Proceedings ArticleDOI
Offline Handwritten Signature Verification and Recognition Based on Deep Transfer Learning
TLDR
Experimental results verify the effectiveness of the models: VGG16 and SigNet for signature verification and the superiority of V GG16 in signature recognition task.Abstract:
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.read more
Citations
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Journal ArticleDOI
CapsNet regularization and its conjugation with ResNet for signature identification
TL;DR: A new regularization term for CapsNet is proposed that significantly improves the generalization power of the original method from small training data while requiring much fewer parameters, making it suitable for large input images.
Journal ArticleDOI
DeepSignature: fine-tuned transfer learning based signature verification system
Saeeda Naz,Kiran Bibi,Riaz Ahmad +2 more
TL;DR: The experimental analyses demonstrate that DenseNet architecture outperformed the other architectures of CNN and a fine-tuned transfer learning-based approach is investigated to verify and identify the offline images of signatures.
Journal ArticleDOI
A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection
TL;DR: This paper presents a comparative study of various deep learning models using Siamese architecture, over a wide catalogue of signature images, and applies a set of classifiers to classify the signature as genuine or forged.
Journal Article
Machine learning for signature verification
TL;DR: In this article, the authors proposed two types of learning to verify whether a questioned signature matches known signature samples: general learning and person-dependent learning, where the differences between genuines and forgeries across all individuals are learned.
Proceedings ArticleDOI
Handwritten Signature Verification System using Deep Learning
TL;DR: In this article , the authors evaluate the performance of a few well-known deep convolutional neural networks as feature extractors in handwritten signature verification using transfer learning with activation function, as well as to review the available convolution neural network signature verification methods.
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