Abstract An novel 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 backwardmerging 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 support vector data description (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 in SVC2004 signatures database is 4.25%.
