Signature verification is considered one of the main features in determining the person identity. Our proposed framework emphasizes the potential of Deep Learning Models (DLMs) in revolutionizing signature verification techniques and underscores the need for continuous exploration and advancement in the realm of automated signature authentication. Therefore, five pre-trained DLMs, ResNet50, DenseNet121, MobileNetV3, InceptionV3, and VGG16, based on five different datasets, the ICDAR 2011 (Dutch) , CEDAR, BH-Sig260 Bengali, BHSig260 Hindi,and ICDAR 2011(Dutch), are introduced in this paper to verify the person identity. Furthermore, data augmentation techniques are applied to overcome dataset limitations and increase the framework's performance. Additionally, transfer learning and fine-tuning techniques are performed to reduce computational time and memory usage. It is observed that the InceptionV3 DLM based on the ICDAR 2011 (Dutch) achieved the best performance of 100% accuracy, 100% AUC and 100% sensitivity. While, CEDAR Dataset achieves performance with an accuracy of 99.76%, an AUC of 99.94%, sensitivity of 99.76%, precision of 99.76%, an F1-score of 99.71%, score, and a computational time of 13.627s.
Salama, W. (2023). Signature Verification Based on Deep Learning. Alexandria Journal of Science and Technology, 1(2), 55-63. doi: 10.21608/ajst.2023.236375.1016
MLA
Wessam M. Salama. "Signature Verification Based on Deep Learning". Alexandria Journal of Science and Technology, 1, 2, 2023, 55-63. doi: 10.21608/ajst.2023.236375.1016
HARVARD
Salama, W. (2023). 'Signature Verification Based on Deep Learning', Alexandria Journal of Science and Technology, 1(2), pp. 55-63. doi: 10.21608/ajst.2023.236375.1016
VANCOUVER
Salama, W. Signature Verification Based on Deep Learning. Alexandria Journal of Science and Technology, 2023; 1(2): 55-63. doi: 10.21608/ajst.2023.236375.1016
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