Aiming at the problems of difficulty in capturing micro expression features and low recognition rate,an improved pseudo 3D residu-al network micro expression recognition method is proposed.Firstly,after preprocessing the dataset samples,different bottleneck structures were designed for Pseudo-3D-SS,Pseudo-3D-PS,and Pseudo-3D-SSS in the design phase of the Pseudo 3D Residual Network(Pseudo 3DResNet)to address three issues:unreasonable interlayer order of basic 3D residual units,unstable output values,and information propaga-tion blocking.These structures were applied to four residual blocks.Secondly,two independently designed convolutional layers and pooling layers are applied to filter weakly characterized micro expression sequences between different residual blocks,in order to further highlight valuable feature information and remove redundancy,achieving spatiotemporal feature extraction.Finally,in order to further reduce the im-pact of brightness changes on optical flow feature extraction and improve the speed of tracking feature points,the L1 norm based total variation optical flow method was improved.The TV-OFM method was used to extract optical flow features to obtain horizontal and vertical optical flow sequences for each micro expression.The experiment showed that the proposed method improved the unweighted F1 score(UF1)and unweight-ed average recall(UAR)by 3.61%and 2.92%,respectively,compared to recent methods;In the comparative experiment of optical flow meth-od,the proposed method improved the speed of tracking feature points by 30.08%compared to the comparative method;Comparing the four improved residual block variants with other network variants,it was found that the Avg(UF1+UAR)of the proposed model increased by 2.5%,and the network structure has stronger generalization and feature extraction capabilities;Overlaying convolution and pooling operations in lay-ers can extract features more evenly,further improve the recognition rate,and make the model more robust and progressiveness.
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