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How Iris Recognition Works (2004)

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by John Daugman
Citations:172 - 1 self
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BibTeX

@MISC{Daugman04howiris,
    author = {John Daugman},
    title = {How Iris Recognition Works},
    year = {2004}
}

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Abstract

Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b mm 2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. This paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.

Citations

6514 Elements of Information Theory - Cover, Thomas - 1991
1080 Eigenfaces vs. fisherfaces: Recognition using class specific linear projection - Belhumeur, Hespanha, et al. - 1997
535 The FERET Evaluation Methodology for Face-Recognition Algorithms - Phillips, Moon, et al. - 2000
453 Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by twodimensional visual cortical filters - Daugman - 1985
312 Complete discrete 2D gabor transforms by neural networks for image analysis and compression - DAUGMAN - 1988
270 confidence visual recognition of persons by a Test of statistical Independence - Daugman - 1993
211 Face recognition: The problem of compensating for changes in illumination direction - Adnin, Moses, et al. - 1997
59 An Introduction to Evaluating Biometric Systems - Phillips, Martin, et al. - 2000
51 Face Recognition for Smart Environments - Pentland, Choudhury - 2000
43 Richness of Visual Phase Information: Update on recognizing persons by Iris patterns - Daugman
18 Epigenetic randomness, complexity and singularity of human iris patterns - Daugman, Downing
15 Demodulation, predictive coding, and spatial vision - Daugman, Downing - 1995
9 Gross anatomy and embryology of the eye,” The - Kronfeld - 1962
9 Binomial and negative binomial analogues under correlated Bernoulli trials - Viveros, Balasubramanian, et al. - 1984
6 Iridology: A critical review - Berggren - 1985
5 An evaluation of iridology - Simon, Worthen, et al. - 1979
4 Photophysics and photochemistry of melanin - Chedekel - 1995
3 U.S. Patent No. 5,291,560: Biometric Personal Identification System Based on Iris Analysis - Daugman - 1994
2 discrete 2-d gabor transform by neural networks for image analysis and compression - “Complete - 1988
2 richness of visual phase information: Update on recognizing persons by their iris patterns - “Statistical - 2001
1 randomness, complexity, and singularity of human iris patterns - “Epigenetic - 2001
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