Letters: A novel dimensionality-reduction approach for face recognition

  • Authors:

  • Fengxi Song;David Zhang;Jingyu Yang

  • Affiliations:

  • Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Sili, Shenzhen 518055, PR China and New Star Research Institute of Applied Technology, Hefei, PR China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, PR China;Department of Computer Science, Nanjing University of Science & Technology, Nanjing, PR China

  • Venue:
  • Neurocomputing
  • Year:
  • 2006

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Abstract

In this paper, we propose a novel dimensionality-reduction method-Fisher discriminant with Schur decomposition (FDS). Similar to Foley-Sammon discriminant analysis (FSD), FDS is an improvement of Fisher discriminant analysis (FDA) in that it eliminates linear dependences among discriminant vectors. In comparison with FSD, FDS is very simple in theory and realization. Experimental results conducted on two benchmark face-image databases, i.e. ORL and AR, demonstrate that FDS is highly effective and efficient in reducing dimensionalities of facial image spaces. Especially when the size of a database is large, FDS can even outperform the state-of-the-art facial feature extraction methods such as the null space method.