Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification

Neha Sharma, Sheifali Gupta, Heba G. Mohamed, Divya Anand, Juan Luis Vidal Mazón, Deepali Gupta () and Nitin Goyal
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Neha Sharma: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Sheifali Gupta: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Heba G. Mohamed: Department of Electrical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Divya Anand: Department of Computer Science, Lovely Professional University, Phagwara 144001, Punjab, India
Juan Luis Vidal Mazón: Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
Deepali Gupta: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Nitin Goyal: Computer Science and Engineering Department, Shri Vishwakarma Skill University, Palwal 121102, Haryana, India

Sustainability, 2022, vol. 14, issue 18, 1-14

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.

Keywords: signature verification; two-channel; Siamese network; convolutional neural network; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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