Offline signature verification system: a graph neural network based approach

2021 ◽  
Author(s):  
Sourodeep Roy ◽  
Debjit Sarkar ◽  
Samir Malakar ◽  
Ram Sarkar
Keyword(s):  
Neural Network ◽  
Signature Verification ◽  
System A ◽  
Verification System ◽  
Offline Signature Verification

Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM

2020 ◽  
Author(s):  
Faiza Eba Batool ◽  
Muhammad Attique ◽  
Muhammad Sharif ◽  
Kashif Javed ◽  
Muhammad Nazir ◽  
...  
Keyword(s):  
Signature Verification ◽  
Geometric Features ◽  
Novel Technique ◽  
System A ◽  
Verification System ◽  
Offline Signature Verification

A new online signature verification system based on combining Mellin transform, MFCC and neural network

2011 ◽  
Vol 21 (2) ◽  
pp. 404-416 ◽  
Author(s):  
Asghar Fallah ◽  
Mahdi Jamaati ◽  
Ali Soleamani
Keyword(s):  
Neural Network ◽  
Mellin Transform ◽  
Signature Verification ◽  
Verification System ◽  
Online Signature ◽  
Online Signature Verification

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

2014 ◽  
Vol 3 (4) ◽  
pp. 156
Author(s):  
Gautam S. Prakash ◽  
Shanu Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Detection System ◽  
Structural Parameters ◽  
Signature Verification ◽  
Width Ratio ◽  
Forgery Detection ◽  
Offline Signature Verification ◽  
Artificial Neural

<p>Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document  authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, I<sup>st</sup> and II<sup>nd</sup> order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.</p>

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