计算机科学 ›› 2009, Vol. 36 ›› Issue (5): 262-264.
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摘要: 对人脸表情图像进行分割得到眉区、眼区和嘴部区域,再对分割出来的表情区域利用高维局部自相关(HLAC)计算特征并得到加权的特征向量,其中加权系数根据心理学中的FACS表情测量理论选取,最后利用近邻中心距离分类器进行表情识别。实验基于CMU-PITTSBURGH表情图像库,在没有增大计算量的前提下相比PCA方法,特征融合(HLAC+WPCA)的方法显著地提高了表情的识别率。
关键词: 表情识别 特征融合 高维局部自相关 加权主元分析
Abstract: The facial expression image were segmented to form the eyebrows, eyes and mouth areas, and these areas were computed with Higher-order Local Auto-Correlations method, through which the Weighted Principal Component Analysis values of these areas were obtai
Key words: Facial expression recognition, Feature fusion, Higher-order local auto-correlations (HLAC),Weighted principal component analysis (WPCA)
. 一种特征融合算法的表情识别[J]. 计算机科学, 2009, 36(5): 262-264. https://doi.org/
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