A novel method to fingerprint classification, in which the naive 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 architecture 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 fingerprint images by combining different fingerprint features, but produces a classification accuracy of 90.8% for 5-class classification on the NIST 4 database, that is higher than conventional methods.
['Jin-Hyuk Hong', 'Sung-Bae Cho']
Dynamically subsumed-OVA SVMs for fingerprint classification