TY  - JOUR
N2  - 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
AV  - public
IS  - 18
ID  - uninimx3740
Y1  - 2022/09//
A1  - Sharma, Neha
A1  - Gupta, Sheifali
A1  - Mohamed, Heba G.
A1  - Anand, Divya
A1  - Vidal Mazón, Juan Luis
A1  - Gupta, Deepali
A1  - Goyal, Nitin
JF  - Sustainability
KW  - signature verification; two-channel; Siamese network; convolutional neural network; deep learning
TI  - Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification
UR  - http://doi.org/10.3390/su141811484
VL  - 14
SN  - 2071-1050
ER  -