24 November 2014 Facial expression recognition based on fused Feature of PCA and LDP
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Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 930112 (2014) https://doi.org/10.1117/12.2070953
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China
Abstract
Facial expression recognition is an important part of the study in man-machine interaction. Principal component analysis (PCA) is an extraction method based on statistical features which were extracted from the global grayscale features of the whole image .But the grayscale global features are environmentally sensitive. In order to recognize facial expression accurately, a fused method of principal component analysis and local direction pattern (LDP) is introduced in this paper. First, PCA extracts the global features of the whole grayscale image; LDP extracts the local grayscale texture features of the mouth and eyes region, which contribute most to facial expression recognition, to complement the global grayscale features of PCA. Then we adopt Support Vector Machine (SVM) classifier for expression classification. Experimental results demonstrate that this method can classify different expressions more effectively and get higher recognition rate compared with the traditional method.
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Zhang Yi, Hou-lin Mao, Yuan Luo, "Facial expression recognition based on fused Feature of PCA and LDP", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 930112 (24 November 2014); doi: 10.1117/12.2070953; https://doi.org/10.1117/12.2070953
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