SVM based ASM for facial landmarks location

Du, C; Wu, Q; Yang, J; Wu, Z

SVM based ASM for facial landmarks location

Du, C Wu, Q

Yang, J Wu, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008, 2008, pp. 321 - 326
Issue Date:: 2008-09-22

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Field	Value	Language
dc.contributor.author	Du, C	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Wu, Z	en_US
dc.date.issued	2008-09-22	en_US
dc.identifier.citation	Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008, 2008, pp. 321 - 326	en_US
dc.identifier.isbn	9781424423583	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11943
dc.description.abstract	Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark follow a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a new method support vector machine (SVM) based ASM (SVMBASM) is proposed. It approaches the finding task as a small sample size classification problem, and uses SVM classifier to deal with this problem. Moreover, considering 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 adopted. Performance evaluation on SJTU face database show that the proposed SVMBASM outperforms the original ASM in terms of the average error as well as the average frequency of convergence. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008	en_US
dc.relation.isbasedon	10.1109/CIT.2008.4594695	en_US
dc.title	SVM based ASM for facial landmarks location	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Australia	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
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	open_access
pubs.publication-status	Published	en_US

Abstract:

Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark follow a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a new method support vector machine (SVM) based ASM (SVMBASM) is proposed. It approaches the finding task as a small sample size classification problem, and uses SVM classifier to deal with this problem. Moreover, considering 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 adopted. Performance evaluation on SJTU face database show that the proposed SVMBASM outperforms the original ASM in terms of the average error as well as the average frequency of convergence. © 2008 IEEE.

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http://hdl.handle.net/10453/11943