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Title: A hybrid HMM/ANN based approach for online signature verification
Authors: Quan, ZH
Huang, DS
Liu, KH
Chau, KW 
Issue Date: 2007
Source: IJCNN 2007 : proceedings of the International Joint Conference on Neural Networks, Orlando, Florida, USA, Aug 12-17, 2007, p. 402-405
Abstract: This paper presents a new approach based on HMM/ANN hybrid for online signature verification. A group of ANNs are used as local probability estimators for an HMM. The Viterbi algorithm is employed to work out the global posterior probability of a model. The proposed HMM/ANN hybrid has a strong discriminant ability, i.e, from a local sense, the ANN can be regarded as an efficient classifier, and from a global sense, the posterior probability is consistent with that of a Bayes classifier. Finally, the experimental results show that this approach is promising and competing.
Keywords: Hidden Markov Model
Artificial neural networks
Online signature verification
Viterbi algorithm
Publisher: IEEE
ISBN: 9781424413805
ISSN: 1098-7576
Rights: © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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