A comprehensive survey on Pose-Invariant Face Recognition

Ding, C; Tao, D

A comprehensive survey on Pose-Invariant Face Recognition

Ding, C Tao, D

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Publication Type:: Journal Article
Citation:: ACM Transactions on Intelligent Systems and Technology, 2016, 7 (3)
Issue Date:: 2016-02-01

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Field	Value	Language
dc.contributor.author	Ding, C	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2016-02-01	en_US
dc.identifier.citation	ACM Transactions on Intelligent Systems and Technology, 2016, 7 (3)	en_US
dc.identifier.issn	2157-6904	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43297
dc.description.abstract	© 2016 ACM. The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.	en_US
dc.relation.ispartof	ACM Transactions on Intelligent Systems and Technology	en_US
dc.relation.isbasedon	10.1145/2845089	en_US
dc.title	A comprehensive survey on Pose-Invariant Face Recognition	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	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
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2016 ACM. The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.

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http://hdl.handle.net/10453/43297