Simple but Effective In-the-wild Micro-Expression Spotting Based on Head Pose Segmentation

Micro-expressions may occur in high-stake situations when people attempt to conceal or suppress their true feelings. Nowadays, intelligent micro-expression analysis has long been focused on videos captured under constrained laboratory conditions. This is due to the relatively small number of publicly available datasets. Moreover, micro-expression characteristics are subtle and brief, and thus very susceptible to interference from external factors and difficult to capture. In particular, head movement is unavoidable in unconstrained scenarios, making micro-expression spotting highly challenging. This paper proposes a simple yet effective method for avoiding the interference of head movement on micro-expression spotting in natural scenarios by considering three-dimensional space. In particular, based on the head pose, which can be mapped to two-dimensional vectors (translations and rotations) for representation, long and complex videos could be divided into short video segments that basically exclude head movement interference. Following that, segmented micro-expression spotting is realized based on an effective short-segment-based micro-expression spotting algorithm. Experimental results on in-the-wild databases demonstrate the effectiveness of our proposed method in avoiding head movement interference. Additionally, due to the simplicity of this method, it creates opportunities for spotting micro-expressions in real-world scenarios, possibly even in real-time. Furthermore, it helps alleviate the small sample size problem in micro-expression analysis by boosting the spotting performance in massive unlabeled videos.