A Comparative Analysis on Offline Signature Verification System Using Deep Convolutional Neural Network

2021 ◽  
pp. 609-618
Author(s):  
Deepak Moud ◽  
Pooja Sharma ◽  
Rahul Chandra Kushwaha
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Signature Verification ◽  
Verification System ◽  
Offline Signature Verification

scholarly journals Writer Independent Manipuri Offline Signature Verification using Transfer Learning Technique of Convolutional Neural Network

2019 ◽  
Vol 9 (1S) ◽  
pp. 76-80
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Ability ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Powerful Learning ◽  
Offline Signature Verification ◽  
Learning Technique ◽  
Handwritten Signature

Automatic Signature Verification system is used to verify whether a signature is genuine or forged. Forged Signatures are those signatures that a person produced by imitating the signature of another person. Automatic Signature Verification is very important as a person’s handwritten signature is used everywhere to authenticate themselves and there is not very much difference between a genuine signature and the imitation of it, i.e. a forged signature. In this work, signature verification is done using different pre-trained Convolutional Neural Networks (CNNs). Convolutional Neural Network has powerful learning ability, and it can be used to distinguish between a genuine and a forged signature automatically. In this experiment, Manipuri signature dataset was used, the dataset was prepared originally and it contains 729 genuine signatures and 243 forged signatures. Features were extracted from pre-trained networks and classification was done using binary Support Vector Machine (SVM) classifier and the performances of the networks were compared. And according to the experiment we achieved a classification accuracy of 84.7 using VGG19 features, accuracy of 86.8 using VGG16 features and accuracy of 81.9 using Alexnet features.

scholarly journals 3-D Offline Signature Verification with Convolutional Neural Network

2020 ◽  
Author(s):  
Na Tyrer ◽  
Fan Yang ◽  
Gary C. Barber ◽  
Guangzhi Qu ◽  
Bo Pang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Signature Verification ◽  
Offline Signature Verification ◽  
Optical Profilometer ◽  
Novel Method

Signature verification is essential to prevent the forgery of documents in financial, commercial, and legal settings. There are many researchers have focused on this topic, however, utilizing the 3-D information presented by a signature using a 3D optical profilometer is a relatively new idea, and the convolutional neural network is a powerful tool for image recognition. The present research focused on using the 3 dimensions of offline signatures in combination with a convolutional neural network to verify signatures. It was found that the accuracy of the data for offline signature verification was over 90%, which shows promise for this method as a novel method in signature verification.

scholarly journals Offline Signature Verification using Deep Learning Convolutional Neural Network (CNN) Architectures GoogLeNet Inception-v1 and Inception-v3

2019 ◽  
Vol 161 ◽  
pp. 475-483 ◽  
Author(s):  
Jahandad ◽  
Suriani Mohd Sam ◽  
Kamilia Kamardin ◽  
Nilam Nur Amir Sjarif ◽  
Norliza Mohamed
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Signature Verification ◽  
Offline Signature Verification
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