Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordinal Measures for Iris Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and fast assessment of iris image quality
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Circuits and Systems for Video Technology
Coarse Iris classification by learned visual dictionary
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Iris recognition based on elastic graph matching and Gabor wavelets
Computer Vision and Image Understanding
Noisy iris image matching by using multiple cues
Pattern Recognition Letters
A fusion approach to unconstrained iris recognition
Pattern Recognition Letters
Iris localization in frontal eye images for less constrained iris recognition systems
Digital Signal Processing
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
Efficient iris segmentation method in unconstrained environments
Pattern Recognition
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This paper describes the winning algorithm we submitted to the recent NICE.I iris recognition contest. Efficient and robust segmentation of noisy iris images is one of the bottlenecks for non-cooperative iris recognition. To address this problem, a novel iris segmentation algorithm is proposed in this paper. After reflection removal, a clustering based coarse iris localization scheme is first performed to extract a rough position of the iris, as well as to identify non-iris regions such as eyelashes and eyebrows. A novel integrodifferential constellation is then constructed for the localization of pupillary and limbic boundaries, which not only accelerates the traditional integrodifferential operator but also enhances its global convergence. After that, a curvature model and a prediction model are learned to deal with eyelids and eyelashes, respectively. Extensive experiments on the challenging UBIRIS iris image databases demonstrate that encouraging accuracy is achieved by the proposed algorithm which is ranked the best performing algorithm in the recent open contest on iris recognition (the Noisy Iris Challenge Evaluation, NICE.I).