Privacy, information technology, and health care
Communications of the ACM
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
Guide to Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Understanding user perspectives on biometric technology
Communications of the ACM - Enterprise information integration: and other tools for merging data
Pupil dilation degrades iris biometric performance
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring New Directions in Iris Recognition
SYNASC '09 Proceedings of the 2009 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Degradation of iris recognition performance due to non-cosmetic prescription contact lenses
Computer Vision and Image Understanding
Improved Iris Recognition through Fusion of Hamming Distance and Fragile Bit Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph matching iris image blocks with local binary pattern
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Theoretical and Practical Boundaries of Binary Secure Sketches
IEEE Transactions on Information Forensics and Security
How confident are you to counter uncertainty?
Proceedings of the First International Conference on Security of Internet of Things
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Biometric fuzziness occurs due to variations during data acquisitions and degrades the performance of the iris biometric system. Most of the proposed techniques overcome the fuzziness by removing some parts of the biometric image and/or using image processing methods which are usually computationally inefficient. However, we approach the problem from a post-processing perspective and we start our work with the raw binary iris codes. Our approach is based on the concept of commonality between two codes. The common substrings extract significant structural similarities and the misalignment distance accounts for rotational inconsistencies. This research explores how the length of common substrings can be used as a metric of similarity of iris biometric. We tested our metric using commercial Bath dataset and noticed that it achieves an EER of 1.3%, is robust at handling heterogonous biometric, and is more suitable for low FAR-based applications compared to the widely used Hamming Distance.