Spatio-temporal framework and algorithms for video-based face recognition

The objective of this research is to devise algorithms for video-based face recognition based on spatio-temporal manifolds. The primary focus is to better represent nonlinear face patterns on a data manifold, while incorporating spatio-temporal information that is inherent in videos. In addition, a...

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Main Author:	See, John Su Yang
Format:	Thesis
Published:	2014
Subjects:	TA Engineering (General) Civil engineering (General)
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id	my-mmu-ep.5988
record_format	uketd_dc
spelling	my-mmu-ep.59882015-06-30T09:06:03Z Spatio-temporal framework and algorithms for video-based face recognition 2014-05 See, John Su Yang TA Engineering (General). Civil engineering (General) TA1501-1820 Applied optics. Photonics The objective of this research is to devise algorithms for video-based face recognition based on spatio-temporal manifolds. The primary focus is to better represent nonlinear face patterns on a data manifold, while incorporating spatio-temporal information that is inherent in videos. In addition, a systematic approach to evaluate sampling is also crucial as current experimental setups are inadequate to provide balanced evaluation. These goals are inspired by motivations from computer vision, psychology and cognitive neuroscience. 2014-05 Thesis http://shdl.mmu.edu.my/5988/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php phd doctoral Multimedia University Faculty of Computing and Informatics
institution	Multimedia University
collection	MMU Institutional Repository
topic	TA Engineering (General) Civil engineering (General) TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) TA Engineering (General) Civil engineering (General) See, John Su Yang Spatio-temporal framework and algorithms for video-based face recognition
description	The objective of this research is to devise algorithms for video-based face recognition based on spatio-temporal manifolds. The primary focus is to better represent nonlinear face patterns on a data manifold, while incorporating spatio-temporal information that is inherent in videos. In addition, a systematic approach to evaluate sampling is also crucial as current experimental setups are inadequate to provide balanced evaluation. These goals are inspired by motivations from computer vision, psychology and cognitive neuroscience.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	See, John Su Yang
author_facet	See, John Su Yang
author_sort	See, John Su Yang
title	Spatio-temporal framework and algorithms for video-based face recognition
title_short	Spatio-temporal framework and algorithms for video-based face recognition
title_full	Spatio-temporal framework and algorithms for video-based face recognition
title_fullStr	Spatio-temporal framework and algorithms for video-based face recognition
title_full_unstemmed	Spatio-temporal framework and algorithms for video-based face recognition
title_sort	spatio-temporal framework and algorithms for video-based face recognition
granting_institution	Multimedia University
granting_department	Faculty of Computing and Informatics
publishDate	2014
_version_	1747829603351658496

Spatio-temporal framework and algorithms for video-based face recognition

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