Off-Line Signature Recognition and Verification by Kernel Principal Component Self-Regression - Zhang, B.-L. (ICMLA 2006 : 5th International Conference on Machine Learning and Applications : proceedings 14-16 December 2006, Orlando, FL, 2006)
Automatic signature verification is an active area of research with numerous applications such as bank check verification, ATM access, etc. In this research, a kernel principal component self-regression (KPCSR) model is proposed for offline signature verification and recognition problems. Developed from the kernel principal component regression (KPCR), the self-regression model selects a subset of the principal components from the kernel space for the input variables to accurately characterize each user's signature, thus offering good verification and recognition performance. The model directly works on bitmap images in the preliminary experiments, showing satisfactory performance. A modular scheme with subject-specific KPCSR structure proves very efficient, from which each user is assigned an independent KPCSR model for coding the corresponding visual information. Experimental results obtained on public benchmarking signature databases demonstrate the superiority of the proposed method