Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2012, Article ID 421032, 12 pages doi:10.1155/2012/421032 Research Article A Gabor-Block-Based Kernel Discriminative Common Vector Approach Using Cosine Kernels for Human Face Recognition Arindam Kar, 1 Debotosh Bhattacharjee, 2 Dipak Kumar Basu, 2 Mita Nasipuri, 2 and Mahantapas Kundu 2 1 Indian Statistical Institute, Kolkata 700108, India 2 Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India Correspondence should be addressed to Arindam Kar, kgparindamkar@gmail.com Received 14 March 2012; Revised 16 July 2012; Accepted 13 August 2012 Academic Editor: Samanwoy Ghosh-Dastidar Copyright © 2012 Arindam Kar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor- block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using the L 1 , L 2 distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach. 1. Introduction Face authentication has gained considerable attention in the near past through the increasing need for access verification systems using several modalities like voice, face image, finger- prints, pin codes, and so forth. Such systems are used for the verification of a user’s identity on the Internet, when using automated banking system, or when entering into a secured building, and so on. The Gabor wavelet transformation (GWT) models well the receptive field profiles of the cortical simple cells and also has the properties of multiscale and multidirectional filtering. These properties are in accordance with the characteristics of human vision [1–3]. Further, the discriminant analysis is an effective image feature extraction and recognition technique as they allow the extraction of discriminative features, reduce dimensionality, and consume less computing time [4, 5]. In our previous work [6], we combined the GWT and Bayesian principal component analysis (PCA) techniques and presented a GWT-Bayesian PCA face recognition method which outperforms some conventional linear discriminating methods. As an extension of linear discriminant technique, the kernel based nonlinear discriminant analysis technique has now been widely applied to the field of pattern recognition. Baudat and Anouar [7] developed a commonly used generalized discriminant analysis (GDA) method for nonlinear discrimination. Jing et al. [8] put forward a Kernel Discriminative Common Vectors (KDCVs) method. In this paper we develop blockbased GWT KDCV and propose a block-based low-energized nonlinear GWT discriminant feature extraction for enhanced face recognition. As the high energized blocks of GWT image generally have larger nonlinear discriminability values. Then the nonlinear discriminant features are extracted from the selected low-energized block of GWT image by presenting a new generalized KDCV method is then extended to include cosine kernel model which extracts the nonlinear discriminating features from the selected blocks to get the best recognition result. These features are finally used for classification using three different classifiers. The experimen- tal results demonstrate the effectiveness of this new approach.