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Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study

by Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 7
Year of Publication: 2012
Authors: Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay
10.5120/9128-3295

Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay . Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study. International Journal of Computer Applications. 57, 7 ( November 2012), 33-41. DOI=10.5120/9128-3295

@article{ 10.5120/9128-3295,
author = { Tirtharaj Dash, Tanistha Nayak, Subhagata Chattopadhyay },
title = { Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 7 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 33-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number7/9128-3295/ },
doi = { 10.5120/9128-3295 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:50.006313+05:30
%A Tirtharaj Dash
%A Tanistha Nayak
%A Subhagata Chattopadhyay
%T Handwritten Signature Verification (Offline) using Neural Network Approaches: A Comparative Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 7
%P 33-41
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Forgery detection has been a challenging area in the field of biometry, e. g. , handwritten signatures. Signature verification is a bi-objective optimization problem. The two crucial parameters are accuracy and time of computation. In this work, a comprehensive study on application of Adaptive Resonance Theory (ART) Nets (Type 1 and 2) and Associative Memory Net (AMN) has been conducted. To decrease the time complexity a corresponding parallel version using OpenMP is developed for each algorithm. The algorithms are trained with the original/genuine signature and tested with a sample of twelve very similar-looking forged signatures. The study concludes that ART-1 detects fake signatures with an accuracy of 99. 89%; whereas, ART-2 and AMN detect forgery with accuracies of 99. 99% and 75. 68% respectively which are comparable to other methods cited in this paper.

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Index Terms

Computer Science
Information Sciences

Keywords

Forgery detection signature verification bi-objective optimization Adaptive Resonance Theory Associative Memory Net

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