Kernel sparse representation-based classifier ensemble for face recognition

Kernel sparse representation-based classifier (KSRC) has been proposed, which has good representation and classification performance on face image data. The performance of KSRC on face image data is partly dependent on the random projection matrix when using the random projection method and the kernel Gram matrix. This paper develops the kernel sparse representation-based classifier ensemble (KSRCE), which does not require to consider the effect of random projection and kernel Gram matrix on KSRC. Actually, the random projection matrix and the kernel Gram matrix could be used for designing the diversity schemes for KSRCE. In the combination stage, we can combine the labels or the reconstruction errors of a test sample. Experimental results on three face data sets show that KSRCE is very promising.

[1]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

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

[3]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[4]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[5]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[6]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  D. B. Gerham.  Characterizing virtual eigensignatures for general purpose face recognition , 1998 .

[8]  Li Zhang,et al.  Wavelet support vector machine , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Li Zhang,et al.  Sparse ensembles using weighted combination methods based on linear programming , 2011, Pattern Recognit..

[11]  Ting Wang,et al.  Kernel Sparse Representation-Based Classifier , 2012, IEEE Transactions on Signal Processing.

[12]  Fabio Roli,et al.  A theoretical and experimental analysis of linear combiners for multiple classifier systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[14]  Weida Zhou,et al.  Support Vector Machines Based on the Orthogonal Projection Kernel of Father Wavelet , 2005, Int. J. Comput. Intell. Appl..

[15]  Allen Y. Yang,et al.  Feature Selection in Face Recognition: A Sparse Representation Perspective , 2007 .

[16]  Jon Atli Benediktsson,et al.  Multiple Classifier Systems , 2015, Lecture Notes in Computer Science.

[17]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

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

[19]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[20]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[21]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  E. Candes,et al.  11-magic : Recovery of sparse signals via convex programming , 2005 .

[24]  Josef Kittler,et al.  Multiple Classifier Systems , 2004, Lecture Notes in Computer Science.

[25]  Stan Z. Li,et al.  Face recognition based on nearest linear combinations , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).