Automated Detection of Equine Facial Action Units
This work addresses the need for efficient facial analysis in veterinary or animal behavior research, but it is incremental as it adapts existing methods to a new domain.
The authors tackled the problem of automating the laborious manual labeling of horse facial action units by proposing a deep learning-based method, achieving promising initial results for nine action units in eye and lower face regions.
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.