  Creating intelligent systems capable of recognizing emotions is a difficult
task, especially when looking at emotions in animals. This paper describes the
process of designing a "proof of concept" system to recognize emotions in
horses. This system is formed by two elements, a detector and a model. The
detector is a fast region-based convolutional neural network that detects
horses in an image. The model is a convolutional neural network that predicts
the emotions of those horses. These two elements were trained with multiple
images of horses until they achieved high accuracy in their tasks. In total,
400 images of horses were collected and labeled to train both the detector and
the model while 40 were used to test the system. Once the two components were
validated, they were combined into a testable system that would detect equine
emotions based on established behavioral ethograms indicating emotional affect
through head, neck, ear, muzzle and eye position. The system showed an accuracy
of 80% on the validation set and 65% on the test set, demonstrating that it is
possible to predict emotions in animals using autonomous intelligent systems.
Such a system has multiple applications including further studies in the
growing field of animal emotions as well as in the veterinary field to
determine the physical welfare of horses or other livestock.
