High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
A Theory of Shape Identification
A Theory of Shape Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iris recognition using signal-level fusion of frames from video
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Flexible image segmentation and quality assessment for real-time iris recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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The pattern of the human iris contains rich information which provides one of the most accurate methods for recognition of individuals. Identification through iris recognition is achieved by matching a biometric template generated from the texture of the iris against an existing database of templates. This relies on the assumption that the probability of two different iris generating similar templates is very low. This assumption opens a question: how can one be sure that two iris templates are similar because they were generated from the same iris and not because of some other random factor?. In this paper we introduce a novel technique for iris matching based on the a contrario framework, where two iris templates are decided to belong to the same iris according to the unlikelyness of the similarity between them. This method provides an intuitive detection thresholding technique, based on the probability of occurence of the distance between two templates. We perform tests on different iris databases captured in heterogeneous environments and we show that the proposed identification method is more robust than the standard method based on the Hamming distance.