1. Emerging biometrics Presenter ： Shao-Chieh Lien Adviser ： Wei-Yang Lin

2. Contents Introduction Iris recognition Image Acquisition Iris localization 2-D Wavelet demodulation Recognition Comparison Reference

3. Introduction John Daugman’s algorithm The basis of almost all currently (as of 2006) commercially deployed iris-recognition systems

4. Introduction (cont.) Aged 12 in a refugee camp in Pakistan 18 years later to a remote part of Afghanistan

5. Iris recognition (infrared light)

6. Image Acquisition Iris radius: 80-130 pixels

7. Iris localization A smoothing function such as a Gaussian of scale σ Searching iteratively for the maximal contour integral Three parameter space of center coordinates and radius defining a path of contour integration

8. Iris localization (cont.) The path of contour integration in the equation is changed from circular to arcuate. It is used to localize both the upper and lower eyelid boundaries. Images with less than 50% of the iris visible between the fitted eyelid splines are deemed inadequate.

9. Regardless of Size, Position, and Orientation

10. Regardless of Size, Position, and Orientation (cont.) r: [0, 1] θ: [0, 2π] (x p (θ), y p (θ)): pupillary boundary points (x s (θ), y s (θ)): limbus boundary points

11. 2-D Wavelet demodulation A given area of the iris is projected onto complex-valued 2-D Gabor wavelets: α, β are the multiscale 2-D wavelet size parameters

12. 2-D Wavelet demodulation (cont.) ω is wavelet frequency (r 0, θ 0 ) represent the polar coordinates of each region of iris

13. 2-D Wavelet demodulation (cont.) 2048 such phase bits (256 bytes) are computed for each iris

14. 2-D Wavelet demodulation (cont.) Advantage: phase angles remain defined regardless of how poor the image contrast may be

15. Test of statistical independence HD: Hamming Distance ∥ maskA ∩ maskB ∥ : total number of phase bits that mattered in iris comparisons after artifacts such as eyelashes and specular reflections were discounted HD = 0: perfect match

16. Experiment result 4258 different iris images Bernoulli trial: successive “coin tosses.”

17. Binomial Distribution N = 249, p = 0.5, x = m/N, x is the Hamming Distance (HD)

18. Experiment result

19. Genetically Identical Eyes

20. Best match F 0 (x): the probability of getting a false match 1-F 0 (x): the probability of not making a false match (single test) [1-F 0 (x)] n : best of n

21. Best match (cont.) F n (x) = 1-[1-F 0 (x)] n f n (x): density function

22. Best match (cont.)

23. False match probability

24. Decision Environment Less favorable conditions: images acquired by different camera platforms

25. Decision Environment (cont.) Ideal conditions: almost artificial

26. “decidability” index d’ μ1, μ2: mean σ1, σ2: standard deviation

27. Probabilities Table Not stable “authentics” distributions depend strongly on the quality of imaging (e.g., motion blur, focus, noise, etc.) Different for different optical platforms

28. Comparison Fujitsu PalmSecure (palm vein recognition) IrisGuard H100 (iris recognition) Hitachi UB READER (finger vein recognition) [7] International Biometric Group, “Comparative Biometric Testing, Round 6 Public Report”, 2006.

29. Acquisition Devices Fujitsu PalmSecure IrisGuard H100 Hitachi UB READER

30. Test Environment

31. Comparison Processes ∼ 90,000 genuine comparisons and ∼ 116m impostor comparisons were executed across the three Test Systems. Accuracy was evaluated at the attempt and transaction levels. Attempt-level results are based on all available comparison scores Transactional results are based on the strongest comparison score of the six available in most recognition transactions.

32. Accuracy Terminology

33. Accuracy Results Fujitsu FMR, FNMR, T-FMR, and T-FNMR Hitachi, IrisGuard FMR, FNMR, T-FMR, and T-FNMR

34. DET Curves

36. Reference [1] http://en.wikipedia.org/wiki/Iris_recognitionhttp://en.wikipedia.org/wiki/Iris_recognition [2] http://www.cl.cam.ac.uk/~jgd1000/http://www.cl.cam.ac.uk/~jgd1000/ [3] http://www.biometricgroup.com/http://www.biometricgroup.com/ [4] J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30, Jan. 2004. [5] J. G. Daugman, "Probing the uniqueness and randomness of IrisCodes: Results from 200 billion iris pair comparisons." Proceedings of the IEEE, vol. 94, no. 11, pp 1927-1935, 2006. [6] J. G. Daugman, "Demodulation by complex-valued wavelets for stochastic pattern recognition." Int'l Journal of Wavelets, Multi-resolution and Information Processing, vol. 1, no. 1, pp 1-17, 2003. [7] International Biometric Group, “Comparative Biometric Testing, Round 6 Public Report”, 2006.