Enhancing feature selection for face detection using genetic algorithm
Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In thi...
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| Format: | Conference or Workshop Item |
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2007
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| _version_ | 1796855322129727488 |
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| author | Mohd. Zin, Zalhan Khalid, Marzuki Yusof, Rubiyah |
| author_facet | Mohd. Zin, Zalhan Khalid, Marzuki Yusof, Rubiyah |
| author_sort | Mohd. Zin, Zalhan |
| collection | ePrints |
| description | Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, we show how Genetic Algorithm (GA) can be used, within the Adaboost framework, to find sets of features which can provide better classifiers with a shorter training time. |
| first_indexed | 2024-03-05T18:26:55Z |
| format | Conference or Workshop Item |
| id | utm.eprints-13959 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:26:55Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | utm.eprints-139592017-08-08T08:11:39Z http://eprints.utm.my/13959/ Enhancing feature selection for face detection using genetic algorithm Mohd. Zin, Zalhan Khalid, Marzuki Yusof, Rubiyah TK Electrical engineering. Electronics Nuclear engineering Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, we show how Genetic Algorithm (GA) can be used, within the Adaboost framework, to find sets of features which can provide better classifiers with a shorter training time. 2007 Conference or Workshop Item PeerReviewed Mohd. Zin, Zalhan and Khalid, Marzuki and Yusof, Rubiyah (2007) Enhancing feature selection for face detection using genetic algorithm. In: Malaysia-Japan International Symposium on Advanced Technology 2007 (MJISAT2007), 2007, Kuala Lumpur. |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd. Zin, Zalhan Khalid, Marzuki Yusof, Rubiyah Enhancing feature selection for face detection using genetic algorithm |
| title | Enhancing feature selection for face detection using genetic algorithm |
| title_full | Enhancing feature selection for face detection using genetic algorithm |
| title_fullStr | Enhancing feature selection for face detection using genetic algorithm |
| title_full_unstemmed | Enhancing feature selection for face detection using genetic algorithm |
| title_short | Enhancing feature selection for face detection using genetic algorithm |
| title_sort | enhancing feature selection for face detection using genetic algorithm |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT mohdzinzalhan enhancingfeatureselectionforfacedetectionusinggeneticalgorithm AT khalidmarzuki enhancingfeatureselectionforfacedetectionusinggeneticalgorithm AT yusofrubiyah enhancingfeatureselectionforfacedetectionusinggeneticalgorithm |