Dynamically Subsumed-OVA SVMs for Fingerprint Classification

Abstract. A novel method to fingerprint classification, in which the naïve Bayes classifier (NB) and OVA SVMs are integrated, is presented. In order to solve the tie problem of combing OVA SVMs, we propose a subsumption ar-chitecture dynamically organized by the probability of classes. NB calculates the probability using singularities and pseudo codes, while OVA SVMs are trained on FingerCode. The proposed method not only tolerates ambiguous fin-gerprint images by combining different fingerprint features, but produces a classification accuracy of 90.8 % for 5-class classification on the NIST 4 data-base, that is higher than conventional methods.