Reference selection for offline writer-dependent signature verification using a Siamese convolutional neural network

2021 ◽  
Author(s):  
Ming-I Lo ◽  
Tsung-Yu Lu ◽  
Er-Hao Chen ◽  
Yeong-Luh Ueng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signature Verification ◽  
Reference Selection ◽  
Selection For

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.

Signature Verification Using Convolutional Neural Network

2019 ◽  
Author(s):  
Shayekh Mohiuddin Ahmed Navid ◽  
Shamima Haque Priya ◽  
Nabiul Hoque Khandakar ◽  
Zannatul Ferdous ◽  
Akm Bahalul Haque
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signature Verification

scholarly journals Potential Role of Convolutional Neural Network Based Algorithm in Patient Selection for DCIS Observation Trials Using a Mammogram Dataset

2020 ◽  
Vol 27 (6) ◽  
pp. 774-779
Author(s):  
Simukayi Mutasa ◽  
Peter Chang ◽  
Eduardo P. Van Sant ◽  
John Nemer ◽  
Michael Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Patient Selection ◽  
Potential Role ◽  
Selection For

Handwritten signature verification using shallow convolutional neural network

2020 ◽  
Vol 79 (27-28) ◽  
pp. 19993-20018
Author(s):  
Anamika Jain ◽  
Satish Kumar Singh ◽  
Krishna Pratap Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signature Verification ◽  
Handwritten Signature
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