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

Information

Ying Chen²  Shiqing Zhang¹ 

[1] Institute of Image Processing and Pattern Recognition, Taizhou University, Taizhou 317000, China;

关键词: non-negative least-squares;    sparse coding;    local binary patterns;    facial expression recognition;   

DOI  :  10.3390/info5020305

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.