引用本文:付晓峰,韦 巍.基于多尺度中心化二值模式的人脸表情识别[J].控制理论与应用,2009,26(6):629~633.[点击复制]
FU Xiao-feng,WEI Wei.Facial expression recognition based on multi-scale centralized binary pattern[J].Control Theory and Technology,2009,26(6):629~633.[点击复制]
基于多尺度中心化二值模式的人脸表情识别
Facial expression recognition based on multi-scale centralized binary pattern
摘要点击 1280  全文点击 1142  投稿时间：2008-06-12  修订日期：2008-11-07
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DOI编号  10.7641/j.issn.1000-8152.2009.6.008
  2009,26(6):629-633
中文关键词  中心化二值模式  多尺度  图像欧式距离  表情识别
英文关键词  centralized binary pattern  multi-scale  image Euclidean distance  facial expression recognition
基金项目  浙江省人才基金资助项目(R105341); 浙江省“新世纪151人才工程”资助项目.
作者单位E-mail
付晓峰 浙江大学 电气工程学院, 浙江 杭州 310027 saqieer98@yahoo.com.cn 
韦 巍 浙江大学 电气工程学院, 浙江 杭州 310027  
中文摘要
      现有局部二值模式(LBP) 算子存在不足: 产生的直方图维数过长、鉴别力不高、对噪声反应敏感. 针对此类问题, 提出中心化二值模式(CBP) 算子, 其优点: 1) 通过比较邻域中近邻点对, 大大降低了直方图维数; 2) 考虑中心像素点的作用并赋予其最高权重, 实现鉴别力的提高; 3) 改变LBP算子的符号函数, 明显减弱白噪声对图像的影响.此外, 为提高识别率, 将多尺度CBP(MCBP) 直方图作为人脸表征. 为增强算法对表情图像中细小变形的鲁棒性, 引入图像欧式距离(IMED) 并将其嵌入MCBP方法. 在JAFFE和Cohn-Kanade表情库的实验结果表明: 所提方法优于其它表情识别方法, IMED可增强MCBP的表情识别能力.
英文摘要
      The existing local binary pattern(LBP) operators have disadvantages of long histograms produced by them, low discrimination and high sensitivities to noise. To deal with these problems, we propose the centralized binary pattern(CBP) operator. The CBP operator has several advantages: 1) It significantly reduces the histogram dimensionality by comparing pairs of neighbors in the neighborhood; 2) It enhances the discrimination by emphasizing the effect of the center pixel point through giving it the largest weight; 3) It decreases the white noise influence on face images by modifying the sign function of the existing LBP operator. Moreover, the multi-scale CBP(MCBP) histogram is used as face representation to increase the recognition accuracy. Furthermore, in order to improve the robustness to small deformation of expressional images, the image Euclidean distance(IMED) is introduced and embedded in MCBP. Experiments on JAFFE and Cohn-Kanade facial expression databases demonstrate that the proposed method outperforms other modern approaches and show that IMED can enhance the performance of MCBP in facial expression recognition.