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Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine

by Manisha Nirgude, Sachine Gengaje
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 7
Year of Publication: 2017
Authors: Manisha Nirgude, Sachine Gengaje
10.5120/ijca2017915366

Manisha Nirgude, Sachine Gengaje . Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine. International Journal of Computer Applications. 173, 7 ( Sep 2017), 28-33. DOI=10.5120/ijca2017915366

@article{ 10.5120/ijca2017915366,
author = { Manisha Nirgude, Sachine Gengaje },
title = { Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 7 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number7/28349-2017915366/ },
doi = { 10.5120/ijca2017915366 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:39.253094+05:30
%A Manisha Nirgude
%A Sachine Gengaje
%T Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 7
%P 28-33
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Iris recognition system is becoming more popular day by day and is being used in many sectors for authentication replacing traditional methods like password, ATM etc. Iris recognition system is more accurate due to unique and stable iris patterns. Here, a feature extraction method based on multi-resolution analysis is proposed. Iris image is represented at multiple resolution levels and feature vector is formed by combining detailed information obtained at different resolution levels. Further, support vector machine classifier is used for recognition purpose to handle nonlinearity of features. Experiment is performed using CASIA 3.0 database with an objective to arrive at optimum number of features with high recognition rate.

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Index Terms

Computer Science
Information Sciences

Keywords

Iris recognition Multi-resolution analysis wavelet transform support vector machine RBF kernel

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