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In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant featureextraction approach for enhanced face recognition is proposed. Firstly, thelow-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 DiscriminativeCommon Vector (KDCV) method. The KDCV method is extended to include cosinekernel function in the discriminating method. The KDCV with the cosine kernelsis then applied on the extracted low energized discriminating feature vectorsto obtain the real component of a complex quantity for face recognition. Inorder to derive positive kernel discriminative vectors; we apply only thosekernel discriminative eigenvectors that are associated with non-zeroeigenvalues. The feasibility of the low energized Gabor block based generalizedKDCV method with cosine kernel function models has been successfully tested forimage classification using the L1, L2 distance measures; and the cosinesimilarity measure on both frontal and pose-angled face recognition.Experimental results on the FRAV2D and the FERET database demonstrate theeffectiveness of this new approach.

A Gabor block based Kernel Discriminative Common Vector (KDCV) approach using cosine kernels for Human Face Recognition