Discriminative Features Extraction in Minor Component Subspace

In this paper, we propose a new method of extracting the discriminative features for classification from a given training dataset. The proposed method combines the advantages of both the null space method and the maximum margin criterion (MMC) method, whilst overcomes their drawbacks. The better performance of the proposed method is confirmed by face recognition experiments.

[1]  Hakan Cevikalp,et al.  Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[4]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[5]  Ja-Chen Lin,et al.  A new LDA-based face recognition system which can solve the small sample size problem , 1998, Pattern Recognit..

[6]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[7]  A. Martínez,et al.  The AR face databasae , 1998 .

[8]  Tao Jiang,et al.  Efficient and robust feature extraction by maximum margin criterion , 2003, IEEE Transactions on Neural Networks.

[9]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.