Abstract
Algorithms first described in 1993 for recognizing persons by their iris patterns have now been tested in several public field trials, producing no false matches in several million comparison tests. The underlying recognition principle is the failure of a test of statistical independence on texture phase structure as encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 244 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. This paper reviews the current algorithms and presents the results of 2.3 million comparisons among eye images acquired in trials in Britain, the USA, and Japan, and it discusses aspects of the process still in need of improvement.
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Daugman, J. Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns. International Journal of Computer Vision 45, 25–38 (2001). https://doi.org/10.1023/A:1012365806338
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DOI: https://doi.org/10.1023/A:1012365806338