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Multi-algorithmic IRIS Recognition

by G. Sathish, Dr. S.V.Saravanan, Dr. S. Narmadha, Dr. S. Uma Maheswari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 11
Year of Publication: 2012
Authors: G. Sathish, Dr. S.V.Saravanan, Dr. S. Narmadha, Dr. S. Uma Maheswari
10.5120/4745-6934

G. Sathish, Dr. S.V.Saravanan, Dr. S. Narmadha, Dr. S. Uma Maheswari . Multi-algorithmic IRIS Recognition. International Journal of Computer Applications. 38, 11 ( January 2012), 13-21. DOI=10.5120/4745-6934

@article{ 10.5120/4745-6934,
author = { G. Sathish, Dr. S.V.Saravanan, Dr. S. Narmadha, Dr. S. Uma Maheswari },
title = { Multi-algorithmic IRIS Recognition },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 11 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 13-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number11/4745-6934/ },
doi = { 10.5120/4745-6934 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:25:06.367836+05:30
%A G. Sathish
%A Dr. S.V.Saravanan
%A Dr. S. Narmadha
%A Dr. S. Uma Maheswari
%T Multi-algorithmic IRIS Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 11
%P 13-21
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Modern societies give higher relevance to personal recognition system that contribute to the increase of security and reliability, essentially due to terrorism and other extremism or illegal activities. The objective of this work is to present a multi-algorithmic biometric authentication system for physical access control based on iris pattern for high security access. The CASIA database of IRIS images provided by Chinese Academy of Sciences Institute of Automation is used and the system is implemented in MATLAB. The iris recognition is based on Daugman's approach and multiple classifiers using Hamming distance and Neural networks. In Daugman's approach, the iris features are extracted using 2D Gabor Wavelets. The proposed work provides match for iris pattern if hamming distance is below 0.15 whereas for the existing works it is 0.20. The Neural Classifier uses a feed forward network with three hidden layers and used after normalization and feature extraction phase. Features given to neural network are Energy, Entropy, Standard deviation, Covariance. The error rate has been reduced from e-3 to e-5 in this proposed work. The multi-algorithmic approach together with improvement in segmentation and matching stages is found to report higher verification accuracy with lower error rate.

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Index Terms

Computer Science
Information Sciences

Keywords

Iris Recognition 2D Gabor Wavelets Hamming Distance Neural Classifier Feed Forward Network CASIA