Offline signature verification system based on the online data
EURASIP Journal on Advances in Signal Processing
Off-line signature recognition using morphological pixel variance analysis
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Signature recognition using vector quantization
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Off-line signature verification systems: a survey
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Offline signature verification and identification by hybrid features and Support Vector Machine
International Journal of Artificial Intelligence and Soft Computing
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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 off-line 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 database demonstrates the superiority of the proposed method.