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Face recognition using kernel ridge regression

An, Senjian, Liu, Wanquan and Venkatesh, Svetha 2007, Face recognition using kernel ridge regression, in CVPR 2007 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, [Piscataway, N.J.], pp. [1]-[7], doi: 10.1109/CVPR.2007.383105.

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Title Face recognition using kernel ridge regression
Author(s) An, Senjian
Liu, Wanquan
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Computer Vision and Pattern Recognition. Conference (2007 : Minneapolis, Minn.)
Conference location Minneapolis, Minn.
Conference dates 17-22 Jun. 2007
Title of proceedings CVPR 2007 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Editor(s) [Unknown]
Publication date 2007
Conference series Computer Vision and Pattern Recognition. Conference
Start page [1]
End page [7]
Total pages 7
Publisher IEEE
Place of publication [Piscataway, N.J.]
Keyword(s) computer vision
face recognition
image databases
image recognition
kernel
linear discriminant analysis
principal component analysis
supervised learning
target recognition
image recognition
Summary In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem of face recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisherfaces and Laplacianfaces) and also performs comparably with the recently developed Orthogonal Laplacianfaces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9781424411801
1424411807
Language eng
DOI 10.1109/CVPR.2007.383105
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 ©2007, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044591

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.