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Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis

by Akanksha Joshi, Abhishek Gangwar, Zia Saquib
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 9
Year of Publication: 2012
Authors: Akanksha Joshi, Abhishek Gangwar, Zia Saquib
10.5120/4996-7270

Akanksha Joshi, Abhishek Gangwar, Zia Saquib . Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis. International Journal of Computer Applications. 40, 9 ( February 2012), 35-39. DOI=10.5120/4996-7270

@article{ 10.5120/4996-7270,
author = { Akanksha Joshi, Abhishek Gangwar, Zia Saquib },
title = { Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 9 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number9/4996-7270/ },
doi = { 10.5120/4996-7270 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:39.771427+05:30
%A Akanksha Joshi
%A Abhishek Gangwar
%A Zia Saquib
%T Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 9
%P 35-39
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Iris recognition is seen as a highly reliable biometric technology. The performance of iris recognition is severely impacted when encountering poor quality images. The selection of the features subset and the classification is an important issue for iris biometrics. Here, we explored the contribution of collarette region in identifying a person. We applied five level haar wavelet decomposition for collarette region feature extraction and used the second level approximation coefficients combined with fifth level vertical coefficients for better accuracy. Then, we applied Direct Linear Discriminant Analysis (DLDA) to produce discriminative low-dimensional feature vectors. The approach is evaluated on CASIA Iris Interval database and we achieved 98.96% accuracy using the collarette region which is a significant improvement in the performance of recognition.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Collarette Feature Extraction Wavelet Transform.

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