March 22, 2004

  28. Other Systems R. Wildes et al. System: 1. Image Acquisition: - 256 pixels across diameter; - 20 cm distance; - diffuse source, circular polarization, and a low-light level camera; 2. Iris Localization: - image is transformed into a binary edge-map; - contour fitting using Hough transforms 3. Pattern Matching: - alignment of two patterns; - representation (a Laplacian pyramid); - goodness of match (estimate of correlation coefficient); - Fisher’s linear discriminant; “Iris Recognition: An Emerging Biometric Technology,” Proc. of the IEEE, 1997. March 22, 2004

  29. Fraud Protection 1. Hippus = steady-state small oscillations of pupil size at about 0.5 Hz. 2. Tracking eyelid movements. 3. Examining ocular reflections (4 optical surfaces - 4 reflections). 4. 2D Fourier spectra (printer’s dot in artificial irises). March 22, 2004

  30. References 1. 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. Y. Zhu, T. Tan, and Y. Wang, “Biometric Personal Identification Based on Iris Patterns,” ACTA AUTOMATICA SINICA , No.1, 2002. March 22, 2004

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