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http://dx.doi.org/10.6109/jkiice.2017.21.12.2298

Online Signature Verification Method using General Handwriting Data  

Heo, Gyeongyong (School of Electrical, Electronic & Communication Eng., Dong-Eui University)
Kim, Seong-Hoon (Department of Software, Kyungpook National University)
Woo, Young Woon (School of Creative Software Eng., Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.12, 2017 , pp. 2298-2304 More about this Journal
Abstract
Online signature verification is one of the simple and efficient method of identity verification and has less resistance than other biometric technologies. In training to build a verification model, negative samples are required to build the model, but in most practical applications it is not easy to get negative samples - forgery signatures. In this paper, proposed is a method using someone else's signatures as negative samples. In verification, shape-based features extracted from the time-sequenced signature data are extracted and a support vector machine is used to verify. SVM tries to map a feature vector to a high dimensional space and to draw a linear boundary in the high dimensional space. SVM is one of the best classifiers and has been applied to various applications. Using general handwriting data, i.e., someone else's signatures which have little in common with positive samples improved the verification rate experimentally, which means that signature verification without negative samples is possible.
Keywords
Online Signature Verification; Forgery; Support Vector Machine; General Handwriting;
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Times Cited By KSCI : 4  (Citation Analysis)
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