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Reference Selection for Offline Hybrid Siamese Signature Verification Systems

Tsung-Yu Lu1, Mu-En Wu2, Er-Hao Chen3, Yeong-Luh Ueng4,*

1 Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
2 Department of Information and Finance Management, National Taipei University of Technology, Taipei, Taiwan
3 Department of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan
4 Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan

* Corresponding Author: Yeong-Luh Ueng. Email: email

Computers, Materials & Continua 2022, 73(1), 935-952.


This paper presents an off-line handwritten signature verification system based on the Siamese network, where a hybrid architecture is used. The Residual neural Network (ResNet) is used to realize a powerful feature extraction model such that Writer Independent (WI) features can be effectively learned. A single-layer Siamese Neural Network (NN) is used to realize a Writer Dependent (WD) classifier such that the storage space can be minimized. For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively, we propose a method of selecting a reference signature as one of the inputs for the Siamese network. To take full advantage of the reference signature, we modify the conventional contrastive loss function to enhance the accuracy. By using the proposed techniques, the accuracy of the system can be increased by 5.9%. Based on the GPDS signature dataset, the proposed system is able to achieve an accuracy of 94.61% which is better than the accuracy achieved by the current state-of-the-art work.


Cite This Article

T. Lu, M. Wu, E. Chen and Y. Ueng, "Reference selection for offline hybrid siamese signature verification systems," Computers, Materials & Continua, vol. 73, no.1, pp. 935–952, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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