  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. Code for the project is publicly available.
