Reconhecimento de Faces Baseado em Representação Esparsa e Modelo de Esparsidade Conjunta com Completamento de Matrizes
(Face Recognition Based on Sparse Representation and Joint Sparsity Model with Matrix Completion)
Fernando Kentaro Inaba (firstname.lastname@example.org), Evandro Ottoni Teatini Salles (email@example.com)
Universidade Federal do Espírito Santo
This paper appears in: Revista IEEE América Latina
Publication Date: Jan. 2012
Volume: 10, Issue: 1
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 illumina¬tion conditions and we also investigate the behavior of the system when the face image is partially occluded.
Sparse Representation, Joint Sparsity Model, Face Recognition, Matrix Completion, l1-minimization
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