Abstract: Face micro-expressions have the characteristics of short duration and small amplitude of movement.Factors such as the small sample size of dataset also bring great challenges to micro-expression recognition.To solve these problems,this paper proposes a micro-expression recognition method based on ME-ResNet residual network.First,in the pre-processing stage,extract the key frame sequence between the start frame and the vertex frame in the micro-expression video clip at equal intervals and then,use the improved Farneback optical flow method to extract the motion features of the micro-expression key frame sequence.Se-cond,construct a ResNet50 network based on 3D convolution and add the spatial channel attention CBAM mechanism to the network Bottleneck module,so as to enhance the ability to focus on key facial motor features.Next,construct the ME-ResNet network model and sent the extracted facial optical flow motion features to the network for training.Finally,use the data enhancement to increase the sample size of network training and apply the ME-ResNet network model to micro-expression recognition tasks.Also,experimental results on CASME II,SMIC and SAMM datasets show that the recognition rate of the proposed algorithm reaches 84.42%,72.56% and 70.41% respectively.It has higher recognition ability compared with other algorithms.

Key words: Micro-expression recognition, Farneback optical flow method, Convolutional neural networks, Motion features, Data augmentation