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Reseach Article

Effect of Moment Invariants on Signature Recognition Rate by using Fuzzy Min-Max Neural Networks

Published on December 2014 by Jayesh Rane, Sagar More
National Conference on Advances in Communication and Computing
Foundation of Computer Science USA
NCACC2014 - Number 1
December 2014
Authors: Jayesh Rane, Sagar More
f4d40ff9-86a6-40ef-94e7-46a0ecb27964

Jayesh Rane, Sagar More . Effect of Moment Invariants on Signature Recognition Rate by using Fuzzy Min-Max Neural Networks. National Conference on Advances in Communication and Computing. NCACC2014, 1 (December 2014), 21-24.

@article{
author = { Jayesh Rane, Sagar More },
title = { Effect of Moment Invariants on Signature Recognition Rate by using Fuzzy Min-Max Neural Networks },
journal = { National Conference on Advances in Communication and Computing },
issue_date = { December 2014 },
volume = { NCACC2014 },
number = { 1 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 21-24 },
numpages = 4,
url = { /proceedings/ncacc2014/number1/19121-2006/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Communication and Computing
%A Jayesh Rane
%A Sagar More
%T Effect of Moment Invariants on Signature Recognition Rate by using Fuzzy Min-Max Neural Networks
%J National Conference on Advances in Communication and Computing
%@ 0975-8887
%V NCACC2014
%N 1
%P 21-24
%D 2014
%I International Journal of Computer Applications
Abstract

This paper presents a method of recognition of signatures by Fuzzy Min-Max Neural Networks and analyses the effect of moment invariants on signature recognition by comparing the accuracy of recognition. In addition, database is also tested by fuzzy min-max neural networks for recognition of signatures resulting more accurate results. Image processing and fuzzy neural network toolboxes are used in person identification system provided by MATLAB. For the identification of signatures database is created for five persons with the thirty times repetitions. These signatures are preprocessed by scanning the images and then converting them to standard binary images. The features are selected and extracted which gives the information about the structure of signature. This paper also investigates the performance of the system by using fuzzy min max neural networks classifier.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Min Max Neural Networks Handwritten Signatures Artificial Neural Network Multi Layer Perceptrons Hu's Seven Moment Invariants.