Block-wise 2D kernel PCA/LDA for face recognition

Authors:
Armin Eftekhari;Mohamad Forouzanfar;Hamid Abrishami Moghaddam;Javad Alirezaie
Affiliations:
Colorado School of Mines, Golden CO 80401, USA;School of Information Technology and Engineering, University of Ottawa, 800 King Edward Avenue, Ottawa, Ontario, KIN 6N5, Canada and Department of Biomedical Engineering, Faculty of Electrical Eng ...;Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran, Iran and GRAMFC Unité de Génie Biophysique et M ...;Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Ryerson University, 350 Victoria street, Toronto, Ontario, Canada M5B 2K3
Venue:
Information Processing Letters
Year:
2010

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Abstract

Direct extension of (2D) matrix-based linear subspace algorithms to kernel-induced feature space is computationally intractable and also fails to exploit local characteristics of input data. In this letter, we develop a 2D generalized framework which integrates the concept of kernel machines with 2D principal component analysis (PCA) and 2D linear discriminant analysis (LDA). In order to remedy the mentioned drawbacks, we propose a block-wise approach based on the assumption that data is multi-modally distributed in so-called block manifolds. Proposed methods, namely block-wise 2D kernel PCA (B2D-KPCA) and block-wise 2D generalized discriminant analysis (B2D-GDA), attempt to find local nonlinear subspace projections in each block manifold or alternatively search for linear subspace projections in kernel space associated with each blockset. Experimental results on ORL face database attests to the reliability of the proposed block-wise approach compared with related published methods.