文章基本信息

标题：Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding
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作者：Ying Chen ; Shiqing Zhang ; Xiaoming Zhao 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2014
卷号：5
期号：2
页码：305-318
DOI：10.3390/info5020305
语种：English
出版社：MDPI Publishing
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.
关键词：non-negative least-squares; sparse coding; local binary patterns; facial expression recognition