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 the 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.
Highlights
This paper explores the possibility of creating an intelligent system capable of predicting the emotion of a horse from its face and neck traits based on existing research and ethograms connecting specific head, neck, eye, nose, and ear positions with specific stress levels and emotional valence [14,15,16,21,32,33,34]
The work resulted in a detector capable of finding a horse’s face in an image, a model capable of predicting the emotion of a horse given a picture of its head, and a user-friendly
The system does so in an autonomous way and with good results. This proves that we are capable of creating a system that can recognize the emotions of a non-human animal species that has the ability to produce facial expressions and that it might be possible to detect these emotions by other methods, such as measuring the animal’s heart rate, its temperature, or recording the sounds that they produce and feeding all this data into a system similar to the one created here [41]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Such facial recognition has been incorporated into machine learning and video coding software to help researchers and practitioners develop better techniques to decipher pain in equids [24,25,26] The use of these measures and assessment tools supports the development of standardized methods of assessing behavioral parameters and suggesting emotional affect based on existing studies in behavior, physiological measures, and species-specific ethograms. We need a model that, once it receives the detected ROI, is capable of predicting the emotion of the horse This means this model must be able to detect facial cues and different neck positions and relate them to the appropriate emotion. This should be an intelligent system capable of detecting a horse, analyzing its face and neck features, and predicting its emotion with reasonable accuracy
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