Siamese-Network Based Signature Verification using Self Supervised Learning
| Autor: | Muhammad Fawwaz Mayda, Aina Musdholifah |
|---|---|
| Jazyk: | English<br />Indonesian |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems), Vol 17, Iss 2, Pp 115-126 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1978-1520 2460-7258 |
| DOI: | 10.22146/ijccs.74627 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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