Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition

Tsai, PC; Hintz, TB; Tran, TP; Jan, T

Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition

Tsai, PC Hintz, TB Tran, TP Jan, T

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Publisher:: IN-TECH
Publication Type:: Chapter
Citation:: Recent Advances in Face Recognition, 2008, 1, pp. 161 - 182
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Tsai, PC	en_US
dc.contributor.author	Hintz, TB	en_US
dc.contributor.author	Tran, TP	en_US
dc.contributor.author	Jan, T	en_US
dc.contributor.editor	Delac, K	en_US
dc.contributor.editor	Grgic, M	en_US
dc.contributor.editor	Bartlett, MS	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Recent Advances in Face Recognition, 2008, 1, pp. 161 - 182	en_US
dc.identifier.isbn	978-953-7619-34-3	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17525
dc.description.abstract	Facial analysis and recognition have received substential attention from researchers in biometrics, pattern recognition, and computer vision communities. They have a large number of applications, such as security, communication, and entertainment. Although a great deal of efforts has been devoted to automated face recognition systems, it still remains a challenging uncertainty problem. This is because human facial appearance has potentially of very large intra-subject variations of head pose, illumination, facial expression, occlusion due to other objects or accessories, facial hair and aging. These misleading variations may cause classifiers to degrade generalization performance.	en_US
dc.publisher	IN-TECH	en_US
dc.relation.ispartof	Recent Advances in Face Recognition	en_US
dc.title	Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition	en_US
dc.type	Chapter
utslib.location	Vienna, Austria	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.citation.edition	1	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.edition	1	en_US
pubs.place-of-publication	Vienna, Austria	en_US

Abstract:

Facial analysis and recognition have received substential attention from researchers in biometrics, pattern recognition, and computer vision communities. They have a large number of applications, such as security, communication, and entertainment. Although a great deal of efforts has been devoted to automated face recognition systems, it still remains a challenging uncertainty problem. This is because human facial appearance has potentially of very large intra-subject variations of head pose, illumination, facial expression, occlusion due to other objects or accessories, facial hair and aging. These misleading variations may cause classifiers to degrade generalization performance.

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http://hdl.handle.net/10453/17525