Emerging biometrics

Emerging biometricsEmerging biometricsPresenterPresenter ：： Shao-Chieh LienShao-Chieh Lien

AdviserAdviser ：： Wei-Yang LinWei-Yang Lin

ContentsContents

• Introduction• Iris recognition

• Image Acquisition• Iris localization• 2-D Wavelet demodulation• Recognition

• Comparison• Reference

IntroductionIntroduction

• John Daugman’s algorithm• The basis of almost all currently

(as of 2006) commercially deployed

iris-recognition systems

Introduction (cont.)Introduction (cont.)

Aged 12in a refugee

camp in Pakistan

18 years laterto a remote partof Afghanistan

Iris recognition (infrared light)Iris recognition (infrared light)

Image AcquisitionImage Acquisition

• Iris radius: 80-130 pixels

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

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.

Regardless of Size, Position, and Orientation

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

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

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

2-D Wavelet demodulation (cont.)2-D Wavelet demodulation (cont.)

• 2048 such phase bits

(256 bytes) are

computed for each

iris

2-D Wavelet demodulation (cont.)2-D Wavelet demodulation (cont.)

• Advantage:

phase angles remain

defined regardless

of how poor the image

contrast may be

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

EExperiment resultxperiment result

• 4258 different

iris images• Bernoulli trial:

successive

“coin tosses.”

Binomial DistributionBinomial Distribution

• N = 249, p = 0.5, x = m/N, x is the Hamming Distance (HD)

EExperiment resultxperiment result

Genetically Identical Eyes

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

Best match (cont.)Best match (cont.)

• Fn(x) = 1-[1-F0(x)]n

• fn(x): density function

Best match (cont.)Best match (cont.)

False match probabilityFalse match probability

Decision EnvironmentDecision Environment

• Less favorable conditions: images acquired by different camera platforms

Decision Environment (cont.)Decision Environment (cont.)

• Ideal conditions: almost artificial

“decidability” index d’

• μ1, μ2: mean• σ1, σ2: standard deviation

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

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.

Acquisition Devices

Fujitsu PalmSecureIrisGuard H100

Hitachi UB READER

Test Environment

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.

Accuracy Terminology

Accuracy Results

Fujitsu FMR, FNMR, T-FMR, and T-FNMR

Hitachi, IrisGuard FMR, FNMR, T-FMR, and T-FNMR

DET Curves

DET Curves

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.