Multiresolution Feature Based Fractional Power Polynomial Kernel Fisher Discriminant Model for Face Recognition

This paper prese nts a technique for face recognition which uses wavelet transform to derive desirable facial features. Three level decompositions are used to form the pyramidal multiresolution features to cope with the variations due to illumination and facial expression changes. The fractional power polynomial kernel maps the input data into an implicit feature space with a nonlinear mapping. Being linear in the feature space, but nonlinear in the input space, kernel is capable of deriving low dimensional features that incorporate higher order statistic. The Linear Discriminant Analysis is applied to kernel mapped multiresolution featured data. The effectiveness of this Wavelet Kernel Fisher Classifier algorithm is compared with the different existing popular algorithms for face recognition using FERET, ORL Yale and YaleB databases. This algorithm performs better than some of the existing popular algorithms.

[1]  David Zhang,et al.  A Fourier-LDA approach for image recognition , 2005, Pattern Recognit..

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

[3]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Chengjun Liu,et al.  Independent component analysis of Gabor features for face recognition , 2003, IEEE Trans. Neural Networks.

[7]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[8]  Yuntao Qian,et al.  Face recognition using a kernel fractional-step discriminant analysis algorithm , 2007, Pattern Recognit..

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

[10]  Kin-Man Lam,et al.  Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image , 2006, IEEE Transactions on Image Processing.

[11]  Konstantinos N. Plataniotis,et al.  Face recognition using kernel direct discriminant analysis algorithms , 2003, IEEE Trans. Neural Networks.

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