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Information 2014, 5(2), 305-318; doi:10.3390/info5020305

Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

Institute of Image Processing and Pattern Recognition, Taizhou University, Taizhou 317000, China
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Received: 24 January 2014 / Revised: 3 April 2014 / Accepted: 21 April 2014 / Published: 15 May 2014
(This article belongs to the Section Information Applications)
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Abstract

Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS) sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP) and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE) database. Compared with other widely used methods such as linear support vector machines (SVM), sparse representation-based classifier (SRC), nearest subspace classifier (NSC), K-nearest neighbor (KNN) and radial basis function neural networks (RBFNN), the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks. View Full-Text
Keywords: non-negative least-squares; sparse coding; local binary patterns; facial expression recognition non-negative least-squares; sparse coding; local binary patterns; facial expression recognition
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Chen, Y.; Zhang, S.; Zhao, X. Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding. Information 2014, 5, 305-318.

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