  The recently developed Equine Facial Action Coding System (EquiFACS) provides
a precise and exhaustive, but laborious, manual labelling method of facial
action units of the horse. To automate parts of this process, we propose a Deep
Learning-based method to detect EquiFACS units automatically from images. We
use a cascade framework; we firstly train several object detectors to detect
the predefined Region-of-Interest (ROI), and secondly apply binary classifiers
for each action unit in related regions. We experiment with both regular CNNs
and a more tailored model transferred from human facial action unit
recognition. Promising initial results are presented for nine action units in
the eye and lower face regions.
