Face recognition using nonparametric-weighted Fisherfaces

Li, DL; Prasad, M; Hsu, SC; Hong, CT; Lin, CT

Face recognition using nonparametric-weighted Fisherfaces

Li, DL Prasad, M

Hsu, SC Hong, CT Lin, CT

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Publication Type:: Journal Article
Citation:: Eurasip Journal on Advances in Signal Processing, 2012, 2012 (1)
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Li, DL	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.contributor.author	Hsu, SC	en_US
dc.contributor.author	Hong, CT	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Eurasip Journal on Advances in Signal Processing, 2012, 2012 (1)	en_US
dc.identifier.issn	1687-6172	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115045
dc.description.abstract	This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases. © 2012 Li et al.	en_US
dc.relation.ispartof	Eurasip Journal on Advances in Signal Processing	en_US
dc.relation.isbasedon	10.1186/1687-6180-2012-92	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Face recognition using nonparametric-weighted Fisherfaces	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	2012	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	2012	en_US

Abstract:

This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases. © 2012 Li et al.

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http://hdl.handle.net/10453/115045