Enhancing Signature Verification Using Triplet Siamese Similarity Networks in Digital Documents

Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir, Norma Latif Fitriyani () and Muhammad Syafrudin ()
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Sara Tehsin: Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44080, Pakistan
Ali Hassan: Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44080, Pakistan
Farhan Riaz: Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44080, Pakistan
Inzamam Mashood Nasir: Department of Computer Science, HITEC University Taxila, Taxila 47040, Pakistan
Norma Latif Fitriyani: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Muhammad Syafrudin: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2024, vol. 12, issue 17, 1-15

Abstract: In contexts requiring user authentication, such as financial, legal, and administrative systems, signature verification emerges as a pivotal biometric method. Specifically, handwritten signature verification stands out prominently for document authentication. Despite the effectiveness of triplet loss similarity networks in extracting and comparing signatures with forged samples, conventional deep learning models often inadequately capture individual writing styles, resulting in suboptimal performance. Addressing this limitation, our study employs a triplet loss Siamese similarity network for offline signature verification, irrespective of the author. Through experimentation on five publicly available signature datasets—4NSigComp2012, SigComp2011, 4NSigComp2010, and BHsig260—various distance measure techniques alongside the triplet Siamese Similarity Network (tSSN) were evaluated. Our findings underscore the superiority of the tSSN approach, particularly when coupled with the Manhattan distance measure, in achieving enhanced verification accuracy, thereby demonstrating its efficacy in scenarios characterized by close signature similarity.

Keywords: signature verification; triplet Siamese similarity network; document forgery; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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