Kernel discriminant embedding in face recognition

Pang Ying Han, Andrew Teoh Beng Jin, Ann Toh Kar

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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 languageEnglish
Pages (from-to)634-642
Number of pages9
JournalJournal of Visual Communication and Image Representation
Issue number7
Publication statusPublished - 2011 Oct

Bibliographical note

Funding Information:
This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University (Grant No. R112002105080020 (2010)).

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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