  In this paper, we present a novel Gabor wavelet based Kernel Entropy
Component Analysis (KECA) method by integrating the Gabor wavelet
transformation (GWT) of facial images with the KECA method for enhanced face
recognition performance. Firstly, from the Gabor wavelet transformed images the
most important discriminative desirable facial features characterized by
spatial frequency, spatial locality and orientation selectivity to cope with
the variations due to illumination and facial expression changes were derived.
After that KECA, relating to the Renyi entropy is extended to include cosine
kernel function. The KECA with the cosine kernels is then applied on the
extracted most important discriminating feature vectors of facial images to
obtain only those real kernel ECA eigenvectors that are associated with
eigenvalues having positive entropy contribution. Finally, these real KECA
features are used for image classification using the L1, L2 distance measures;
the Mahalanobis distance measure and the cosine similarity measure. The
feasibility of the Gabor based KECA method with the cosine kernel has been
successfully tested on both frontal and pose-angled face recognition, using
datasets from the ORL, FRAV2D and the FERET database.
