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- W2086411795 abstract "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." @default.
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- W2086411795 date "2011-12-05" @default.
- W2086411795 modified "2023-09-25" @default.
- W2086411795 title "On the commonality of iris biometrics" @default.
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- W2086411795 doi "https://doi.org/10.1145/2095697.2095704" @default.
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