Local binary pattern on hexagonal structure for face matching

He, X; Hintz, T; Li, J; Zhang, H; Wu, Q; Jia, W

Local binary pattern on hexagonal structure for face matching

He, X

Hintz, T Li, J Zhang, H Wu, Q

Jia, W

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007, 2007, pp. 455 - 460
Issue Date:: 2007-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (426.34 kB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Hintz, T	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007, 2007, pp. 455 - 460	en_US
dc.identifier.isbn	9781601320438	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6839
dc.description.abstract	Principal Components Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA), have been widely used for 2D face recognition. Local Binary Pattern (LBP), however, provides a simpler and more effective way to represent faces. With LBP, face image is divided into small regions from which LBP histograms are extracted and concatenated into a single and global feature histogram representing the face image. The recognition is performed using Chi square and other commonly used dissimilarity measures. In this paper, we construct LBP codes together with three dissimilarity measures on hexagonal structure. We show that LBPs defined on hexagonal structure will lead to a faster and more accurate scheme for face recognition.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0451666
dc.relation.ispartof	Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007	en_US
dc.title	Local binary pattern on hexagonal structure for face matching	en_US
dc.type	Conference Proceeding
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	Las Vegas, USA	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 - 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	open_access
pubs.publication-status	Published	en_US

Abstract:

Principal Components Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA), have been widely used for 2D face recognition. Local Binary Pattern (LBP), however, provides a simpler and more effective way to represent faces. With LBP, face image is divided into small regions from which LBP histograms are extracted and concatenated into a single and global feature histogram representing the face image. The recognition is performed using Chi square and other commonly used dissimilarity measures. In this paper, we construct LBP codes together with three dissimilarity measures on hexagonal structure. We show that LBPs defined on hexagonal structure will lead to a faster and more accurate scheme for face recognition.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/6839