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Emerging biometrics

Emerging biometrics. Presenter ： Shao-Chieh Lien Adviser ： Wei-Yang Lin. Contents. Introduction Iris recognition Image Acquisition Iris localization 2-D Wavelet demodulation Recognition Comparison Reference. Introduction. John Daugman’s algorithm The basis of almost all currently

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Emerging biometrics

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  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] • (xp(θ), yp(θ)): pupillary boundary points • (xs(θ), ys(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 • (r0, θ0) 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 • F0(x): the probability of getting a false match • 1-F0(x): the probability of not making a false match (single test) • [1-F0(x)]n: best of n

  21. Best match (cont.) • Fn(x) = 1-[1-F0(x)]n • fn(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

  35. DET Curves

  36. Reference • [1] http://en.wikipedia.org/wiki/Iris_recognition • [2] http://www.cl.cam.ac.uk/~jgd1000/ • [3] 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.

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