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Reseach Article

Iris Recognition based on PCA for Person Identification

Published on September 2015 by Aniket S. Buddharpawar, and S. Subbaraman
Emerging Applications of Electronics System, Signal Processing and Computing Technologies
Foundation of Computer Science USA
NCESC2015 - Number 1
September 2015
Authors: Aniket S. Buddharpawar, and S. Subbaraman
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Aniket S. Buddharpawar, and S. Subbaraman . Iris Recognition based on PCA for Person Identification. Emerging Applications of Electronics System, Signal Processing and Computing Technologies. NCESC2015, 1 (September 2015), 9-12.

@article{
author = { Aniket S. Buddharpawar, and S. Subbaraman },
title = { Iris Recognition based on PCA for Person Identification },
journal = { Emerging Applications of Electronics System, Signal Processing and Computing Technologies },
issue_date = { September 2015 },
volume = { NCESC2015 },
number = { 1 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 9-12 },
numpages = 4,
url = { /proceedings/ncesc2015/number1/22360-7325/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Applications of Electronics System, Signal Processing and Computing Technologies
%A Aniket S. Buddharpawar
%A and S. Subbaraman
%T Iris Recognition based on PCA for Person Identification
%J Emerging Applications of Electronics System, Signal Processing and Computing Technologies
%@ 0975-8887
%V NCESC2015
%N 1
%P 9-12
%D 2015
%I International Journal of Computer Applications
Abstract

With over decade of intensive research in the field of biometric, security based applications havebeen developed. There are many biometric security systemsfor person identificationbased on palm print, face, voice, iris, etc. Many researchers have recommended PCA as an efficient algorithm for such applications due to its simplicity, accuracy, and dimensionality reduction on large dataset while retaining as much as original information as possible. This paper presents the details of PCA tool for analyzing patterns in images. This paper focuses on choosing iris as a biometric for identification since it is unique of a person and it remains unchanged over many years (throughout the life of a person). CASIA v1 database has been used in the studies of PCA for personal identification. PCA gives 85% accuracy by using Euclidean distance as a classifier.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Principal Component Analysis (pca) False Acceptance Rate(far) False Rejection Rate (frr) Chinese Academy Of Science Institute Of Automation (casia).