Capture of microexpressions based on the entropy of oriented optical flow

This paper proposes an algorithm that is effective in detecting the key frame of microexpression based on the entropy of oriented optical flow. Initially, this paper used an improved Horn-Schunck optical flow to extract the motion features of adjacent frames. Then, the threshold algorithm was used to filter the optical flow vectors with high-projective modulus. To capture the key frame of microexpression, the paper used information entropy to count the direction of optical flow vectors and analyzed the changing of microexpressions using an entropy vector of video sequences. Finally, the algorithm in this paper was verified with microexpression database SMIC (Oulu University) and CASME (the Director of the Institute of Psychology at the Chinese Academy of Sciences, Fu Xiaolan). Compared with traditional frame differences, experiments show that the algorithm is good not only in expressing the trend of the microexpression but also in providing the basis for microexpression recognition.