  Parkinson's Disease (PD) is a neurological disorder that affects facial
movements and non-verbal communication. Patients with PD present a reduction in
facial movements called hypomimia which is evaluated in item 3.2 of the
MDS-UPDRS-III scale. In this work, we propose to use facial expression analysis
from face images based on affective domains to improve PD detection. We propose
different domain adaptation techniques to exploit the latest advances in face
recognition and Face Action Unit (FAU) detection. The principal contributions
of this work are: (1) a novel framework to exploit deep face architectures to
model hypomimia in PD patients; (2) we experimentally compare PD detection
based on single images vs. image sequences while the patients are evoked
various face expressions; (3) we explore different domain adaptation techniques
to exploit existing models initially trained either for Face Recognition or to
detect FAUs for the automatic discrimination between PD patients and healthy
subjects; and (4) a new approach to use triplet-loss learning to improve
hypomimia modeling and PD detection. The results on real face images from PD
patients show that we are able to properly model evoked emotions using image
sequences (neutral, onset-transition, apex, offset-transition, and neutral)
with accuracy improvements up to 5.5% (from 72.9% to 78.4%) with respect to
single-image PD detection. We also show that our proposed affective-domain
adaptation provides improvements in PD detection up to 8.9% (from 78.4% to
87.3% detection accuracy).
