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Micro-expression (ME) recognition plays a crucial role in a wide range ofapplications, particularly in public security and psychotherapy. Recently,traditional methods rely excessively on machine learning design and therecognition rate is not high enough for its practical application because ofits short duration and low intensity. On the other hand, some methods based ondeep learning also cannot get high accuracy due to problems such as theimbalance of databases. To address these problems, we design a multi-streamconvolutional neural network (MSCNN) for ME recognition in this paper.Specifically, we employ EVM and optical flow to magnify and visualize subtlemovement changes in MEs and extract the masks from the optical flow images. Andthen, we add the masks, optical flow images, and grayscale images into theMSCNN. After that, in order to overcome the imbalance of databases, we added arandom over-sampler after the Dense Layer of the neural network. Finally,extensive experiments are conducted on two public ME databases: CASME II andSAMM. Compared with many recent state-of-the-art approaches, our methodachieves more promising recognition results.

A Multi-stream Convolutional Neural Network for Micro-expression Recognition Using Optical Flow and EVM