Kernel Discriminant Embedding in face recognition

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 orig...

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Main Authors: Han, Pang Ying, Jin, Andrew Teoh Beng, Toh Kar, Ann
Format: Article
Published: 2011
Subjects:
Online Access:http://shdl.mmu.edu.my/3340/
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author Han, Pang Ying
Jin, Andrew Teoh Beng
Toh Kar, Ann
author_facet Han, Pang Ying
Jin, Andrew Teoh Beng
Toh Kar, Ann
author_sort Han, Pang Ying
building MMU Institutional Repository
collection Online Access
description 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. (C) 2011 Elsevier Inc. All rights reserved.
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spelling mmu-33402012-01-09T04:01:09Z http://shdl.mmu.edu.my/3340/ Kernel Discriminant Embedding in face recognition Han, Pang Ying Jin, Andrew Teoh Beng Toh Kar, Ann QA75.5-76.95 Electronic computers. Computer science 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. (C) 2011 Elsevier Inc. All rights reserved. 2011 Article PeerReviewed Han, Pang Ying and Jin, Andrew Teoh Beng and Toh Kar, Ann (2011) Kernel Discriminant Embedding in face recognition. Journal of Visual Communication and Image Representation, 22 (7). pp. 634-642. ISSN 10473203 http://dx.doi.org/10.1016/j.jvcir.2011.07.009 doi:10.1016/j.jvcir.2011.07.009 doi:10.1016/j.jvcir.2011.07.009
spellingShingle QA75.5-76.95 Electronic computers. Computer science
Han, Pang Ying
Jin, Andrew Teoh Beng
Toh Kar, Ann
Kernel Discriminant Embedding in face recognition
title Kernel Discriminant Embedding in face recognition
title_full Kernel Discriminant Embedding in face recognition
title_fullStr Kernel Discriminant Embedding in face recognition
title_full_unstemmed Kernel Discriminant Embedding in face recognition
title_short Kernel Discriminant Embedding in face recognition
title_sort kernel discriminant embedding in face recognition
topic QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/3340/
http://shdl.mmu.edu.my/3340/
http://shdl.mmu.edu.my/3340/