Micro-expression recognition model based on optical flow and integrated spatio-temporal-channel attention of ResNet-10
In response to the difficulty of general models to capture the features of micro-expressions at different scales,a micro-expression recognition network based on LiteFlowNet and the improved ResNet-10 is proposed to fully extract the information of different dimensions of micro-expression.The facial micro-movements are first highlighted by EVM,and then the processed data are passed through a lightweight optical flow estimation network,LiteFlowNet,to extract the motion information in the video frames.3D-Attention mechanism is introduced on ResNet-10 for feature extraction to adaptively focus on the most discriminative channel,spatial and temporal features in the micro-expression video.The experimental results verify that the network effectively improves the micro-expression recognition performance.
micro-expression recognitionLiteFlowNet3D-AttentionResNet-10Eulerian video magnification(EVM)
NETL
NSTL
万方数据
维普
