Emotion Recognition in Horses with Convolutional Neural Networks

TL;DR

This paper presents a proof-of-concept system using convolutional neural networks to recognize emotions in horses, achieving promising accuracy and enabling applications in animal welfare and veterinary fields.

Contribution

It introduces a novel two-component CNN-based system for detecting horse emotions from images, combining horse detection and emotion prediction.

Findings

80% accuracy on validation set

65% accuracy on test set

demonstrates feasibility of autonomous emotion recognition in animals

Abstract

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.