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Optimizing Feature Extraction using PSO-LDA for Face Recognition

Feature extraction is one of important process in face recognition LDA is dimensional reduction techniques that commonly used as feature extraction. Feature extraction by using LDA will produce feature space to extract important information of data. Selecting number of eigenvector which are used as...

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Main Authors: Muda, A. K., Yun-Huoy, C., Ahmad, S.
Format: Article
Language:English
Published: Dynamic Publishers, Inc., USA 2012
Subjects:
Q Science (General)
Online Access:http://eprints.utem.edu.my/id/eprint/4798/1/Paper23.pdf
http://eprints.utem.edu.my/id/eprint/4798/
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spelling my.utem.eprints.47982022-01-14T15:40:59Z http://eprints.utem.edu.my/id/eprint/4798/ Optimizing Feature Extraction using PSO-LDA for Face Recognition Muda, A. K. Yun-Huoy, C. Ahmad, S. Q Science (General) Feature extraction is one of important process in face recognition LDA is dimensional reduction techniques that commonly used as feature extraction. Feature extraction by using LDA will produce feature space to extract important information of data. Selecting number of eigenvector which are used as feature space will not only effect on the computational time, but also effect on the recognition rate. This paper presents analysis number of eigenvector which is potential used as parameter extraction in feature extraction. The main idea of applying PSO in LDA is to search the number of parameter extraction for the optimal feature subset where features are carefully selected according to a well-defined discrimination criterion. Hybridizing PSO and LDA conducted to find the best number of feature space in order to optimize the recognition rate. The experiment conducted by using four databases with different disturbance, i.e. luminance, expression, focus and background, and also random disturbance. The results show that PSO-LDA can obtain the minimum number of parameter extraction which produces the highest recognition rate. Dynamic Publishers, Inc., USA 2012 Article PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/4798/1/Paper23.pdf Muda, A. K. and Yun-Huoy, C. and Ahmad, S. (2012) Optimizing Feature Extraction using PSO-LDA for Face Recognition. Journal of Information Assurance and Security, 7. pp. 222-228. ISSN 1554-1010
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Muda, A. K.
Yun-Huoy, C.
Ahmad, S.
Optimizing Feature Extraction using PSO-LDA for Face Recognition
description Feature extraction is one of important process in face recognition LDA is dimensional reduction techniques that commonly used as feature extraction. Feature extraction by using LDA will produce feature space to extract important information of data. Selecting number of eigenvector which are used as feature space will not only effect on the computational time, but also effect on the recognition rate. This paper presents analysis number of eigenvector which is potential used as parameter extraction in feature extraction. The main idea of applying PSO in LDA is to search the number of parameter extraction for the optimal feature subset where features are carefully selected according to a well-defined discrimination criterion. Hybridizing PSO and LDA conducted to find the best number of feature space in order to optimize the recognition rate. The experiment conducted by using four databases with different disturbance, i.e. luminance, expression, focus and background, and also random disturbance. The results show that PSO-LDA can obtain the minimum number of parameter extraction which produces the highest recognition rate.
format Article
author Muda, A. K.
Yun-Huoy, C.
Ahmad, S.
author_facet Muda, A. K.
Yun-Huoy, C.
Ahmad, S.
author_sort Muda, A. K.
title Optimizing Feature Extraction using PSO-LDA for Face Recognition
title_short Optimizing Feature Extraction using PSO-LDA for Face Recognition
title_full Optimizing Feature Extraction using PSO-LDA for Face Recognition
title_fullStr Optimizing Feature Extraction using PSO-LDA for Face Recognition
title_full_unstemmed Optimizing Feature Extraction using PSO-LDA for Face Recognition
title_sort optimizing feature extraction using pso-lda for face recognition
publisher Dynamic Publishers, Inc., USA
publishDate 2012
url http://eprints.utem.edu.my/id/eprint/4798/1/Paper23.pdf
http://eprints.utem.edu.my/id/eprint/4798/
_version_ 1724077933999423488
score 13.211869

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