Wavelet and moment invariants based features selection using voronoi diagram for face recognition

Face recognition is a biometric authentication system for human security and personal identification that has become a field of interest in pattern recognition and computer vision societies in recent years as it has become increasingly important and commonly used for legal and personal identificatio...

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Main Author:	Meethongjan, Kittikhun
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/37031/5/KittikhunMeethongjanPFSKSM2013.pdf
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id	my-utm-ep.37031
record_format	uketd_dc
spelling	my-utm-ep.370312017-07-16T07:20:22Z Wavelet and moment invariants based features selection using voronoi diagram for face recognition 2013-06 Meethongjan, Kittikhun TK Electrical engineering. Electronics Nuclear engineering Face recognition is a biometric authentication system for human security and personal identification that has become a field of interest in pattern recognition and computer vision societies in recent years as it has become increasingly important and commonly used for legal and personal identification in various fields such as visa information system, access control and multimedia search engines. However, distinct illumination, pose and blurring of facial images have become a big challenge in finding important facial features and facial representation in these fields. Therefore, this thesis proposes a facial recognition framework based on multi-feature selection approach. The framework in this thesis consists of eight stages: face preprocessing, segmentation, detection, cropping, transformation, extraction, classification and verification. The experiments were performed on gray scale frontal facial image with 750 images applied from three different standard facial databases namely BioID, ORL and Yale. In face segmentation, detection and cropping stages, Voronoi Diagram and Delaunay Triangulation methods have been applied. Wavelet transform and moment invariants methods have been used to extract facial image features. All features were fed into Radial Basis Function neural network for classification and verification purposes. The results show that a recognition accuracy rate of more than 92% has been achieved as compared to other proposed methods. Therefore, the framework in this thesis would be beneficial for the field of face authentication or verification due to its robustness and invariance to pose, illumination, and expression. 2013-06 Thesis http://eprints.utm.my/id/eprint/37031/ http://eprints.utm.my/id/eprint/37031/5/KittikhunMeethongjanPFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70022?site_name=Restricted Repository phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Meethongjan, Kittikhun Wavelet and moment invariants based features selection using voronoi diagram for face recognition
description	Face recognition is a biometric authentication system for human security and personal identification that has become a field of interest in pattern recognition and computer vision societies in recent years as it has become increasingly important and commonly used for legal and personal identification in various fields such as visa information system, access control and multimedia search engines. However, distinct illumination, pose and blurring of facial images have become a big challenge in finding important facial features and facial representation in these fields. Therefore, this thesis proposes a facial recognition framework based on multi-feature selection approach. The framework in this thesis consists of eight stages: face preprocessing, segmentation, detection, cropping, transformation, extraction, classification and verification. The experiments were performed on gray scale frontal facial image with 750 images applied from three different standard facial databases namely BioID, ORL and Yale. In face segmentation, detection and cropping stages, Voronoi Diagram and Delaunay Triangulation methods have been applied. Wavelet transform and moment invariants methods have been used to extract facial image features. All features were fed into Radial Basis Function neural network for classification and verification purposes. The results show that a recognition accuracy rate of more than 92% has been achieved as compared to other proposed methods. Therefore, the framework in this thesis would be beneficial for the field of face authentication or verification due to its robustness and invariance to pose, illumination, and expression.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Meethongjan, Kittikhun
author_facet	Meethongjan, Kittikhun
author_sort	Meethongjan, Kittikhun
title	Wavelet and moment invariants based features selection using voronoi diagram for face recognition
title_short	Wavelet and moment invariants based features selection using voronoi diagram for face recognition
title_full	Wavelet and moment invariants based features selection using voronoi diagram for face recognition
title_fullStr	Wavelet and moment invariants based features selection using voronoi diagram for face recognition
title_full_unstemmed	Wavelet and moment invariants based features selection using voronoi diagram for face recognition
title_sort	wavelet and moment invariants based features selection using voronoi diagram for face recognition
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/37031/5/KittikhunMeethongjanPFSKSM2013.pdf
_version_	1747816494214938624

Wavelet and moment invariants based features selection using voronoi diagram for face recognition

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