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Sparse Representation for Face Recognition


Shahnazeer C K, Jayavel J


Vol. 14  No. 7  pp. 102-106


This paper provides a problem of automatically recognizing human faces from frontal views with various facial expressions, occlusion, illumination and pose. There are two underlying motivations for us to write this paper: the first is to provide an occlusion and various expressions of the existing face recognition and the second is to offer some insights into the studies of pose and illumination of face recognition. We present a mathematical formulation and an algorithmic framework to achieve these goals. The existing framework offers a sparse representation of the test image with respect to the training image. The sparse representation can be accurately and efficiently computed by the l1 minimization. The proposed framework offers an improved sparse representation based classification algorithm. Firstly, for a discriminative representation, a non-negative constraint of sparse coefficient is added to sparse representation problem. Secondly, Mahalanobis distance is employed instead of Euclidean distance to measure the similarity between original data and reconstructed data. The proposed classification algorithm for face recognition has been evaluated under varying illumination and pose. Extensive experiments on publicly available databases verify the efficacy of the proposed method and support the above claims


Face Recognition, Occlusion, Illumination, Pose, Sparse representation, l1-minimization, Mahalanobis distance