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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Dynamic Publishers, Inc., USA
2012
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/4798/ http://eprints.utem.edu.my/id/eprint/4798/1/Paper23.pdf |
| _version_ | 1848887064321327104 |
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| author | Muda, A. K. Yun-Huoy, C. Ahmad, S. |
| author_facet | Muda, A. K. Yun-Huoy, C. Ahmad, S. |
| author_sort | Muda, A. K. |
| building | UTeM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:48:27Z |
| format | Article |
| id | utem-4798 |
| institution | Universiti Teknikal Malaysia Melaka |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:48:27Z |
| publishDate | 2012 |
| publisher | Dynamic Publishers, Inc., USA |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utem-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 |
| spellingShingle | Q Science (General) Muda, A. K. Yun-Huoy, C. Ahmad, S. Optimizing Feature Extraction using PSO-LDA for Face Recognition |
| title | 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_short | Optimizing Feature Extraction using PSO-LDA for Face Recognition |
| title_sort | optimizing feature extraction using pso-lda for face recognition |
| topic | Q Science (General) |
| url | http://eprints.utem.edu.my/id/eprint/4798/ http://eprints.utem.edu.my/id/eprint/4798/1/Paper23.pdf |