Recently Sparse Representation-based classification (SRC) has been successfully applied to pattern classification. In this paper, we present a robust Coarse-to-Fine Sparse Representation (CFSR) for face recognition. In the coarse coding phase, the test sample is represented as a linear combination of all the training samples. In the last phase, a number of “nearest neighbors” is determined to represent the test sample to perform classification. CFSR produces the sparseness through the coarse phase, and exploits the local data structure to perform classification in the fine phase. Moreover, this method can make a better classification decision by determining an individual dictionary for each test sample. Extensive experiments on benchmark face databases show that our method has competitive performance in face recognition compared with other state-of-the-art methods.
Robust Coarse-to-Fine Sparse Representation for Face Recognition / Sun, Y; Tistarelli, Massimo. - 8157:(2013), pp. 171-180. (Intervento presentato al convegno Image Analysis and Processing – ICIAP 2013 tenutosi a Napoli, Italia nel 2013-09-11/2013-09-13) [10.1007/978-3-642-41184-7_18].
Robust Coarse-to-Fine Sparse Representation for Face Recognition
TISTARELLI, Massimo
2013-01-01
Abstract
Recently Sparse Representation-based classification (SRC) has been successfully applied to pattern classification. In this paper, we present a robust Coarse-to-Fine Sparse Representation (CFSR) for face recognition. In the coarse coding phase, the test sample is represented as a linear combination of all the training samples. In the last phase, a number of “nearest neighbors” is determined to represent the test sample to perform classification. CFSR produces the sparseness through the coarse phase, and exploits the local data structure to perform classification in the fine phase. Moreover, this method can make a better classification decision by determining an individual dictionary for each test sample. Extensive experiments on benchmark face databases show that our method has competitive performance in face recognition compared with other state-of-the-art methods.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.