  Micro-expression recognition (\textbf{MER}) has attracted lots of
researchers' attention in a decade. However, occlusion will occur for MER in
real-world scenarios. This paper deeply investigates an interesting but
unexplored challenging issue in MER, \ie, occlusion MER. First, to research MER
under real-world occlusion, synthetic occluded micro-expression databases are
created by using various mask for the community. Second, to suppress the
influence of occlusion, a \underline{R}egion-inspired \underline{R}elation
\underline{R}easoning \underline{N}etwork (\textbf{RRRN}) is proposed to model
relations between various facial regions. RRRN consists of a backbone network,
the Region-Inspired (\textbf{RI}) module and Relation Reasoning (\textbf{RR})
module. More specifically, the backbone network aims at extracting feature
representations from different facial regions, RI module computing an adaptive
weight from the region itself based on attention mechanism with respect to the
unobstructedness and importance for suppressing the influence of occlusion, and
RR module exploiting the progressive interactions among these regions by
performing graph convolutions. Experiments are conducted on handout-database
evaluation and composite database evaluation tasks of MEGC 2018 protocol.
Experimental results show that RRRN can significantly explore the importance of
facial regions and capture the cooperative complementary relationship of facial
regions for MER. The results also demonstrate RRRN outperforms the
state-of-the-art approaches, especially on occlusion, and RRRN acts more robust
to occlusion.
