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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Ji, H. W., & Quan, Z. H. (2005). Signature verification using wavelet transform and support vector machine. In Lecture Notes in Computer Science (Vol. 3644, pp. 671–678). Springer Verlag. https://doi.org/10.1007/11538059_70
Mendeley helps you to discover research relevant for your work.