# Emotion Recognition in Horses with Convolutional Neural Networks
## 1 Introduction
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. Technology already exists to examine emotional expressions in humans. With the improvements in technology during the last few decades, researchers have studied multiple ways of how to recognize emotions in humans using different techniques, such as Markov models, artificial neural networks, and Bayesian networks, among others. The automation of emotion recognition employs three main approaches:  
First, there are knowledge-based techniques that predict emotions based on semantic and syntactic knowledge; statistical methods, which commonly are machine learning algorithms that predict emotions given a big enough data set; and hybrid approaches, which are a combination of the previous two. Many companies have been successful in predicting emotions in humans using these approaches. A good example is the work done by Affectiva [27], a company founded at Massachusetts Institute of Technology (MIT) that uses facial and voice cues to predict emotions. So, the question arises of whether we can create an intelligent system with the ability to predict emotions of animals based on body and facial expressions. Most of the research conducted on animal emotions has been done in mammals, mainly from a psychological and neuroscience perspective [7; 28], with growing work done in looking at equine emotional facial expressions using the EquiFACs and Pain Face coding systems [22; 23]. In addition, most of the research focuses on the ability of animals to interpret our emotions and not on how we can interpret theirs. Within the group of those that focus on interpreting the expressions of the animals, most of them do it without using an intelligent or autonomous system. A good example is AnimalFacs, a tool for identifying facial movements in non-human species. The researchers explain how we, as humans, can analyze the facial expressions of different primates, dogs, cats, and horses [7; 22; 29]. There is emerging research done on how to interpret animal emotions using an autonomous or intelligent system. One example is the work done by [PERSON] and [PERSON] in predicting dog emotions from images [30]. Another example is the work done at the University of Augsburg on recognizing dog emotions from bark sequences using an autonomous model [31]. Extant facial emotional recognition software in equids focuses primarily on pain expressions [24; 25; 26], with more research needed to classify and code a wider range of emotional expressions. Thus, the idea of predicting animal emotions this way is something that has not yet been explored in depth. 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]. In order to do this, two different elements must be created. First, we need to develop a detector capable of recognizing a horse in an image. More specifically, it needs to detect a region of interest (ROI from now on), which in this case, will be the head and neck of a horse. The task of the detector is a prerequisite for the second part. If the detector does not recognize the appropriate ROI, the second part of the system will not be able to predict the emotion correctly regardless of how well it can accomplish that task. Secondly, 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. Altogether, this should be an intelligent system capable of detecting a horse, analyzing its face and neck features, and predicting its emotion with reasonable accuracy.