Offline signature verification using deep learning method | ||
| International Journal of Theoretical and Applied Research | ||
| Article 8, Volume 2, Issue 2, December 2023, Pages 225-233 PDF (499.08 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijtar.2023.205346.1051 | ||
| Authors | ||
| Nehal Hamdy Al banhawy* 1; Heba Mohsen2; Neveen Ibrahim Ghali3; Ayman Khedr4 | ||
| 1Math and computer science department, Faculty of Science, Al-Azhar university, Cairo, Egypt | ||
| 2Lecturer, Computer Science Department, Faculty of Computers and Information Technology, Future university in Egypt, New Cairo, Egypt | ||
| 3Head of Digital Media Technology Department, Faculty of Computers and Information Technology, Future university in Egypt, New Cairo, Egypt | ||
| 4Information systems department, Faculty of computers and artificial intelligence, Helwan university, Cairo, Egypt | ||
| Abstract | ||
| One of the most challenging biometric authentication problems in recent years that we experience in daily life is signature verification. Signature verification systems are classified into two main approaches: offline systems and online systems. The offline signature verification systems are more difficult than the online systems since online systems have further information, such as velocity of writing, motion style and pen pressure, which allow for extracting of more features. This paper presents a deep learning method based on using the convolutional neural network (CNN) model for solving the offline signature verification problem to prevent the process of faking signatures that thieves practice. The CNN model was applied for extracting features and classifying whether the signature is genuine or forged. Our proposed method succeeded in achieving an accuracy of 94.73% on CEDAR dataset by using two types of signatures: genuine signatures and skilled forged signatures to test the performance of the system, which indicates that the method was effective and it can be supported by more feature extractors to get better results. | ||
| Keywords | ||
| Signature analysis, signature verification; deep learning; convolutional neural network | ||
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