为充分利用视频中人脸表情与中性表情的差异，提出了一种新的对非特定人脸的表情识别方法．该方法针对低复杂度的视频表情识别应用场景，利用参考中性表情的特征点偏移角表征被测表情的变化信息，同时利用二维主成分分析(2DPCA)法提取被测表情帧的二维主成分特征，从而综合使用表情的动态和静态特征，并使用支持向量机分类器进行表情分类识别．在 JAFFE 人脸表情库上的实验结果表明，相对于仅使用 2DPCA 的静态图像表情识别方法，文中所提方法的人脸表情识别准确率平均提高 7%．

In order to fully explore the difference in facial expression in videos,a novel facial expression recogni-tion approach for non- repeatable face,which utilizes both dynamic and static features to recognize the facial expres-sion in videos in low- complexity scenarios,is proposed.In this approach,the feature point offset angle of neutralexpression is presented as a feature to represent the difference information of measured facial expression,and simul-taneously,the two- dimension principal component features are extracted via two- dimension principal componentanalysis (2DPCA).Then,the combined dynamic- static information is sent to support vector machine (SVM) clas-sifiers to implement the facial expression recognition.Experimental results on JAFFE database indicate that,incomparison with the existing method which only uses the static features extracted via 2DPCA,the proposed methodmay result in 7% of increment in facial expression recognition accuracy.