Multiscale feature pyramid perception for micro-expression recognition based on domain adaptation

Micro-expressions (MEs) are unintentional, brief facial expressions with the potential to reveal hidden or suppressed emotions, significantly contributing to the study of human emotions and psychological states. Despite this, the brief duration and subtlety of MEs make micro-expression recognition (MER) a difficult task. This paper introduces a domain adaptation based multi-scale feature pyramid perception framework for MER, namely domain adaptation multi-scale feature pyramid perception network (DAMFPP-Net), extracting common feature representations between MEs and macroexpressions (MaEs) and transferring them to MER task. Specifically, the network extracts multi-scale features of both MEs and MaEs by multi-scale feature pyramids construction and perception module. Then, domain adaptation is utilized to reduce the differences in feature distributions between the two, thus obtaining a shared emotional representation. Finally, the common features are transferred to MER task to adapt to the unique characteristics of MEs. Our approach demonstrates state-of-the-art performance through extensive experiments on three public micro-expression datasets.