Off-line signature verification based on multitask learning

Authors:
You Ji;Shiliang Sun;Jian Jin
Affiliations:
Department of Computer Science and Technology, East China Normal University, Shanghai, P.R. China;Department of Computer Science and Technology, East China Normal University, Shanghai, P.R. China;Department of Computer Science and Technology, East China Normal University, Shanghai, P.R. China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Year:
2011

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Abstract

Off-line signature verification is very important to biometric authentication. This paper presents an effective strategy to perform offline signature verification based on multitask support vector machines. This strategy can get a significant resolution of classification between skilled forgeries and genuine signatures. Firstly modified direction feature is extracted from signature's boundary. Secondly we use Principal Component Analysis to reduce dimensions. We add some helpful assistant tasks which are chosen from other tasks to each people's task. Then we use multitask support vector machines to build a useful model. The proposed model is evaluated on GPDS and MCYT data sets. Our experiments demonstrated the effectiveness of the proposed strategy.