Locating facial landmarks by support vector machine-based active shape model

Du, C; Yang, J; Wu, Q; He, X

Locating facial landmarks by support vector machine-based active shape model

Du, C Yang, J Wu, Q

He, X

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Publication Type:: Journal Article
Citation:: International Journal of Intelligent Systems Technologies and Applications, 2011, 10 (2), pp. 151 - 170
Issue Date:: 2011-03-01

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Field	Value	Language
dc.contributor.author	Du, C	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2011-03-01	en_US
dc.identifier.citation	International Journal of Intelligent Systems Technologies and Applications, 2011, 10 (2), pp. 151 - 170	en_US
dc.identifier.issn	1740-8865	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18265
dc.identifier.uri	http://hdl.handle.net/10453/34152
dc.description.abstract	Active shape model (ASM) plays an important role in face research such as face recognition, pose estimation and gaze estimation. A crucial step of the common ASM is finding a new position for each facial landmark at each iteration. Mahalanobis distance minimisation is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark following a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a novel method support vector machine-based active shape model (SVMBASM) is proposed for this task. It approaches the finding task as a small sample size classification problem. Moreover, considering the poor classification performance caused by the imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is further proposed. Performance evaluation on Shanghai Jiao Tong University face database shows that the proposed SVMBASM outperforms the original ASM in terms of the average error and average frequency of convergence. © 2011 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Intelligent Systems Technologies and Applications	en_US
dc.relation.isbasedon	10.1504/IJISTA.2011.039017	en_US
dc.title	Locating facial landmarks by support vector machine-based active shape model	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	10	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0906 Electrical and Electronic Engineering	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/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

Active shape model (ASM) plays an important role in face research such as face recognition, pose estimation and gaze estimation. A crucial step of the common ASM is finding a new position for each facial landmark at each iteration. Mahalanobis distance minimisation is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark following a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a novel method support vector machine-based active shape model (SVMBASM) is proposed for this task. It approaches the finding task as a small sample size classification problem. Moreover, considering the poor classification performance caused by the imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is further proposed. Performance evaluation on Shanghai Jiao Tong University face database shows that the proposed SVMBASM outperforms the original ASM in terms of the average error and average frequency of convergence. © 2011 Inderscience Enterprises Ltd.

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http://hdl.handle.net/10453/18265