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Reseach Article

Face Recognition Using Particle Swarm Optimization

Published on April 2012 by P. V. Shinde, B. L. Gunjal, R. G. Ghule
Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
Foundation of Computer Science USA
ETCSIT - Number 2
April 2012
Authors: P. V. Shinde, B. L. Gunjal, R. G. Ghule
a7c21416-c01d-4b79-92dc-db56ab211635

P. V. Shinde, B. L. Gunjal, R. G. Ghule . Face Recognition Using Particle Swarm Optimization. Emerging Trends in Computer Science and Information Technology (ETCSIT2012). ETCSIT, 2 (April 2012), 11-13.

@article{
author = { P. V. Shinde, B. L. Gunjal, R. G. Ghule },
title = { Face Recognition Using Particle Swarm Optimization },
journal = { Emerging Trends in Computer Science and Information Technology (ETCSIT2012) },
issue_date = { April 2012 },
volume = { ETCSIT },
number = { 2 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 11-13 },
numpages = 3,
url = { /proceedings/etcsit/number2/5969-1012/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
%A P. V. Shinde
%A B. L. Gunjal
%A R. G. Ghule
%T Face Recognition Using Particle Swarm Optimization
%J Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
%@ 0975-8887
%V ETCSIT
%N 2
%P 11-13
%D 2012
%I International Journal of Computer Applications
Abstract

Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. The algorithm is applied to coefficients extracted by two feature extraction techniques: the discrete cosine transforms (DCT) and the discrete wavelet transform (DWT). The proposed PSO-based feature selection algorithm is utilized to search the feature space for the optimal feature subset where features are carefully selected according to a well defined discrimination criterion. Evolution is driven by a fitness function defined in terms of maximizing the class separation (scatter index). The classifier performance and the length of selected feature vector are considered for performance evaluation using the ORL face database. Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Discrete Cosine Transform Discrete Wavelet Transform Face Recognition Feature Selection Genetic Algorithm Particle Swarm Optimization