Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost)
Various face detection techniques has been proposed over the past decade. 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. Th...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2007
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| Online Access: | http://eprints.utm.my/7338/1/RubiyahYusof2007__Enhanced_feature_selections_of_Adaboost_training.pdf |
| _version_ | 1825910024249016320 |
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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 | Various face detection techniques has been proposed over the past decade. 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. The technique is referred as GABoost for our face detection system. The GA carries out an evolutionary search over possible features search space which results in a higher number of feature types and sets selected in lesser time. Experiments on a set of images from BioID database proved that by using GA to search on large number of feature types and sets, GABoost is able to obtain cascade of boosted classifiers for a face detection system that can give higher detection rates, lower false positive rates and less training time.
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| first_indexed | 2024-03-05T18:10:54Z |
| format | Conference or Workshop Item |
| id | utm.eprints-7338 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:10:54Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | utm.eprints-73382010-06-01T15:51:10Z http://eprints.utm.my/7338/ Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) Mohd. Zin, Zalhan Khalid, Marzuki Yusof, Rubiyah TK Electrical engineering. Electronics Nuclear engineering Various face detection techniques has been proposed over the past decade. 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. The technique is referred as GABoost for our face detection system. The GA carries out an evolutionary search over possible features search space which results in a higher number of feature types and sets selected in lesser time. Experiments on a set of images from BioID database proved that by using GA to search on large number of feature types and sets, GABoost is able to obtain cascade of boosted classifiers for a face detection system that can give higher detection rates, lower false positive rates and less training time. 2007 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/7338/1/RubiyahYusof2007__Enhanced_feature_selections_of_Adaboost_training.pdf Mohd. Zin, Zalhan and Khalid, Marzuki and Yusof, Rubiyah (2007) Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost). In: Proceeding of the 3rd IASTED International Conference on Computational Intelligent 2007, 02 - 04 July 2007, Banff, Alberta, Canada. http://portal.acm.org/citation.cfm?id=1672050 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd. Zin, Zalhan Khalid, Marzuki Yusof, Rubiyah Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) |
| title | Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) |
| title_full | Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) |
| title_fullStr | Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) |
| title_full_unstemmed | Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) |
| title_short | Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) |
| title_sort | enhanced feature selections of adaboost training for face detection using genetic algorithm gaboost |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/7338/1/RubiyahYusof2007__Enhanced_feature_selections_of_Adaboost_training.pdf |
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