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Reseach Article

GA based Enhanced Irislet for IRIS Recognition

Published on September 2016 by Kulvir Mahi, Naresh Kumar, Sarvjit Singh
International Conference on Advances in Emerging Technology
Foundation of Computer Science USA
ICAET2016 - Number 9
September 2016
Authors: Kulvir Mahi, Naresh Kumar, Sarvjit Singh
5e1b7424-7cfa-4e7d-89a9-fd2d7d320937

Kulvir Mahi, Naresh Kumar, Sarvjit Singh . GA based Enhanced Irislet for IRIS Recognition. International Conference on Advances in Emerging Technology. ICAET2016, 9 (September 2016), 32-38.

@article{
author = { Kulvir Mahi, Naresh Kumar, Sarvjit Singh },
title = { GA based Enhanced Irislet for IRIS Recognition },
journal = { International Conference on Advances in Emerging Technology },
issue_date = { September 2016 },
volume = { ICAET2016 },
number = { 9 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 32-38 },
numpages = 7,
url = { /proceedings/icaet2016/number9/25937-t151/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Emerging Technology
%A Kulvir Mahi
%A Naresh Kumar
%A Sarvjit Singh
%T GA based Enhanced Irislet for IRIS Recognition
%J International Conference on Advances in Emerging Technology
%@ 0975-8887
%V ICAET2016
%N 9
%P 32-38
%D 2016
%I International Journal of Computer Applications
Abstract

Now a day's Biometrics is the most acceptable to identify any person. It is an authentication technique which place confidence in measurable individual and physiological characteristics that will be mechanically verified. A biometric system could operate either in identification mode or verification mode. Because the level of security breaches and dealings fraud have increased, the necessity of technologies for extremely secure identification and private verification is changing into apparent. In this paper, different methods have been used which recognizes the iris samples. This work uses 50 samples of Iris which were collected by 25 known people where each includes 2 samples. For this Irislet and GA based Irislet is used which shows that GA based Irislet recognizes efficiently all the samples even when samples are noisy. Because irislet fails to recognize noisy samples accurately. This proposed GA-based Irislet achieve 100% accuracy for noisy samples also.

References
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Index Terms

Computer Science
Information Sciences

Keywords

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