Feature Selection Based on KPCA, SVM and GSFS for Face Recognition

The feature selection is very important for improving classifier's accuracy and reducing classifier's running time. In this paper, a novel feature selection method based on KPCA, SVM and GSFS is proposed for face recognition. The proposed method can be described as follows, first KPCA is used for extracting initial face features, secondly, the extracted features are divided into some single feature sets, and then the single feature sets are trained separately by SVM to obtain the best feature set through GSFS. In this way, the dimensionality of the initial features can be reduced and also the best features can be obtained. Experimental results on ORL, IITL and UMIST face databases indicate the effectiveness of the proposed method.

[1]  Naftali Tishby,et al.  Margin based feature selection - theory and algorithms , 2004, ICML.

[2]  Sayan Mukherjee,et al.  Feature Selection for SVMs , 2000, NIPS.

[3]  P. Langley.  Selection of Relevant Features in Machine Learning , 1994 .

[4]  Christopher J. C. Burges,et al.  Simplified Support Vector Decision Rules , 1996, ICML.

[5]  Guodong Guo,et al.  Support vector machines for face recognition , 2001, Image Vis. Comput..

[6]  Nicholas Ayache,et al.  Frequency-Based Nonrigid Motion Analysis: Application to Four Dimensional Medical Images , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[8]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[9]  Lijuan Cao,et al.  A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine , 2003, Neurocomputing.

[10]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[11]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[12]  Koby Crammer,et al.  Margin Analysis of the LVQ Algorithm , 2002, NIPS.

[13]  D. B. Graham,et al.  Characterising Virtual Eigensignatures for General Purpose Face Recognition , 1998 .

[14]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[15]  Lipika Dey,et al.  A feature selection technique for classificatory analysis , 2005, Pattern Recognit. Lett..

[16]  Vicki Bruce,et al.  Face Recognition: From Theory to Applications , 1999 .

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[19]  Yves Grandvalet,et al.  Adaptive Scaling for Feature Selection in SVMs , 2002, NIPS.