Signature verification using wavelet transform and support vector machine

Authors:
Hong-Wei Ji;Zhong-Hua Quan
Affiliations:
Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 6
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Abstract

In this paper, we propose a novel on-line handwritten signature verification method. Firstly, the pen-position parameters of the on-line signature are decomposed into multiscale signals by wavelet transform technique. For each signal at different scales, we can get a corresponding zero-crossing representation. Then the distances between the input signature and the reference signature of the corresponding zero-crossing representations are computed as the features. Finally, we build a binary Support Vector Machine (SVM) classifier to demonstrate the advantages of the multiscale zero-crossing representation approach over the previous methods. Based on a common benchmark database, the experimental results show that the average False Rejection Rate (FRR) and False Acceptance Rate (FAR) are 5.25% and 5%, respectively, which illustrates such new approach to be quite effective and reliable.