Micro-expression recognition with attention mechanism and region enhancement

Micro-expression recognition (MER) is an interdisciplinary research task that has attracted attention. This is because MER can be relevant to multiple fields, such as computer vision, psychology, human-computer interaction, and social security. Because the scarcity of databases and difficulty in video semantics understanding, end-to-end MER still faces many challenges. In this study, we propose an MER framework with attention mechanism and region enhancement (MER-AMRE). Attention mechanisms are introduced to enhance the representation performance of the model, which can improve the recognition accuracy. Additionally, we use Euler video magnification in data preprocessing to enhance facial variation areas. AffectNet is leveraged to pretrain a facial region of interest (RoI) feature extractor with attention regions. Finally, we combine the facial RoI features with global facial features to recognize micro-expressions. Extensive experiments on two well-known micro-expression datasets, CASME II and SAMM, verified the robustness and generalization of the proposed MER-AMRE framework.