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Recognising faces in unseen modes : a tensor based approach

Rana, Santu, Liu, Wanquan, Lazarescu, Mihai and Venkatesh, Svetha 2008, Recognising faces in unseen modes : a tensor based approach, in CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Washington, D. C., pp. 1-8.

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Title Recognising faces in unseen modes : a tensor based approach
Author(s) Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Liu, Wanquan
Lazarescu, Mihai
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name IEEE Conference on Computer Vision and Pattern Recognition (26th : 2008 : Anchorage, Alaska)
Conference location Anchorage, Alaska
Conference dates 23-28 Jun. 2008
Title of proceedings CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition
Editor(s) [Unknown]
Publication date 2008
Conference series IEEE Conference on Computer Vision and Pattern Recognition
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) Australia
biometrics
computational efficiency
face recognition
independent component analysis
lighting
principal component analysis
tensile stress
testing
vectors
Summary This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost.
ISBN 1424422426
9781424422425
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044579

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.