Facial Expression Recognition Based on Improved Dictionary Learning and Sparse Representation

doi:10.16182/j.issn1004731x.joss.201801004

Abstract

Abstract: In order to overcome the problems induced by illumination andocclude in facial expression recognition and reduce the time required by sparse representation classification, the facial expression recognition algorithm withfusion of HOG feature and improved KC-FDDL dictionary learning sparserepresentation is put forward. Improved K-means cluster and Fisher discriminationdictionary learningis implemented on thetraining setgenerated by extracting HOG features of normalized expression image.Facial expression classification is conducted by thesparse representation with weighted residuals. Experimental results on the Cohn-Kanade databaseshow that this method is lower time-consumingand more accurate for similar facial expression classification than other facial expression classification methods.

Key words: occlude, HOG features, KC-FDDLdictionarylearning, sparse representation, weighted residuals

CLC Number:

TP391

Li Ming, Peng Xiujiao, Wang Yan. Facial Expression Recognition Based on Improved Dictionary Learning and Sparse Representation[J]. Journal of System Simulation, 2018, 30(1): 28-36.

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