Micro-expression Recognition Method Combining Dual-Stream Convolution and Capsule Network

Aiming at the problem that the existing micro-expression recognition methods do not comprehensively consider the facial spatial structure information and the single input feature, which leads to the low recognition rate of accuracy, the method combining dual-stream convolution and capsule network is proposed. An improved dual-stream convolutional shallow network is used to extract feature, and CapsNet is used for micro-expression identification. This method first takes the image with the magnified motion amplitude and the optical flow image as dual feature input, and uses attention mechanism and dual-stream convolutional network to extract the spatiotemporal features. Dynamic routing between capsules is used to encode features for better expression. Finally, the squashing function for classification. Experiments are used CASME II, SAMM and SMIC datasets. Contrast with existing advanced methods, accuracy of micro-expression i identification is increased by 3.34%, 3.71%, and 4.13%, respectively, indicating the advanced nature and effectiveness of this method.