Micro-expression Recognition Method Combining Feature Fusion and Attention Mechanism

Abstract

Micro-expression refers to an uncontrollable muscle movement on the face when people try to hide or suppress their true emotions.Due to the short duration,small motion range,and difficulty in concealing and restraining,the recognition accuracy of such emotional facial expressions is restricted.In order to cope with these challenges,this paper proposes a novel micro-expression recognition method combining feature fusion and attention mechanism,considering optical flow features and face features,and further adding attention mechanism to improve the recognition performance.The processing steps of this method are as follows:1)Extract the optical flow and optical strain from Onset to Apex in each micro-expression segment,input the vertical optical flow,horizontal optical flow and optical strain into a shallow 3DCNN,and extract the optical flow features.2)Taking the deep convolution neural network ResNet-10 as the backbone network,the convolution attention module is added to extract face features.3)Combine the two feature vectors for classification.The experimental results reveal that the proposed method is superior to the traditional methods and existing deep learning methods in micro-expression recognition.

Keywords

• micro-expression recognition|feature fusion|attention mechanism|deep learning|transfer learning