Micro-Expression Recognition Algorithm Based on Information Entropy Feature  

作　　者：WU Jin MIN Yu YANG Xiaodie MA Simin 吴进;闵育;杨小蝶;马思敏(School of Electronic Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710121,China)

机构地区：[1]School of Electronic Engineering,Xi'an University of Posts and Telecoinniunications,Xi'an 710121,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2020年第5期589-599,共11页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China(Nos.61772417,61634004,and 61602377);the Key R&D Progrm Projects in Shaanxi Province(No.2017GY-060);the Shaanxi Natural Science Basic Research Project(No.018JM4018)。

摘　　要：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 ft^ature 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.

关 键 词：micro-expression recognition two-dimensional principal component analysis(2DPCA) optical flow information entropy statistics support vector machine(SVM) 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]