Affine Subspace Nearest Points Classification Algorithm for Wavelet Face Recognition

A new classification method called Affine Subspace Nearest Points (ASNP) algorithm is presented in this paper. Similar to the idea of the geometrical explanation of Support Vector Machines (SVMs), the ASNP algorithm designed by us as a binary classifier extends the areas searched for the nearest points from the convex hulls in SVM to affine subspaces, and constructs the decision hyperplane separating the affine subspaces with equivalent margin. We combine the algorithm with the 2D wavelet transform (WT) for face recognition. The low frequency features of face images extracted by 2D wavelet transform are employed as the inputs of the ASNP classifiers. Experiments on the ORL face database show that the proposed method obtains good recognition accuracy.

[1]  Jen-Tzung Chien,et al.  Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Karl Sammut,et al.  Wavelet packet face representation and recognition , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[4]  S. Mallat Multiresolution approximations and wavelet orthonormal bases of L^2(R) , 1989 .

[5]  I. Daubechies Orthonormal bases of compactly supported wavelets , 1988 .

[6]  DaubechiesIngrid Orthonormal bases of compactly supported wavelets II , 1993 .

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

[8]  Rafal Foltyniewicz,et al.  Automatic face recognition via wavelets and mathematical morphology , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[9]  S. Sathiya Keerthi,et al.  A fast iterative nearest point algorithm for support vector machine classifier design , 2000, IEEE Trans. Neural Networks Learn. Syst..

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

[11]  Guodong Guo,et al.  Face recognition by support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[12]  Kristin P. Bennett,et al.  Duality and Geometry in SVM Classifiers , 2000, ICML.

[13]  Pong C. Yuen,et al.  Human face recognition using PCA on wavelet subband , 2000, J. Electronic Imaging.

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

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

[16]  Amir Averbuch,et al.  Image compression using wavelet transform and multiresolution decomposition , 1996, IEEE Trans. Image Process..

[17]  Shuzhi Sam Ge,et al.  Face recognition by applying wavelet subband representation and kernel associative memory , 2004, IEEE Transactions on Neural Networks.