NON-IDEAL IRIS RECOGNTION ALGORITHM BASED ON FREAK OPERATOR AND BIDIRECTIONAL CLOSEST HAMMING DISTANCE
Aimed at the significant degradation of non-ideal iris recognition performance under low-constraint conditions,and the low real-time performance of scale invariant feature transform(SIFT)and speeded up robust feature(SURF)methods,a recognition algorithm based on FREAK operator and bidirectional nearest Hamming distance is proposed.A Gaussian kernel was used to construct a multi-scale feature point detection operator to extract a robust set of feature points,and the FREAK operator was introduced to improve the SIFT operator to enhance the characterization and matching speed of feature points.The bidirectional closest Hamming distance matching strategy was used to enhance the stability of feature matching pairs so as to reduce the number of mismatched points of non-homologous irises.The experimental results show that the recognition error rate and the correct recognition rate are improved and the recognition time for a single verification is about 0.3s.The proposed method can effectively cope with the texture quality changes in non-ideal iris classes and has better real-time ability compared with SIFT and SURF algorithms.
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