Offline handwritten signature identification and verification using contourlet transform and Support Vector Machine

2010 ◽  
Author(s):  
Elaheh Soleymanpour ◽  
Boshra Rajae ◽  
Hamid Reza Pourreza
Keyword(s):  
Support Vector Machine ◽  
Contourlet Transform ◽  
Support Vector ◽  
Handwritten Signature

Hyper Accuracy Star Location Algorithm Based on Nonsubsampled Contourlet Transform and Mapped Least Squares Support Vector Machine

2013 ◽  
Vol 33 (5) ◽  
pp. 0512001
Author(s):  
刘南南 Liu Nannan ◽  
徐抒岩 Xu Shuyan ◽  
胡君 Hu Jun ◽  
王栋 Wang Dong ◽  
曹小涛 Cao Xiaotao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Contourlet Transform ◽  
Support Vector ◽  
Nonsubsampled Contourlet Transform ◽  
Location Algorithm

scholarly journals ONLINE HANDWRITTEN SIGNATURE RECOGNITION BY PRINCIPAL COMPONENTS AND SUPPORT VECTOR MACHINE.

2017 ◽  
Vol 5 (4) ◽  
pp. 889-893
Author(s):  
FahadLayth Malallah ◽  
◽  
ZeyadT. Sharef ◽  
KameranHama Farj ◽  
ZaidAhmed Aljawaryy ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Principal Components ◽  
Support Vector ◽  
Signature Recognition ◽  
Handwritten Signature

Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network

2019 ◽  
Vol 15 (4) ◽  
pp. 54-62
Author(s):  
Amruta Bharat Jagtap ◽  
Ravindra S. Hegadi ◽  
K.C. Santosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Feature Learning ◽  
Signature Verification ◽  
Support Vector ◽  
Deep Convolution Neural Network ◽  
Deep Cnn ◽  
Handwritten Signature

In biometrics, handwritten signature verification can be considered as an important topic. In this article, the authors' proposed method to verify handwritten signatures are based on deep convolution neural network (CNN), which is s bio-inspired network that works as if there exists human brain. Deep CNN extracts features from the studied images, which is followed by cubic support vector machine for classification. To evaluate their proposed work, the authors have tested on three different datasets: GPDS, BME2 and SVC20, and have received encouraging results.

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