Abstract
Minutiae point pattern matching is probably the most common approach to fingerprint verification. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains a challenging problem, both with respect to recovering the optimal alignment as well as to the construction of adequate matching function. In this paper, we develop an evolutionary approach for fingerprint matching by combining the use of the global search functionality of a genetic algorithm with a local improvement operator to search for the optimal global alignment between two minutiae sets. Further, we define a reliable matching function for fitness computation. The proposed approach was evaluated on two public domain collections of fingerprint images and compared with previous work. Experimental results show that our approach is reliable and practical for fingerprint verification, and outperforms the traditional genetic algorithm based method.KeywordsFingerprintsmatching/verificationalignmentminutiaegenetic algorithms
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