Comparison of Feature Space Methods for Face Recognition

  • Authors:

  • Chunyan Xie;Marios Savvides;B. V. K. Vijaya Kumar

  • Affiliations:

  • Carnegie Mellon University, USA;Carnegie Mellon University, USA;Carnegie Mellon University, USA

  • Venue:
  • CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
  • Year:
  • 2006

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Abstract

Many feature space methods have been investigated for appearance-based face recognition. In this paper we compare a new feature space face recognition method - the class-dependence feature analysis (CFA) with three other popular methods, namely, the principal component analysis (PCA), the linear discriminant analysis (LDA) and the independent component analysis (ICA), for appearance-based 2-D face recognition. The numerical results on the face recognition grand challenge (FRGC) show that the CFA outperforms the other three method