Face recognition using modified singular-value-perturbed principal component analysis

For the past 20 years, both the academic and industrial communities have paid more attention and interest on the research and development of face recognition technologies. Generally, face recognition can be classified into three categories, i.e., holistic, feature-based and hybrid method. The most...

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Main Author:	Haron@Saharon, Umi Sabariah
Format:	Thesis
Published:	2010
Subjects:	T Technology (General)
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id	my-mmu-ep.5223
record_format	uketd_dc
spelling	my-mmu-ep.52232014-02-17T01:47:07Z Face recognition using modified singular-value-perturbed principal component analysis 2010-12 Haron@Saharon, Umi Sabariah T Technology (General) For the past 20 years, both the academic and industrial communities have paid more attention and interest on the research and development of face recognition technologies. Generally, face recognition can be classified into three categories, i.e., holistic, feature-based and hybrid method. The most widely used algorithms for the holistic method are eigenface, Fisherfaces, and ICA. Face recognition techniques has numerous potential in various applications, particularly in information security, law enforcement and surveillance. Yet, a lot of these real-world applications are facing the difficulties in collecting large face samples. Nevertheless, the performance of various holistic methods is strongly affected by the number of training sample for each face and most of them will experience serious performance drop or even fail to work if only one training sample per person is available in the system. Moreover, fewer samples per person means less effort in collecting face samples, and reducing the storage and computational cost. 2010-12 Thesis http://shdl.mmu.edu.my/5223/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters Multimedia University Faculty of Computing & Informatics
institution	Multimedia University
collection	MMU Institutional Repository
topic	T Technology (General)
spellingShingle	T Technology (General) Haron@Saharon, Umi Sabariah Face recognition using modified singular-value-perturbed principal component analysis
description	For the past 20 years, both the academic and industrial communities have paid more attention and interest on the research and development of face recognition technologies. Generally, face recognition can be classified into three categories, i.e., holistic, feature-based and hybrid method. The most widely used algorithms for the holistic method are eigenface, Fisherfaces, and ICA. Face recognition techniques has numerous potential in various applications, particularly in information security, law enforcement and surveillance. Yet, a lot of these real-world applications are facing the difficulties in collecting large face samples. Nevertheless, the performance of various holistic methods is strongly affected by the number of training sample for each face and most of them will experience serious performance drop or even fail to work if only one training sample per person is available in the system. Moreover, fewer samples per person means less effort in collecting face samples, and reducing the storage and computational cost.
format	Thesis
qualification_level	Master's degree
author	Haron@Saharon, Umi Sabariah
author_facet	Haron@Saharon, Umi Sabariah
author_sort	Haron@Saharon, Umi Sabariah
title	Face recognition using modified singular-value-perturbed principal component analysis
title_short	Face recognition using modified singular-value-perturbed principal component analysis
title_full	Face recognition using modified singular-value-perturbed principal component analysis
title_fullStr	Face recognition using modified singular-value-perturbed principal component analysis
title_full_unstemmed	Face recognition using modified singular-value-perturbed principal component analysis
title_sort	face recognition using modified singular-value-perturbed principal component analysis
granting_institution	Multimedia University
granting_department	Faculty of Computing & Informatics
publishDate	2010
_version_	1747829565722460160

Face recognition using modified singular-value-perturbed principal component analysis

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