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Enhanced feature selections of Adaboost training for face detection using genetic algorithm

A wide variety of face detection techniques have been proposed over the past decades. Generally, a large number of features are required to be selected for training purposes. Often some of these features are irrelevant and do not contribute directly to the face detection techniques. This creates unn...

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Main Author:	Mohd. Zin, Zalhan
Format:	Thesis
Language:	English
Published:	2007
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/6427/1/ZalhanMohdZinMFKE2007.pdf http://eprints.utm.my/id/eprint/6427/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62247
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id	my.utm.6427
record_format	eprints
spelling	my.utm.64272018-08-26T04:49:39Z http://eprints.utm.my/id/eprint/6427/ Enhanced feature selections of Adaboost training for face detection using genetic algorithm Mohd. Zin, Zalhan TK Electrical engineering. Electronics Nuclear engineering A wide variety of face detection techniques have been proposed over the past decades. Generally, a large number of features are required to be selected for training purposes. Often some of these features are irrelevant and do not contribute directly to the face detection techniques. This creates unnecessary computation and usage of large memory space. In this thesis, features search space has been enlarged by enriching it with seven additional new feature types. With these new feature types and larger search space, Genetic Algorithm (GA) is used within the Adaboost framework, to find sets of features which can provide a better cascade of boosted classifiers with a shorter training time. This technique is referred to as GABoost for this training part of a face detection system. The GA carries out an evolutionary search to select features which results in a higher number of feature types and sets selected in less time. Experiments on a set of images from BioID face database proved that by using GA to search on a large number of feature types and sets, the proposed technique referred to as GABoost was able to obtain the cascades of boosted classifiers for the face detection system that can give higher detection rates (94.25%), lower false positive rates (55.94%) and less training time (6.68 hours). 2007-08 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/6427/1/ZalhanMohdZinMFKE2007.pdf Mohd. Zin, Zalhan (2007) Enhanced feature selections of Adaboost training for face detection using genetic algorithm. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62247
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mohd. Zin, Zalhan Enhanced feature selections of Adaboost training for face detection using genetic algorithm
description	A wide variety of face detection techniques have been proposed over the past decades. Generally, a large number of features are required to be selected for training purposes. Often some of these features are irrelevant and do not contribute directly to the face detection techniques. This creates unnecessary computation and usage of large memory space. In this thesis, features search space has been enlarged by enriching it with seven additional new feature types. With these new feature types and larger search space, Genetic Algorithm (GA) is used within the Adaboost framework, to find sets of features which can provide a better cascade of boosted classifiers with a shorter training time. This technique is referred to as GABoost for this training part of a face detection system. The GA carries out an evolutionary search to select features which results in a higher number of feature types and sets selected in less time. Experiments on a set of images from BioID face database proved that by using GA to search on a large number of feature types and sets, the proposed technique referred to as GABoost was able to obtain the cascades of boosted classifiers for the face detection system that can give higher detection rates (94.25%), lower false positive rates (55.94%) and less training time (6.68 hours).
format	Thesis
author	Mohd. Zin, Zalhan
author_facet	Mohd. Zin, Zalhan
author_sort	Mohd. Zin, Zalhan
title	Enhanced feature selections of Adaboost training for face detection using genetic algorithm
title_short	Enhanced feature selections of Adaboost training for face detection using genetic algorithm
title_full	Enhanced feature selections of Adaboost training for face detection using genetic algorithm
title_fullStr	Enhanced feature selections of Adaboost training for face detection using genetic algorithm
title_full_unstemmed	Enhanced feature selections of Adaboost training for face detection using genetic algorithm
title_sort	enhanced feature selections of adaboost training for face detection using genetic algorithm
publishDate	2007
url	http://eprints.utm.my/id/eprint/6427/1/ZalhanMohdZinMFKE2007.pdf http://eprints.utm.my/id/eprint/6427/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62247
_version_	1643644555399004160
score	13.211869

Enhanced feature selections of Adaboost training for face detection using genetic algorithm

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