Siamese-Network Based Signature Verification using Self Supervised Learning

Muhammad Fawwaz Mayda(1*), Aina Musdholifah(2)

(1) Undergraduate Program of Computer Science, FMIPA UGM, Yogyakarta
(2) Departement of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author


The use of signatures is often encountered in various public documents ranging from academic documents to business documents that are a sign that the existence of signatures is crucial in various administrative processes. The frequent use of signatures does not mean a procedure without loopholes, but we must remain vigilant against signature falsification carried out with various motives behind it. Therefore, in this study, a signature verification system was developed that could prevent the falsification of signatures in public documents by using digital imagery of existing signatures. This study used neural networks with siamese network-based architectures that also empower self-supervised learning techniques to improve accuracy in the realm of limited data. The final evaluation of the machine learning method used gets a maximum accuracy of 83% and this result is better than the machine learning model that does not involve self-supervised learning methods.


machine learning; siamese network; self-supervised learning; signature

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