Facial Micro-expressions Recognition Based on Fusion Optical Flow and Multi-attention Mechanism
Aiming at the problem of low recognition accuracy due to the short duration and small motion range of micro-expressions occurring in specific areas of face,this paper explores a face micro-expression recognition method based on optical flow and multi-attention mechanism.Firstly,the facial optical flow features of vertex frames and initial frames are extracted by using Total Variation and L1 norm(TVL1)algorithm,and the optical flow strain is calculated as a supplementary feature;Then,three kinds of opti-cal flow features are used as inputs,and ResBlock,which introduces Global Attention Mechanism(GAM)and Dual Attention mechanism(DA),is applied to extract features and classify micro-expres-sions,so as to reduce facial information dispersion and enlarge global optical flow features.Finally,the classification experiments on CASME and CASME Ⅱ data sets are carried out to verify the feasibility and effectiveness of the proposed method for frontal face classification.