基于自动提取特征点的三维人脸表情识别

为实现完全自动的人脸表情识别,提出一种基于自动提取三维及二维特征点的三维人脸表情识别算法.该算法采用在三维点云、深度图像以及三维点云对应的二维特征图像上分别自动获得特定特征点,并将非点云上获得的特征点映射回三维点云以获得全部需用特征点的方法.基于这些自动获取的特征点得到三维欧氏距离组成25维特征向量以待分类.通过运用支持向量机作为分类器,取得了平均87.1%的6种基本表情的分类结果,其中惊讶、开心表情的分类结果分别达到了92.3%和91.7%. 

3D Expression Recognition Based on Automatically Detected Facial Points

In order to deal with automatic facial expression recognition task, a 3D facial expression recognition algorithm was proposed based on automatically detected fiducial points from 3D mesh models, range images and the corresponding 2D feature images. First, the fiducial points were automatically detected on the 3D mesh models, range images and the corresponding 2D feature images. And some fiducial points got from non-mesh models were mapped back to 3D mesh models to get full fiducial points. Then, Euclidian distances between these fiducial points were extracted as feature to feed the SVM classifier. Compared with two state-of-the-art algorithms, the classification results show that the fully automatic algorithm can achieve highly competitive classification rate. The average recognition rate was 87.1%, especially the recognition rate for surprise and happiness expression were 92.3% and 91.7% respectively.