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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Signature Verification Using Wavelet Transform and Support Vector Machine
verfasst von : Hong-Wei Ji, Zhong-Hua Quan
Erschienen in: Advances in Intelligent Computing
Verlag: Springer Berlin Heidelberg
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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.