Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/522
Title: A hybrid HMM/ANN based approach for online signature verification
Authors: Quan, Zhong-Hua
Huang, De-Shuang
Liu, Kun-Hong
Chau, Kwok-wing
Subjects: Hidden Markov Model
Artificial neural networks
Online signature verification
Viterbi algorithm
Issue Date: 2007
Publisher: IEEE
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.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/522
ISBN: 9781424413805
142441380X
ISSN: 1098-7576
Appears in Collections:CEE Conference Papers & Presentations

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