Matching Similarity Scores for a Minutiae-based Palmprint Recognition

Recently, palmprint recognition in forensic domain has gained considerable attention since 30 % of evidence left in crime scene originate from palms. Like most of recognition systems, palmprint one is composed of three steps: preprocessing, features extraction and representation and finally features matching. Minutiae are the most reliable and discriminating features used in these systems. Minutiae matching is then very critical. Quantifying the similarity between two sets of extracted minutiae and assigning a score is particularly important in this step. In this paper, we designed similarity scores for a minutiae based recognition system using a minimum of extracted information. Our proposed scores are based on the score of [1], used in point pattern matching. They are tested and compared on the database used in [2]. The best one is tested and compared to the one presented in the same work [2]. Obtained results are very interesting.