# Emotion Recognition in Horses with Convolutional Neural Networks
## 1 Introduction  
There is currently no scientific consensus on defining emotion since an emotion is a subjective mental state associated with the nervous system [1], but there is growing research in emotions in animals related to defining emotions as subjective affect that creates both physiological and behavioral responses [2]. There is extensive research in the human world regarding emotional affects and the effects on behavior and emotional expressions (most of which are based on subjective interviews or standardized questionnaires). As humans, we can gauge emotions based on facial cues, voice tone, posture, and other hints [3]. However, our prediction might be wrong, and a person who appears to be happy may in reality be sad [4, 5]. This suggests that observers can perceive emotions from the subject but that the truth of the emotion may be lost in interpretation. Animals experience emotions in a similar way that can be just as difficult to discern. Since [PERSON], who was one of the first to write about this topic, many scientists have done research in the field of animal emotions. [PERSON], one of the pioneers in affective neuroscience, identified seven different emotions that animals can feel [6]. Most of the research has been done in mammals because of their similarity to humans and because many of them produce facial expressions that bear a clear resemblance to the expressions seen in humans [7]. There is also growing research indicating the emotional breadth of animals and the ability to measure it through similar physiological and behavioral measures [2, 8]. While thefield of research on dog emotions has continued to flourish, research in agricultural animal emotions is growing at a slower rate. There is, however, a large body of research supporting the use of heart rate variability (HRV) in farm animals and the correlation with cortisol (as an indicator of stress) and behavioral responses as a means of assessing emotional states in domestic livestock [9; 10]. Horses are still considered livestock by the United States Department of Agriculture (USDA) and have also been studied for correlations between physiological markers (HRV and cortisol) and behavioral patterns, suggesting that there is a possibility of assessing equine emotional states through behavioral indicators. Studies in stress behaviors, for example, link elevated plasma cortisol levels with increased HRV and specific behavioral patterns, such as elevated head and neck position, widened eyes, variations in ear positions (ranging from forward to indicate alert/attentive to pinned back against the head to indicate higher levels of distress), and increased muscle tension and body movements [11; 12; 13]. Furthermore, existing ethograms in equine behaviors of feral and semi-feral horse herds show that behavioral expression in feral herds also supports the connection between behavioral expression, intention, and potential emotional affect when taken in the context of social interactions and communication [14; 15; 16]. Correlations between physiological measurements and behavioral parameters have already been used to study the psychological and emotional welfare of horses [17; 18; 19] and qualitative measures of behavioral indicators have also been used to assess equine emotional state [20; 21]. Furthermore, with the development of the Equine Facial Action Coding System (EquiFACS) [22] and the Equine Pain Face coding system [23], researchers and practitioners have become even more aware of the ability to look at muscles in both the body and face to assess expressions of physical and psychological health, especially related to pain and discomfort. 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].