Online Signature Verification SystemBased on Support Vector Data Description
ZOU Jie1, WU Zhong-Cheng1,2
1.Automation Control Group, High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031 2.Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031
Abstract An online signature verification system is proposed based on support vector data description (SVDD). Firstly, correspondences of the critical points in signatures are confirmed by bidirectional backward-merging dynamic time wrapping algorithm. Then, subtle differences in the local are calculated by classical dynamic time wrapping algorithm. Feature selection principle based on mean and deviation minimization is proposed. Finally, the classifiers are designed using SVDD. To obtain better result, m-fold cross validation and genetic algorithm are used to seek optimal parameters of SVDD. The average equal error rate for skill forge signatures on SVC2004 signatures database is 4.25%.
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2011, Vol. 24 
