Abstract
Handwritten signature is a widely used biometric. The most challenging problem in automatic signature verification is to detect skilled forgery which is similar to the genuine signatures. This paper presents a novel method for extracting features for off-line signature verification. These features is based on probability distribution function, which characterizes the frequent structural patterns distribution of a signature image. Experiments were conducted on an publicly available signature database MCYT corpus. Experimental results show that the proposed method was able to improve the verification accuracy.
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Wen, J., Chen, M., Ren, J. (2014). Off-Line Signature Verification Based on Local Structural Pattern Distribution Features. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_53
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DOI: https://doi.org/10.1007/978-3-662-45643-9_53
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