Part-Based And Multispace Random Mapping For Face Recognition

In this thesis, we studied several part-based approaches for face recognition. Our objective is to reduce the dimensionality of the raw image while retaining as many salient features as possible. Non-Negative Matrix Factorization (NMF) is recently a proposed method to obtain a part-based linear repr...

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Bibliographic Details
Main Author:	Neo, Han Foon
Format:	Thesis
Published:	2005
Subjects:	TA Engineering (General) Civil engineering (General)
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id	my-mmu-ep.883
record_format	uketd_dc
spelling	my-mmu-ep.8832021-09-21T08:04:16Z Part-Based And Multispace Random Mapping For Face Recognition 2005-09 Neo, Han Foon TA Engineering (General). Civil engineering (General) In this thesis, we studied several part-based approaches for face recognition. Our objective is to reduce the dimensionality of the raw image while retaining as many salient features as possible. Non-Negative Matrix Factorization (NMF) is recently a proposed method to obtain a part-based linear representation of facial image. However, the bases learned by NMF do not display perfectly the local characteristics as there are still some non-zero weight values in the features. These values appear as noise and contribute to the degradation of the recognition performance. In this thesis, we have proposed a novel part-based feature extractor based on NMF. 2005-09 Thesis http://shdl.mmu.edu.my/883/ https://proxyvlib.mmu.edu.my/login?url=http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Information Science and Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Neo, Han Foon Part-Based And Multispace Random Mapping For Face Recognition
description	In this thesis, we studied several part-based approaches for face recognition. Our objective is to reduce the dimensionality of the raw image while retaining as many salient features as possible. Non-Negative Matrix Factorization (NMF) is recently a proposed method to obtain a part-based linear representation of facial image. However, the bases learned by NMF do not display perfectly the local characteristics as there are still some non-zero weight values in the features. These values appear as noise and contribute to the degradation of the recognition performance. In this thesis, we have proposed a novel part-based feature extractor based on NMF.
format	Thesis
qualification_level	Master's degree
author	Neo, Han Foon
author_facet	Neo, Han Foon
author_sort	Neo, Han Foon
title	Part-Based And Multispace Random Mapping For Face Recognition
title_short	Part-Based And Multispace Random Mapping For Face Recognition
title_full	Part-Based And Multispace Random Mapping For Face Recognition
title_fullStr	Part-Based And Multispace Random Mapping For Face Recognition
title_full_unstemmed	Part-Based And Multispace Random Mapping For Face Recognition
title_sort	part-based and multispace random mapping for face recognition
granting_institution	Multimedia University
granting_department	Faculty of Information Science and Technology
publishDate	2005
_version_	1776101416327184384

Part-Based And Multispace Random Mapping For Face Recognition

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