Elements of information theory
Elements of information theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A conceptual framework for testing biometric algorithms within operating systems' authentication
Proceedings of the 2002 ACM symposium on Applied computing
On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
System accuracy estimation of SRAM-based device authentication
Proceedings of the 16th Asia and South Pacific Design Automation Conference
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
How confident are you to counter uncertainty?
Proceedings of the First International Conference on Security of Internet of Things
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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.