Using Adapted Levenshtein Distance for On-Line Signature Authentication
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Iris image segmentation and sub-optimal images
Image and Vision Computing
A non-linear normalization model for iris recognition
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
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Iris recognition from surveillance-type imagery is an active research topic in biometrics. However, iris identification in unconstrained conditions raises many proplems related to localization and alignment, and typically leads to degraded recognition rates. While development has mainly focused on more robust preprocessing, this work highlights the possibility to account for distortions at matching stage. We propose a constrained version of the Levenshtein Distance (LD) for matching of binary iris-codes as an alternative to the widely accepted Hamming Distance (HD) to account for iris texture distortions by e.g. segmentation errors or pupil dilation. Constrained LD will be shown to outperform HD-based matching on CASIA (third version) and ICE (2005 edition) datasets. By introducing LD alignment constraints, the matching problem can be solved in O(nċ s) time and O(n+s) space with n and s being the number of bits and shifts, respectively.