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Artificial Neural Networks based Classification Technique for Iris Recognition

by Shreyansh Daftry
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 4
Year of Publication: 2012
Authors: Shreyansh Daftry
10.5120/9103-3241

Shreyansh Daftry . Artificial Neural Networks based Classification Technique for Iris Recognition. International Journal of Computer Applications. 57, 4 ( November 2012), 22-25. DOI=10.5120/9103-3241

@article{ 10.5120/9103-3241,
author = { Shreyansh Daftry },
title = { Artificial Neural Networks based Classification Technique for Iris Recognition },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 4 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 22-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number4/9103-3241/ },
doi = { 10.5120/9103-3241 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:35.289903+05:30
%A Shreyansh Daftry
%T Artificial Neural Networks based Classification Technique for Iris Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 4
%P 22-25
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Iris recognition is one of the challenging problems inhuman computer interaction. An automated iris recognition system requires an efficient method for classification of iris region in the face sequence, extraction of iris features, and construction of classification model. In recent years, Neural Networks (NN) has demonstrated excellent performance in a variety of classification problems. In this paper, we have used a simple 2dimensionaldiscrete wavelet transform (DWT) representation which captures the small differences in the image that is desired for the current applications. The DWT is used to generate feature images from individual wavelet sub bands. The results of our studies show that, the system gives about 90. 00%recognition rate.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Iris Recognition Discrete wavelet transform Classification model Neural Networks Pattern Recognition Machine Learning

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