1. March 10, 20041 Iris Recognition Instructor: Natalia Schmid BIOM 426: Biometrics Systems

2. March 10, 20042 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

3. March 10, 20043 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. March 10, 20044 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. March 10, 20045 Individuality of Iris Left and right eye irises have distinctive pattern.

6. March 10, 20046 Iris Recognition System

7. March 10, 20047 Iris Imaging Distance up to 1 meter Near-infrared camera Mirrow

8. March 10, 20048 Imaging Systems http://www.iridiantech.com/

9. March 10, 20049 Imaging Systems http://www.iridiantech.com/

10. March 10, 200410 Image Processing John Daugman (1994) Pupil detection: circular edge detector Segmenting sclera

11. March 10, 200411 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. March 10, 200412 Encoding 2-D Gabor filter in polar coordinates:

13. March 10, 200413 IrisCode Formation Intensity is left out of consideration. Only sign (phase) is of importance. 256 bytes 2,048 bits

14. March 10, 200414 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. March 10, 200415 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

16. March 10, 200416 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

17. March 10, 200417 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. March 10, 200418 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. March 10, 200419 Decision Non-ideal conditions: The same eye distributions depend strongly on the quality of imaging. - motion blur - focus - noise - pose variation - illumination

20. March 10, 200420 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. March 10, 200421 Error Probabilities Biometrics: Personal Identification in Networked Society, p. 115

22. March 10, 200422 False Accept Rate For large database search: - FMR is used in verification - FAR is used in identification Adaptive threshold: to keep FAR fixed:

23. March 10, 200423 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).

24. March 10, 200424 Performance Comparison UK National Physical Laboratory test report, 2001. http://www.cl.cam.ac.uk/users/jgd1000/NPLsummary.gif

25. March 10, 200425 Best-of-3 error rates UK National Physical Laboratory test report, 2001. Performance Comparison

26. March 10, 200426 http://www.abc.net.au/science/news/stories/s982770.htm Future of Iris

27. March 10, 200427 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.