A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications

Chou, KP; Prasad, M; Yang, J; Su, SY; Tao, X; Saxena, A; Lin, WC; Lin, CT

A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications

Chou, KP Prasad, M

Yang, J Su, SY Tao, X Saxena, A Lin, WC Lin, CT

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2020, pp. 1-23
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Chou, KP
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.contributor.author	Yang, J
dc.contributor.author	Su, SY
dc.contributor.author	Tao, X
dc.contributor.author	Saxena, A
dc.contributor.author	Lin, WC
dc.contributor.author	Lin, CT
dc.date.accessioned	2020-11-03T20:07:43Z
dc.date.available	2020-11-03T20:07:43Z
dc.date.issued	2020-01-01
dc.identifier.citation	Multimedia Tools and Applications, 2020, pp. 1-23
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/143735
dc.description.abstract	© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-020-09216-7
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Multimedia Tools and Applications]. The final authenticated version is available online at: https://link.springer.com/article/10.1007/s11042-020-09216-7]”
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: https://dx.doi.org/10.1007/s11042-020-09216-7.	en_US
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Software Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications
dc.type	Journal Article
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2020-11-03T20:07:38Z
pubs.publication-status	Published

Abstract:

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.

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http://hdl.handle.net/10453/143735