Face detection : A comparison between histogram thresholding and neural networks

Face detection is an important process in many applications such as face recognition, person identification and tracking, and access control. The technique used for face detection depends on how a face is modelled. In this thesis, a face is defined as a skin region and a lips region that meet certai...

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Main Author:	Jamal Ahmad Dargham
Format:	Thesis
Language:	English English
Published:	2008
Subjects:	TK8300-8360 Photoelectronic devices (General)
Online Access:	https://eprints.ums.edu.my/id/eprint/38702/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/38702/2/FULLTEXT.pdf
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spelling	my-ums-ep.387022024-05-17T08:36:11Z Face detection : A comparison between histogram thresholding and neural networks 2008 Jamal Ahmad Dargham TK8300-8360 Photoelectronic devices (General) Face detection is an important process in many applications such as face recognition, person identification and tracking, and access control. The technique used for face detection depends on how a face is modelled. In this thesis, a face is defined as a skin region and a lips region that meet certain geometrical criteria. Thus, the face detection system has three main components: a skin detection module, a lips detection module, and a face verification module. Multi-layer perceptron (MLP) neural networks and histogram thresholding techniques have been used for skin and lips detection. In order to test the face detection system, two databases were created. The images in the first database, called In-house, were taken under controlled environment while those in the second database, called WWW, were collected from the World Wide Web. Only the skin and the lips colour in the normalised RGB colour scheme were used for the skin and lips detection respectively. A new method for obtaining the r, g, and b components of the normalised RGB systems from the R, G, and B components of the RGB system was proposed. It was found out that the proposed method, called maximum intensity normalisation, gives higher percentage of correct skin detection than the conventional rgb colour scheme regardless of the database used or the skin detection method. Two methods were used to find the number of neurons in the hidden layer of the MLP. The first method use binary search between a minimum and a maximum values while the second method use sequential search with a stopping criteria. The effect of scale factor, facial expressions and minor occlusions with glasses on skin, lips and face detection was investigated. It was found out that, as the scale factor increases the percentage skin and lips detection error decreases. However, the percentage decrease in skin and lips detection errors depends on the intensity normalisation, the detection method and the chrominance component used. But the scale factor did not have any effect on the face detection. In general, the facial expression did not have any significant effect on skin detection. However, for lips detection, the laughing expression did give the highest lips detection error followed by smiling expression. Furthermore, the percentage increase in lips detection error as a result of the facial expression depends on the intensity normalisation, the detection method and the chrominance component used. As for face detection, the facial expression has a negative effect on the correct face detection especially at scale factor of 3. Although, the minor occlusion increases the skin detection error it has no significant effect on the performance of face detection. 2008 Thesis https://eprints.ums.edu.my/id/eprint/38702/ https://eprints.ums.edu.my/id/eprint/38702/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/38702/2/FULLTEXT.pdf text en validuser dphil doctoral Universiti Malaysia Sabah Sekolah Kejuruteraan dan Teknologi Maklumat
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	TK8300-8360 Photoelectronic devices (General)
spellingShingle	TK8300-8360 Photoelectronic devices (General) Jamal Ahmad Dargham Face detection : A comparison between histogram thresholding and neural networks
description	Face detection is an important process in many applications such as face recognition, person identification and tracking, and access control. The technique used for face detection depends on how a face is modelled. In this thesis, a face is defined as a skin region and a lips region that meet certain geometrical criteria. Thus, the face detection system has three main components: a skin detection module, a lips detection module, and a face verification module. Multi-layer perceptron (MLP) neural networks and histogram thresholding techniques have been used for skin and lips detection. In order to test the face detection system, two databases were created. The images in the first database, called In-house, were taken under controlled environment while those in the second database, called WWW, were collected from the World Wide Web. Only the skin and the lips colour in the normalised RGB colour scheme were used for the skin and lips detection respectively. A new method for obtaining the r, g, and b components of the normalised RGB systems from the R, G, and B components of the RGB system was proposed. It was found out that the proposed method, called maximum intensity normalisation, gives higher percentage of correct skin detection than the conventional rgb colour scheme regardless of the database used or the skin detection method. Two methods were used to find the number of neurons in the hidden layer of the MLP. The first method use binary search between a minimum and a maximum values while the second method use sequential search with a stopping criteria. The effect of scale factor, facial expressions and minor occlusions with glasses on skin, lips and face detection was investigated. It was found out that, as the scale factor increases the percentage skin and lips detection error decreases. However, the percentage decrease in skin and lips detection errors depends on the intensity normalisation, the detection method and the chrominance component used. But the scale factor did not have any effect on the face detection. In general, the facial expression did not have any significant effect on skin detection. However, for lips detection, the laughing expression did give the highest lips detection error followed by smiling expression. Furthermore, the percentage increase in lips detection error as a result of the facial expression depends on the intensity normalisation, the detection method and the chrominance component used. As for face detection, the facial expression has a negative effect on the correct face detection especially at scale factor of 3. Although, the minor occlusion increases the skin detection error it has no significant effect on the performance of face detection.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Jamal Ahmad Dargham
author_facet	Jamal Ahmad Dargham
author_sort	Jamal Ahmad Dargham
title	Face detection : A comparison between histogram thresholding and neural networks
title_short	Face detection : A comparison between histogram thresholding and neural networks
title_full	Face detection : A comparison between histogram thresholding and neural networks
title_fullStr	Face detection : A comparison between histogram thresholding and neural networks
title_full_unstemmed	Face detection : A comparison between histogram thresholding and neural networks
title_sort	face detection : a comparison between histogram thresholding and neural networks
granting_institution	Universiti Malaysia Sabah
granting_department	Sekolah Kejuruteraan dan Teknologi Maklumat
publishDate	2008
url	https://eprints.ums.edu.my/id/eprint/38702/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/38702/2/FULLTEXT.pdf
_version_	1804890338172600320

Face detection : A comparison between histogram thresholding and neural networks

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