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Iris Feature Extraction for Personal Identification using Lifting Wavelet Transform

by C.M. Patil, Sudarshan Patilkulkarani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 14
Year of Publication: 2010
Authors: C.M. Patil, Sudarshan Patilkulkarani
10.5120/298-462

C.M. Patil, Sudarshan Patilkulkarani . Iris Feature Extraction for Personal Identification using Lifting Wavelet Transform. International Journal of Computer Applications. 1, 14 ( February 2010), 68-72. DOI=10.5120/298-462

@article{ 10.5120/298-462,
author = { C.M. Patil, Sudarshan Patilkulkarani },
title = { Iris Feature Extraction for Personal Identification using Lifting Wavelet Transform },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 14 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 68-72 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number14/298-462/ },
doi = { 10.5120/298-462 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:42:13.330511+05:30
%A C.M. Patil
%A Sudarshan Patilkulkarani
%T Iris Feature Extraction for Personal Identification using Lifting Wavelet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 14
%P 68-72
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach has become a major research topic with practical applications in recent years as it promises nearly perfect recognition rates. In this paper, we present a novel, efficient approach for iris recognition. Our goal is to develop a lifting (integer) wavelet based algorithm that enhances iris images, reduces noise to the maximum extent possible, and extracts the important features from the image. Then the similarity between two iris images is estimated using some standard distance measures and comparison of threshold. The proposed technique is computationally effective with recognition rate of 99.97 % on the standard CASIA iris database.

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Index Terms

Computer Science
Information Sciences

Keywords

Iris recognition biometrics identification security

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