基于特征流的面部表情运动分析及应用

面部表情的分析与识别,不但在社会生活中具有普遍意义,而且在计算机的情感计算方面也起着有重要作用.关于表情运动特征的分析,有根据人脸面部几何结构特征的变化来分析的,有根据特征脸的概念定义的"表情空间"来分析的,也有从特征点跟踪的方法或运动模板的角度来分析的.基于人脸面部物理-几何结构模型,提取面部表情特征区域,通过动态图像序列中的光流估计,计算其运动场,进而计算特征流向量,把一组图像序列的运动向量组成运动特征序列,对表情的运动进行分析.该系统作为一个智能体应用到多功能感知机中,作为视频通道输入的一部分来理解人类的体势语言信息.

Analysis and Application of the Facial Expression Motions Based on Eigen-Flow

Analysis and recognition of the facial expressions play an important role in both the social society and the affective computing in the field of the computer science. There are three primary methods for the analysis of expression motive features: methods based on the facial geometrical structure features, on the definition of the expression space based on the eigen-face, and on the motion pattern matching. This paper extracts the feature regions of the expressions based on the facial physics-muscle model and evaluats the optical flow of the expression image sequences. The eigen-flow vectors can be calculated to constitute the eigen-sequences, and therefore, the expressions can be analyzed. The recognition system is implemented as an agent in the multi-perception machine and it is used as part of the video input for understanding the human body languages.