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Proceedings Paper

Offline signature verification using convolution Siamese network
Author(s): Zi-Jian Xing; Fei Yin; Yi-Chao Wu; Cheng-Lin Liu
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Paper Abstract

This paper presents an offline signature verification approach using convolutional Siamese neural network. Unlike the existing methods which consider feature extraction and metric learning as two independent stages, we adopt a deepleaning based framework which combines the two stages together and can be trained end-to-end. The experimental results on two offline public databases (GPDSsynthetic and CEDAR) demonstrate the superiority of our method on the offline signature verification problem.

Paper Details

Date Published: 10 April 2018
PDF: 9 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151I (10 April 2018); doi: 10.1117/12.2303380
Show Author Affiliations
Zi-Jian Xing, National Lab. of Pattern Recognition (China)
Univ. of Chinese Academy of Sciences (China)
Fei Yin, National Lab. of Pattern Recognition (China)
Univ. of Chinese Academy of Sciences (China)
Yi-Chao Wu, National Lab. of Pattern Recognition (China)
Univ. of Chinese Academy of Sciences (China)
Cheng-Lin Liu, National Lab. of Pattern Recognition (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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