A novel similarity measurement for iris authentication

This paper introduces a novel similarity measurement which derives the likelihood ratio between two eyes. The proposed method takes into consideration the individual and system error rates of eye features. It handles two kinds of individual probabilities: (consistent Probability (CP), the Inconsistent Probability (IP),) to achieve the best matching approach between two feature sets. While calculating the probabilities, we assume that a reasonable alignment approach should be obtained before the matching approach introduced. The proposed matching algorithm is theoretically proved to be optimal, and experimental results show that the proposed method has more efficient performance on separating genuine and impostor pairs.