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|Title:||A hybrid HMM/ANN based approach for online signature verification|
|Subjects:||Hidden Markov Model|
Artificial neural networks
Online signature verification
|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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|Appears in Collections:||CEE Conference Papers & Presentations|
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