Tsung-Yu Lu1, Mu-En Wu2, Er-Hao Chen3, Yeong-Luh Ueng4,*
CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 935-952, 2022, DOI:10.32604/cmc.2022.026717
- 18 May 2022
Abstract 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 More >
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