Dynamic Micro-Expression Recognition Using Knowledge Distillation

Micro-expression is a spontaneous expression that occurs when a person tries to mask his or her inner emotion, and can neither be forged nor suppressed. It is a kind of short-duration, low-intensity, and usually local-motion facial expression. However, owing to these characteristics of micro-expression, it is difficult to obtain micro-expression data, which is the bottleneck of applying deep learning methods to micro-expression recognition. In addition, micro-expression is still a type of expression, and it can also be encoded by the facial action coding system. Therefore, there is a certain correlation between action unit recognition and micro-expression recognition. Addressing those, we propose a novel knowledge transfer technique distills and transfers knowledge from action unit for micro-expression recognition, where knowledge from a pre-trained deep teacher neural network is distilled and transferred to a shallow student neural network. Specifically, a teacher-student correlative framework is designed with a novel objective function. And features extracted from the teacher network is used as prior knowledge to guide the student part to efficiently learning from the target micro-expression dataset. Experiments are conducted on four available published micro-expression datasets (SMIC2, CASME, CASME II, and SAMM). The experimental results show that our model outperforms the state-of-the-art systems.