Learning Approach for Offline Signature Verification Using Vector Quantization Technique

2015 ◽  
pp. 337-344
Author(s):  
Aarti Chugh ◽  
Charu Jain ◽  
Priti Singh ◽  
Preeti Rana
Keyword(s):  
Vector Quantization ◽  
Signature Verification ◽  
Learning Approach ◽  
Offline Signature Verification

scholarly journals DWT based Offline Signature Verification using Angular Features

2012 ◽  
Vol 52 (15) ◽  
pp. 40-48 ◽  
Author(s):  
Prashanth C.R ◽  
K. B. Raja ◽  
K. R. Venugopal ◽  
L. M. Patnaik
Keyword(s):  
Signature Verification ◽  
Offline Signature Verification

scholarly journals Offline Signature Identification and Verification Based on Capsule Representations

2020 ◽  
Vol 20 (5) ◽  
pp. 60-67
Author(s):  
Dilara Gumusbas ◽  
Tulay Yildirim
Keyword(s):  
Feature Extraction ◽  
Daily Life ◽  
Extraction Methods ◽  
Signature Verification ◽  
Verification Task ◽  
Feature Representations ◽  
Automatic Feature Extraction ◽  
Offline Signature Verification ◽  
Orientation Information ◽  
Trained Network

AbstractOffline signature is one of the frequently used biometric traits in daily life and yet skilled forgeries are posing a great challenge for offline signature verification. To differentiate forgeries, a variety of research has been conducted on hand-crafted feature extraction methods until now. However, these methods have recently been set aside for automatic feature extraction methods such as Convolutional Neural Networks (CNN). Although these CNN-based algorithms often achieve satisfying results, they require either many samples in training or pre-trained network weights. Recently, Capsule Network has been proposed to model with fewer data by using the advantage of convolutional layers for automatic feature extraction. Moreover, feature representations are obtained as vectors instead of scalar activation values in CNN to keep orientation information. Since signature samples per user are limited and feature orientations in signature samples are highly informative, this paper first aims to evaluate the capability of Capsule Network for signature identification tasks on three benchmark databases. Capsule Network achieves 97 96, 94 89, 95 and 91% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 and 32×32 resolutions, which are lower than usual, respectively. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Capsule Network achieves average 91, 86, and 89% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 resolutions, respectively. Through this evaluation, the capability of Capsule Network is shown for offline verification and identification tasks.

scholarly journals Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

2018 ◽  
pp. 470-480 ◽  
Author(s):  
Paul Maergner ◽  
Vinaychandran Pondenkandath ◽  
Michele Alberti ◽  
Marcus Liwicki ◽  
Kaspar Riesen ◽  
...  
Keyword(s):  
Edit Distance ◽  
Signature Verification ◽  
Graph Edit Distance ◽  
Offline Signature Verification
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