Arindam Kar

Work place: Indian Statistical Institute, Kolkata-700108, India



Research Interests: Computer systems and computational processes, Computer Vision, Image Manipulation, Image Processing, Data Structures and Algorithms, Algorithmic Information Theory


Arindam Kar received his M.Sc and M.Tech. degrees from Indian Institute of Technology (Kharagpur), India in 1998 and 2000 respectively. At present he is persuing Ph. D. from Jadavpur University, India, and working as an Associate Scientist in Indian Statistical Institute, Kolkata, India. His areas of interest includes Computer Vision, Image Processing, Genetic Algorithms, and Watermarking Techniques.

Author Articles
A Face Recognition Approach Based on Entropy Estimate of the Nonlinear DCT Features in the Logarithm Domain Together with Kernel Entropy Component Analysis

By Arindam Kar Debotosh Bhattacharjee Dipak Kumar Basu Mita Nasipuri Mahantapas Kundu

DOI:, Pub. Date: 8 Aug. 2013

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.

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