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A Comparative Study of Feature Extraction and Classification Methods for Iris Recognition

by Thiyam Churjit Meetei, Shahin Ara Begum
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 7
Year of Publication: 2014
Authors: Thiyam Churjit Meetei, Shahin Ara Begum
10.5120/15514-4251

Thiyam Churjit Meetei, Shahin Ara Begum . A Comparative Study of Feature Extraction and Classification Methods for Iris Recognition. International Journal of Computer Applications. 89, 7 ( March 2014), 13-20. DOI=10.5120/15514-4251

@article{ 10.5120/15514-4251,
author = { Thiyam Churjit Meetei, Shahin Ara Begum },
title = { A Comparative Study of Feature Extraction and Classification Methods for Iris Recognition },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 7 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number7/15514-4251/ },
doi = { 10.5120/15514-4251 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:36.932919+05:30
%A Thiyam Churjit Meetei
%A Shahin Ara Begum
%T A Comparative Study of Feature Extraction and Classification Methods for Iris Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 7
%P 13-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Iris recognition is one of commonly employed biometric for personal recognition. In this paper, Single Value Decomposition (SVD), Automatic Feature Extraction (AFE), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used to extract the iris feature from a pattern named IrisPattern based on the iris image. The IrisPatterns are classified using a Feedforward Backpropagation Neural Network (BPNN) and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel with different dimensions and a comparative study is carried out. From the experimental result, it is observed that ICA is the most effective feature extraction method for both BPNN and SVM with Gaussian RBF for the consider datats. Futher, SVM with Gaussian RBF can classify faster than BPNN.

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Index Terms

Computer Science
Information Sciences

Keywords

Iris Recognition. image Segmentation. SVM. Classification.

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