Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial
  Action Units Recognition
Zihan Wang Siyang Song Cheng Luo Songhe Deng Weicheng Xie Linlin Shen
Human facial action units (AUs) are mutually related in a hierarchical
manner, as not only they are associated with each other in both spatial and
temporal domains but also AUs located in the same/close facial regions show
stronger relationships than those of different facial regions. While none of
existing approach thoroughly model such hierarchical inter-dependencies among
AUs, this paper proposes to comprehensively model multi-scale AU-related
dynamic and hierarchical spatio-temporal relationship among AUs for their
occurrences recognition. Specifically, we first propose a novel multi-scale
temporal differencing network with an adaptive weighting block to explicitly
capture facial dynamics across frames at different spatial scales, which
specifically considers the heterogeneity of range and magnitude in different
AUs' activation. Then, a two-stage strategy is introduced to hierarchically
model the relationship among AUs based on their spatial distribution (i.e.,
local and cross-region AU relationship modelling). Experimental results
achieved on BP4D and DISFA show that our approach is the new state-of-the-art
in the field of AU occurrence recognition. Our code is publicly available at
https://github.com/CVI-SZU/MDHR.