Sharma, Neha and Gupta, Sheifali and Mohamed, Heba G. and Anand, Divya and Vidal Mazón, Juan Luis and Gupta, Deepali and Goyal, Nitin UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, divya.anand@uneatlantico.es, juanluis.vidal@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2022) Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification. Sustainability, 14 (18). p. 11484. ISSN 2071-1050
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Abstract
One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | signature verification; two-channel; Siamese network; convolutional neural network; deep learning |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 29 Sep 2022 09:32 |
| Last Modified: | 17 Jul 2023 09:23 |
| URI: | http://repositorio.funiber.org/id/eprint/3740 |
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