8 March 2018 Facial expression recognition based on weber local descriptor and sparse representation
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Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106111N (2018) https://doi.org/10.1117/12.2288617
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
Automatic facial expression recognition has been one of the research hotspots in the area of computer vision for nearly ten years. During the decade, many state-of-the-art methods have been proposed which perform very high accurate rate based on the face images without any interference. Nowadays, many researchers begin to challenge the task of classifying the facial expression images with corruptions and occlusions and the Sparse Representation based Classification framework has been wildly used because it can robust to the corruptions and occlusions. Therefore, this paper proposed a novel facial expression recognition method based on Weber local descriptor (WLD) and Sparse representation. The method includes three parts: firstly the face images are divided into many local patches, and then the WLD histograms of each patch are extracted, finally all the WLD histograms features are composed into a vector and combined with SRC to classify the facial expressions. The experiment results on the Cohn-Kanade database show that the proposed method is robust to occlusions and corruptions.
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Yan Ouyang, "Facial expression recognition based on weber local descriptor and sparse representation", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106111N (8 March 2018); doi: 10.1117/12.2288617; https://doi.org/10.1117/12.2288617
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