Emotion Recognition in Horses with Convolutional Neural Networks

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 faster region-based convolutional neural network that detects horses in an image. The second one, the model, is a convolutional neural network that predicts the emotion of those horses. These two models were trained with multiple images of horses until they achieved high accuracy in their tasks, creating therefore the desired system. 400 images of horses were used to train both the detector and the model while 80 were used to validate 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 between 69% and 74% on the validation set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. It is a first "proof of concept" approach that can be enhanced in many ways. 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.