Please use this identifier to cite or link to this item:
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.
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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
ISBN: 9781424413805
ISSN: 1098-7576
Appears in Collections:CEE Conference Papers & Presentations

Files in This Item:
File Description SizeFormat 
IJCNN2007.pdf942.95 kBAdobe PDFView/Open

All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated. No item in the PolyU IR may be reproduced for commercial or resale purposes.