A facial expression recognition system based on supervised locally linear embedding

Authors:
Dong Liang;Jie Yang;Zhonglong Zheng;Yuchou Chang
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Huashan Road 1954, Shanghai 200030, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Huashan Road 1954, Shanghai 200030, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Huashan Road 1954, Shanghai 200030, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Huashan Road 1954, Shanghai 200030, China
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

In this paper, a facial expression recognition system based on supervised locally linear embedding (SLLE) is introduced. The system consists of three modules: face detection, feature extraction with SLLE and classification. In face detection module, two independent characteristics, skin color characteristic and motion characteristic are used to detect face region, and a trained SVM is used to verify candidate regions. In feature extraction module, SLLE, a supervised learning algorithm that can compute low dimensional, neighborhood-preserving embeddings of high dimensional data is used to reduce data dimension and extract features. In classification module, minimum-distance classifier is used to recognize different expressions. The experiments show that the proposed method is superior to PCA-based method.