Progressively Learning from Macro-Expressions for Micro-Expression Recognition

Micro-expression (ME) recognition is challenging due to the low-intensity facial motions. An idea to overcome this is learning assisted by macro-expressions (MaEs). However, the intensity gap between MaE and ME is so huge that related works fail to effectively leverage MaE’s assistance in overcoming low-intensity interference, which attempt to directly force ME knowledge to mimic MaE knowledge. In this paper, we propose that the knowledge transfer from MaE to ME can be converted into a progressive process for better implementation. Thus, we construct a progressive multi-step learning framework, which accomplishes two tasks: first, we dissect the huge intensity gap into multiple segments that are easier to bridge by constructing multiple learning steps, each corresponding to various intensity levels of expression recognition tasks. Second, through a designed self-knowledge distillation (self-KD) model, each dissected gap can be bridged, enabling the MaE knowledge to progressively transfer to guide the ME learning. Experiments carried out on three widely used databases demonstrated that the proposed PLMaM achieves state-of-the-art results.