emerging biometrics
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Emerging biometricsEmerging biometricsPresenterPresenter :: Shao-Chieh LienShao-Chieh Lien
AdviserAdviser :: Wei-Yang LinWei-Yang Lin
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ContentsContents
• Introduction• Iris recognition
• Image Acquisition• Iris localization• 2-D Wavelet demodulation• Recognition
• Comparison• Reference
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IntroductionIntroduction
• John Daugman’s algorithm• The basis of almost all currently
(as of 2006) commercially deployed
iris-recognition systems
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Introduction (cont.)Introduction (cont.)
Aged 12in a refugee
camp in Pakistan
18 years laterto a remote partof Afghanistan
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Iris recognition (infrared light)Iris recognition (infrared light)
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Image AcquisitionImage Acquisition
• Iris radius: 80-130 pixels
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Iris localizationIris 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
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Iris localization (cont.)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.
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Regardless of Size, Position, and Orientation
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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
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2-D Wavelet demodulation2-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
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2-D Wavelet demodulation (cont.)2-D Wavelet demodulation (cont.)
• ω is wavelet frequency• (r0, θ0) represent the polar coordinates of each
region of iris
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2-D Wavelet demodulation (cont.)2-D Wavelet demodulation (cont.)
• 2048 such phase bits
(256 bytes) are
computed for each
iris
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2-D Wavelet demodulation (cont.)2-D Wavelet demodulation (cont.)
• Advantage:
phase angles remain
defined regardless
of how poor the image
contrast may be
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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
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EExperiment resultxperiment result
• 4258 different
iris images• Bernoulli trial:
successive
“coin tosses.”
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Binomial DistributionBinomial Distribution
• N = 249, p = 0.5, x = m/N, x is the Hamming Distance (HD)
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EExperiment resultxperiment result
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Genetically Identical Eyes
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Best matchBest 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
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Best match (cont.)Best match (cont.)
• Fn(x) = 1-[1-F0(x)]n
• fn(x): density function
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Best match (cont.)Best match (cont.)
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False match probabilityFalse match probability
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Decision EnvironmentDecision Environment
• Less favorable conditions: images acquired by different camera platforms
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Decision Environment (cont.)Decision Environment (cont.)
• Ideal conditions: almost artificial
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“decidability” index d’
• μ1, μ2: mean• σ1, σ2: standard deviation
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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
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ComparisonComparison
• 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.
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Acquisition Devices
Fujitsu PalmSecureIrisGuard H100
Hitachi UB READER
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Test Environment
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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.
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Accuracy Terminology
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Accuracy Results
Fujitsu FMR, FNMR, T-FMR, and T-FNMR
Hitachi, IrisGuard FMR, FNMR, T-FMR, and T-FNMR
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DET Curves
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DET Curves
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ReferenceReference
• [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.