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A Robust On-line Signature Verification System

  • Ryu, Sang-Yeun (Chungbuk National University, School of Electrical and Computer Engineering) ;
  • Lee, Dae-Jong (Electrical Safety Research Institute) ;
  • Chun, Myung-Geun (Chungbuk National University, School of Electrical and Computer Engineering)
  • Published : 2003.06.01

Abstract

This paper proposes a robust on-line signature verification system based on a new segmentation method and fusion scheme. The proposed segmentation method resolves the problem of segment-to-segment comparison where the variation between reference signature and input signature causes the errors in the location and the number of segments. In addition, the fusion scheme is adopted, which discriminates genuineness by calculating each feature vector's fuzzy membership degree yielded from the proposed segmentation method. Experimental results show that the proposed signature verification system has lower False Reject Rate(FRR) for genuine signature and False Accept Rate(FAR) for forgery signature.

Keywords

References

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  1. Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction vol.8, pp.2, 2018, https://doi.org/10.3390/app8020153