3D smiling facial expression recognition based on SVM

Shuming Liu, Xiaopeng Chen, Di Fan, Xu Chen, Fei Meng, Qiang Huang
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引用次数: 6

Abstract

Using Kinect acquired RGB-D image to obtain a face feature parameters and three-dimensional coordinates of the characteristic parameters, and to select the characteristic parameter Facial by Candide-3 model, and feature extraction and normalization. Smile face expression data collection through Kinect, SVM collected to smiley face data classify and output the result of recognition, and the results compared with two-dimensional image of smiling face expression recognition results. Experimental results show that three-dimensional image of smiling face expression recognition accuracy than the two-dimensional image of smiling face. This research has important significance for the research and application of facial expression recognition technology.
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基于支持向量机的三维微笑表情识别
利用Kinect获取的RGB-D图像获得人脸特征参数和特征参数的三维坐标,并通过Candide-3模型选择人脸特征参数,并进行特征提取和归一化。通过Kinect采集笑脸表情数据,对采集到的笑脸数据进行分类并输出识别结果,并将结果与二维图像的笑脸表情识别结果进行对比。实验结果表明,三维图像的笑脸表情识别准确率高于二维图像的笑脸。该研究对面部表情识别技术的研究和应用具有重要意义。
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