A Gabor-block-based kernel discriminative common vector approach using cosine kernels for human face recognition

Authors:
Arindam Kar;Debotosh Bhattacharjee;Dipak Kumar Basu;Mita Nasipuri;Mahantapas Kundu
Affiliations:
Indian Statistical Institute, Kolkata, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
Venue:
Computational Intelligence and Neuroscience
Year:
2012

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Abstract

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 fromthe 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 Gaborblock-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using the L1, L2 distancemeasures; 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.