人脸像的面部表情识别一直是人脸识别的一个难点，为了提高表情人脸识别的鲁棒性，提出了一种基于特征运动的人脸识别方法，该方法首先利用块匹配的方法来确定表情人脸和无表情人脸之间的运动向量，然后利用主成分分析方法（PCA）从这些运动向量中，产生低维子空间，称之为特征运动空间，测试时，先将测试人脸与无表情人脸之间的运动向量投影到特征运动空间，再根据这个运动向量在特征运动空间里的残差进行人脸识别，同时还介绍了基于特征运动的个人模型方法和公共模型方法，实验结果证明，该新算法在表情人脸的识别上，优于特征脸方法，有非常高的识别率。

The difficulty of a face recognition problem is to handle different types of variations, such as facial expression, illumination and pose. In order to improve the robustness of face recognition with respect to facial expression, this paper proposes a new approach, the eigenmotion based method, which is tolerant to large variations of facial expressions. In this new approach, first motion vectors are computed between a testing face image and a neutral training image using the block matching method, then projected to a low dimensional subspace that is pre trained by applying principal component analysis(PCA) to motion vectors resulting from training images with expression variations. This subspace is called an eigenmotion space. Finally the identification of the testing image is determined based on its residue to the eigenmotion space. Both the individual modeling method and the common modeling method are described in this paper. Experimental results show that the proposed eigenmotion based method outperforms the eigenface approach in the presence of facial expression variations. The approach can be extended to model other types of variations as well, for example, illumination and pose variations.