Signature verification based on fusion of on-line and off-line kernels

The problem of signature verification is considered within the bounds of the kernel-based methodology of pattern recognition, more specifically, SVM principle of machine learning. A kernel in the set of signatures can be defined in different ways and it is impossible to choose the most appropriate kernel a priori. We propose a principle of fusing several on-line and off-line kernels into an entire training and verification technique. Experiments with signature database SVC2004 have shown that the multi-kernel approach essentially decreases the error rate in comparison with verification based on single kernels.