  This paper exploits the feature extraction capabilities of the discrete
cosine transform (DCT) together with an illumination normalization approach in
the logarithm domain that increase its robustness to variations in facial
geometry and illumination. Secondly in the same domain the entropy measures are
applied on the DCT coefficients so that maximum entropy preserving pixels can
be extracted as the feature vector. Thus the informative features of a face can
be extracted in a low dimensional space. Finally, the kernel entropy component
analysis (KECA) with an extension of arc cosine kernels is applied on the
extracted DCT coefficients that contribute most to the entropy estimate to
obtain only those real kernel ECA eigenvectors that are associated with
eigenvalues having high positive entropy contribution. The resulting system was
successfully tested on real image sequences and is robust to significant
partial occlusion and illumination changes, validated with the experiments on
the FERET, AR, FRAV2D and ORL face databases. Experimental comparison is
demonstrated to prove the superiority of the proposed approach in respect to
recognition accuracy. Using specificity and sensitivity we find that the best
is achieved when Renyi entropy is applied on the DCT coefficients. Extensive
experimental comparison is demonstrated to prove the superiority of the
proposed approach in respect to recognition accuracy. Moreover, the proposed
approach is very simple, computationally fast and can be implemented in any
real-time face recognition system.
