Facial expression recognition based on Adaboost-Gaussian process classification

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	Facial expression recognition based on Adaboost-Gaussian process classification
	Li, Wen-Shu ; He, Fang-Fang ; Qian, Yun-Tao ; Zhou, Chang-Le ; Zhou CL(周昌乐)
刊名	http://dx.doi.org/10.3785/j.issn.1008-973X.2012.01.13
	2012
关键词	Algorithms Classifiers Face recognition Feature extraction Gaussian distribution Gaussian noise (electronic) Gesture recognition Principal component analysis
英文摘要	By using the Gaussian process classifier's advantages of high classification accuracy and low computational complexity, an improved expression recognition method was proposed in order to modify the Adaboost's disadvantage of poor classification accuracy and long time consuming. The facial expression recognition algorithm combines Gaussian process classification (GPC) with Adaboost. The algorithm uses the Gaussian process classifier as weak classifier when training Adaboost. Then these weak classifiers are combined into an overall classification, and the Adaboost is extended into a multi-class classification algorithm. Gabor wavelet transformation is used to extract facial expressional features, since the high-dimensional Gabor features are redundant; the two-dimensional principal component analysis (2DPCA) is used to select these features. Experimental results based on the Cohn-Kanade database and JAFFE database show that the accuracy and recognition speed of the algorithm are inspiring.
语种	英语
出版者	Zhejiang University Press
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92782]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Li, Wen-Shu,He, Fang-Fang,Qian, Yun-Tao,et al. Facial expression recognition based on Adaboost-Gaussian process classification[J]. http://dx.doi.org/10.3785/j.issn.1008-973X.2012.01.13,2012.
APA	Li, Wen-Shu,He, Fang-Fang,Qian, Yun-Tao,Zhou, Chang-Le,&周昌乐.(2012).Facial expression recognition based on Adaboost-Gaussian process classification.http://dx.doi.org/10.3785/j.issn.1008-973X.2012.01.13.
MLA	Li, Wen-Shu,et al."Facial expression recognition based on Adaboost-Gaussian process classification".http://dx.doi.org/10.3785/j.issn.1008-973X.2012.01.13 (2012).