  Facial micro-expressions indicate brief and subtle facial movements that
appear during emotional communication. In comparison to macro-expressions,
micro-expressions are more challenging to be analyzed due to the short span of
time and the fine-grained changes. In recent years, micro-expression
recognition (MER) has drawn much attention because it can benefit a wide range
of applications, e.g. police interrogation, clinical diagnosis, depression
analysis, and business negotiation. In this survey, we offer a fresh overview
to discuss new research directions and challenges these days for MER tasks. For
example, we review MER approaches from three novel aspects: macro-to-micro
adaptation, recognition based on key apex frames, and recognition based on
facial action units. Moreover, to mitigate the problem of limited and biased ME
data, synthetic data generation is surveyed for the diversity enrichment of
micro-expression data. Since micro-expression spotting can boost
micro-expression analysis, the state-of-the-art spotting works are also
introduced in this paper. At last, we discuss the challenges in MER research
and provide potential solutions as well as possible directions for further
investigation.
