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This paper exploits the feature extraction capabilities of the discretecosine transform (DCT) together with an illumination normalization approach inthe logarithm domain that increase its robustness to variations in facialgeometry and illumination. Secondly in the same domain the entropy measures areapplied on the DCT coefficients so that maximum entropy preserving pixels canbe extracted as the feature vector. Thus the informative features of a face canbe extracted in a low dimensional space. Finally, the kernel entropy componentanalysis (KECA) with an extension of arc cosine kernels is applied on theextracted DCT coefficients that contribute most to the entropy estimate toobtain only those real kernel ECA eigenvectors that are associated witheigenvalues having high positive entropy contribution. The resulting system wassuccessfully tested on real image sequences and is robust to significantpartial occlusion and illumination changes, validated with the experiments onthe FERET, AR, FRAV2D and ORL face databases. Experimental comparison isdemonstrated to prove the superiority of the proposed approach in respect torecognition accuracy. Using specificity and sensitivity we find that the bestis achieved when Renyi entropy is applied on the DCT coefficients. Extensiveexperimental comparison is demonstrated to prove the superiority of theproposed approach in respect to recognition accuracy. Moreover, the proposedapproach is very simple, computationally fast and can be implemented in anyreal-time face recognition system.

international journal of information technology and computer science

A Face Recognition approach based on entropy estimate of the nonlinear  DCT features in the Logarithm Domain together with Kernel Entropy Component  Analysis