Facial Expression Recognition Based on Improved Dictionary Learning and Sparse Representation

Authors

Li Ming, 1.School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China; ;2.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
Xiujiao Peng, 1.School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China; ;
Wang Yan, 2.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

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.

Recommended Citation

Ming, Li; Peng, Xiujiao; and Yan, Wang (2019) "Facial Expression Recognition Based on Improved Dictionary Learning and Sparse Representation," Journal of System Simulation: Vol. 30: Iss. 1, Article 4.
DOI: 10.16182/j.issn1004731x.joss.201801004
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss1/4

First Page

28

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201801004

Last Page

36

CLC

TP391

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201801004