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Reseach Article

Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition

Published on September 2015 by Aarati Venugopal Kartha, Vinayak Ashok Bharadi
International Conference and Workshop on Communication, Computing and Virtualization
Foundation of Computer Science USA
ICWCCV2015 - Number 1
September 2015
Authors: Aarati Venugopal Kartha, Vinayak Ashok Bharadi
ac131ab1-6d60-4c86-bb7b-6cecc507ea0a

Aarati Venugopal Kartha, Vinayak Ashok Bharadi . Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition. International Conference and Workshop on Communication, Computing and Virtualization. ICWCCV2015, 1 (September 2015), 0-0.

@article{
author = { Aarati Venugopal Kartha, Vinayak Ashok Bharadi },
title = { Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition },
journal = { International Conference and Workshop on Communication, Computing and Virtualization },
issue_date = { September 2015 },
volume = { ICWCCV2015 },
number = { 1 },
month = { September },
year = { 2015 },
issn = 2249-0868,
pages = { 0-0 },
numpages = 1,
url = { /proceedings/icwccv2015/number1/790-1559/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Communication, Computing and Virtualization
%A Aarati Venugopal Kartha
%A Vinayak Ashok Bharadi
%T Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition
%J International Conference and Workshop on Communication, Computing and Virtualization
%@ 2249-0868
%V ICWCCV2015
%N 1
%P 0-0
%D 2015
%I International Journal of Applied Information Systems
Abstract

Biometric authentication systems have become ubiquitous with the increasing number of surveillance cameras that are deployed almost everywhere, the use of biometric attendance systems and also its large scale use in forensic laboratories. Hyperspectral images are used widely in biometric research because of the immense amount of unique data they generate has proved to be helpful in solving the drawbacks of existing biometric systems. The main focus of the research was to use hyperspectral face images having 33 bands for face recognition using Fast Walsh transform coefficients. Face is a biometric trait which requires low user co-operation and provides better accuracy which makes it preferable over other biometric traits. With the use of hyperspectral face images, the accuracy rate was found to be improved. However the main drawback of these Hyperspectral images was that they generated large amount of redundant data and hence row, column and diagonal mean were computed instead of using the entire image so as reduce the memory and storage constraints. Orthogonal transforms such as Fast Walsh transform was used for texture feature extraction to generate the coefficients for the row, column and diagonal mean vectors. The extracted feature vectors are then subjected to intra class and inter class testing using Euclidian distance measure. The performance of the system was analysed.

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Index Terms

Computer Science
Information Sciences

Keywords

Biometrics Hyperspectral Images Face Recognition Fast Walsh Transform (FWHT).