  Facial palsy is unilateral facial nerve weakness or paralysis of rapid onset
with unknown causes. Automatically estimating facial palsy severeness can be
helpful for the diagnosis and treatment of people suffering from it across the
world. In this work, we develop and experiment with a novel model for
estimating facial palsy severity. For this, an effective Facial Action Units
(AU) detection technique is incorporated into our model, where AUs refer to a
unique set of facial muscle movements used to describe almost every
anatomically possible facial expression. In this paper, we propose a novel
Adaptive Local-Global Relational Network (ALGRNet) for facial AU detection and
use it to classify facial paralysis severity. ALGRNet mainly consists of three
main novel structures: (i) an adaptive region learning module that learns the
adaptive muscle regions based on the detected landmarks; (ii) a skip-BiLSTM
that models the latent relationships among local AUs; and (iii) a feature
fusion&refining module that investigates the complementary between the local
and global face. Quantitative results on two AU benchmarks, i.e., BP4D and
DISFA, demonstrate our ALGRNet can achieve promising AU detection accuracy. We
further demonstrate the effectiveness of its application to facial paralysis
estimation by migrating ALGRNet to a facial paralysis dataset collected and
annotated by medical professionals.
