  Action Unit (AU) detection plays an important role for facial expression
recognition. To the best of our knowledge, there is little research about AU
analysis for micro-expressions. In this paper, we focus on AU detection in
micro-expressions. Microexpression AU detection is challenging due to the small
quantity of micro-expression databases, low intensity, short duration of facial
muscle change, and class imbalance. In order to alleviate the problems, we
propose a novel Spatio-Temporal Adaptive Pooling (STAP) network for AU
detection in micro-expressions. Firstly, STAP is aggregated by a series of
convolutional filters of different sizes. In this way, STAP can obtain
multi-scale information on spatial and temporal domains. On the other hand,
STAP contains less parameters, thus it has less computational cost and is
suitable for micro-expression AU detection on very small databases.
Furthermore, STAP module is designed to pool discriminative information for
micro-expression AUs on spatial and temporal domains.Finally, Focal loss is
employed to prevent the vast number of negatives from overwhelming the
microexpression AU detector. In experiments, we firstly polish the AU
annotations on three commonly used databases. We conduct intensive experiments
on three micro-expression databases, and provide several baseline results on
micro-expression AU detection. The results show that our proposed approach
outperforms the basic Inflated inception-v1 (I3D) in terms of an average of F1-
score. We also evaluate the performance of our proposed method on
cross-database protocol. It demonstrates that our proposed approach is feasible
for cross-database micro-expression AU detection. Importantly, the results on
three micro-expression databases and cross-database protocol provide extensive
baseline results for future research on micro-expression AU detection.
