A review of micro-expression spotting: methods and challenges

Micro-expression (ME) can reflect a person's authentic internal thoughts and thus have significant research value in many fields. With the continuous development of computer technology, ME analysis methods have gradually shifted from psychological-based to computer vision-based in recent years. ME spotting, a critical branch in ME analysis, has received more and more attention. Some review papers on ME have been published in recent years. However, most of them focused mainly on ME recognition and lacked a detailed study of ME spotting. Therefore, this paper attempts to conduct a comprehensive review of the latter. Specifically, we first summarize the research scope of ME spotting and introduce the current spontaneous ME datasets. Then, we review the ME spotting methods from two aspects: apex detection and interval detection. We further classify ME interval detection into handcrafted-based and deep learning-based. The characteristics and limitations of each technique are discussed in detail. After that, evaluation metrics and the experimental comparison of these methods followed. In addition, we discuss the challenges in ME spotting and outline the possible directions for future research. We hope this review article could assist researchers in better understanding ME spotting.