Abstract
Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field deployments, producing no false matches in millions of iris comparisons. The recognition principle is the failure of a test of statistical independence on iris phase structure, as encoded by multi-scale quadrature 2D Gabor 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 bits/mm2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. These high confidence levels are important because they allow very large databases on even a national scale 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.
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Daugman, J. (2004). Recognising Persons by Their Iris Patterns. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_4
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DOI: https://doi.org/10.1007/978-3-540-30548-4_4
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