Kernel Discriminant Embedding in face recognition

Authors:
Pang Ying Han;Andrew Teoh Beng Jin;Ann Toh Kar
Affiliations:
Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;Yonsei University, Seoul, South Korea and Predictive Intelligence Research Cluste, Sunway University, Bandar Sunway, 46150 P.J. Selangor, Malaysia;Yonsei University, Seoul, South Korea
Venue:
Journal of Visual Communication and Image Representation
Year:
2011

Citing 12
Cited 1

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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

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Eigenfaces for recognition

Journal of Cognitive Neuroscience
A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding

ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Locality sensitive discriminant analysis

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Neighbourhood preserving discriminant embedding in face recognition

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Improving kernel Fisher discriminant analysis for face recognition

IEEE Transactions on Circuits and Systems for Video Technology

Robust gait recognition via discriminative set matching

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Abstract

In this paper, we present a novel and effective feature extraction technique for face recognition. The proposed technique incorporates a kernel trick with Graph Embedding and the Fisher's criterion which we call it as Kernel Discriminant Embedding (KDE). The proposed technique projects the original face samples onto a low dimensional subspace such that the within-class face samples are minimized and the between-class face samples are maximized based on Fisher's criterion. The implementation of kernel trick and Graph Embedding criterion on the proposed technique reveals the underlying structure of data. Our experimental results on face recognition using ORL, FRGC and FERET databases validate the effectiveness of KDE for face feature extraction.