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.
| Original language | English |
|---|---|
| Pages (from-to) | 634-642 |
| Number of pages | 9 |
| Journal | Journal of Visual Communication and Image Representation |
| Volume | 22 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2011 Oct 1 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Media Technology
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
Cite this
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Kernel discriminant embedding in face recognition. / Han, Pang Ying; Teoh, Beng Jin; Toh Kar, Ann.
In: Journal of Visual Communication and Image Representation, Vol. 22, No. 7, 01.10.2011, p. 634-642.Research output: Contribution to journal › Article
TY - JOUR
T1 - Kernel discriminant embedding in face recognition
AU - Han, Pang Ying
AU - Teoh, Beng Jin
AU - Toh Kar, Ann
PY - 2011/10/1
Y1 - 2011/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80051614668&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80051614668&partnerID=8YFLogxK
U2 - 10.1016/j.jvcir.2011.07.009
DO - 10.1016/j.jvcir.2011.07.009
M3 - Article
VL - 22
SP - 634
EP - 642
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
SN - 1047-3203
IS - 7
ER -