Dynamically subsumed-OVA SVMs for fingerprint classification

Authors:
Jin-Hyuk Hong;Sung-Bae Cho
Affiliations:
Dept. of Computer Science, Yonsei University, Seoul, Korea;Dept. of Computer Science, Yonsei University, Seoul, Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 5
Cited 0

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Effective fingerprint classification by localized models of support vector machines

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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 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.