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The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression feature information is not sufficient to accurately represent its sequences.In order to improve the accuracy of micro-expression recognition,first,each frame image is extracted from its sequences,and the image frame is pre-processed by using gray normalization,size normalization,and two-dimensional principal component analysis (2DPCA);then,the optical flow method is used to extract the motion characteristics of the reduced-dimensional image,the information entropy value of the optical flow characteristic image is calculated by the information entropy principle,and the information entropy value is analyzed to obtain the eigenvalue.Therefore,more micro-expression feature information is extracted,including more important information,which can further improve the accuracy of micro-expression classification and recognition;finally,the feature images are classified by using the support vector machine (SVM).The experimental results show that the micro-expression feature image obtained by the information entropy statistics can effectively improve the accuracy of micro-expression recognition.