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Face Recognition Based on Sparse Representation and Joint Sparsity Model with Matrix Completion

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2 Author(s)
Inaba, F.K. ; Univ. Fed. do Espirito Santo (UFES), Vitoria, Brazil ; Salles, E.O.T.

In this paper we verify the impacts of the Joint Sparsity Model with Matrix Completion (JSM-MC) for the composition of training set in the context of face recognition using the Sparse Representation-based Classifier (SRC). A pre-processing step (histogram equalization) is performed in the face images to reduce the effects of illumination change. A clustering of training images is done to reduce the training set and uses the l1-norm of the sparse representation coefficients instead of the residuals for classification. The results are evaluated using a database with different illumination conditions and we also investigate the behavior of the system when the face image is partially occluded.

Published in:

Latin America Transactions, IEEE (Revista IEEE America Latina)  (Volume:10 ,  Issue: 1 )