Automatic Pain Detection on Horse and Donkey Faces

Recognition of pain in equines (such as horses and
donkeys) is essential for their welfare. However, this assessment
depends solely on the ability of the observer to locate visible
signs of pain since there is no verbal communication. The use
of Grimace scales is proven to be efficient in detecting pain but
is time-consuming and also dependent on the level of training
of the annotators and, therefore, validity is not easily ensured.
There is a need for automation of this process to help training.
This work provides a system for pain prediction in horses, based
on Grimace scales. The pipeline automatically finds landmarks
on horse faces before classification. Our experiments show that
using different classifiers for different poses of the horse is
necessary, and fusion of different features improves results.
We furthermore investigate the transfer of horse-based models
for donkeys and illustrate the loss of accuracy in automatic
landmark detection and subsequent pain prediction.