Feature selection method based on quantum inspired genetic algorithm for Arabic signature verification

Abdulhussien, Ansam A.; Nasrudin, Mohammad Faidzul; Darwish, Saad M.; Alyasseri, Zaid A.

doi:10.1016/j.jksuci.2023.02.005

Cited by 4 publications

(1 citation statement)

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“…However, it is important to note that many DLMs demand substantial computational effort for accurate verification, and their effectiveness hinges on the features employed to characterize the signature images. Consequently, researchers have explored various techniques for the verification process, including Neural Networks [13], Support Vector Machines [14], Hidden Markov Models [15], Genetic algorithms [16], Euclidean distance, k-nearest neighbors, among others. Geometric features, local and global characteristics [17], have also been extensively examined in the literature.…”

Section: Introdctionmentioning

confidence: 99%

Signature Verification Based on Deep Learning

Salama

2023

Alexandria Journal of Science and Technology

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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 four different datasets, 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 finetuning 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.

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Section: Introdctionmentioning

confidence: 99%