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.