Micro-expression recognition using a multi-scale feature extraction network with attention mechanisms

Micro-expressions are instantaneous flashes of facial expressions that reveal a person's true feelings and emotions. Micro-expression recognition (MER) is challenging due to its low motion intensity, short duration, and the limited number of publicly available samples. Although the present MER methods have achieved great progress, they face the problems of a large number of training parameters and insufficient feature extraction ability. In this paper, we propose a lightweight network MFE-Net with Res-blocks to extract multi-scale features for MER. To extract more valuable features, we incorporate Squeeze-and-Excitation attention and multi-headed self-attention mechanisms in our MFE-Net. The proposed network is used for learning features from three optical flow features (i.e. optical strain, horizontal and vertical optical flow images) which are calculated from the onset and apex frames. We employ the LOSO cross-validation strategy to conduct experiments on CASME II and the composite dataset selected by MEGC2019, respectively. The extensive experimental results demonstrate the viability and effectiveness of our method.