Offline handwritten signature verification and recognition based on deep transfer learning using convolutional neural networks (a literature review)
Paper ID : 1158-MVIP2020
Atefeh Foroozandeh *1, Ataollah Askari Hemmat2, Hossein Rabbani3
1Department of Applied Mathematics, Faculty of Sciences and Modern Technology, Graduate University of Advanced Technology, Kerman, Iran
2Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman
3Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences
Recently, deep convolutional neural networks have been successfully applied in different fields of computer vision and pattern recognition. Offline handwritten signature is one of the most important biometrics applied in banking systems, administrative and financial applications, which is a challenging task and still hard. The aim of this study is to review of the presented signature verification/recognition methods based on the convolutional neural networks and also evaluate the performance of some prominent available deep convolutional neural networks in offline handwritten signature verification/recognition as feature extractor using transfer learning. This is done using four pretrained models as the most used general models in computer vision tasks including VGG16, VGG19, ResNet50, and InceptionV3 and also two pre-trained models especially presented for signature processing tasks including SigNet and SigNet-F. Experiments have been conducted using two benchmark signature datasets: GPDS Synthetic signature dataset and MCYT-75 as Latin signature datasets, and two Persian datasets: UTSig and FUM-PHSD. Obtained experimental results, in comparison with literature, verify the effectiveness of the models: VGG16 and SigNet for signature verification and the superiority of VGG16 in signature recognition task.
Offline Handwritten Signature Verification, Signature Recognition, Convolutional Neural Network, Deep Transfer Learning
Status : Paper Accepted (Oral Presentation)