# Emotion Recognition in Horses with Convolutional Neural Networks
## 2 Materials and Methods  
### Defining a Horse Emotion Tracking Framework  
The first step consists of defining the different emotions, independent of interactions with other horses. Physical markers, head, ear, and neck positions are based on research linking behaviors for arousal and physiological stress with only \"annoyed\", suggesting a negative valence based on previous studies on animal interactions and agonistic behaviors [14; 15; 16; 21; 32; 33; 34]. The emotion of \"alarmed\" was used to indicated heightened arousal based on eye and ear position using existing ethograms with the understanding that the behavioral indicators of this emotion may also indicate heightened vigilance or alertness in addition to alarm [16]. The four emotional markers of \"alarmed\", \"annoyed\", \"curious\", and \"relaxed\" were chosen due to their representative variations of arousal level and emotional expression within the existing equid ethograms, with \"relaxed\" representing the lowest level of arousal and \"alarmed\" representing the highest. The initial photographs were marked and defined by the researchers according to existing ethograms. The term \"alarmed\" refers to a heightened state of awareness in which the horse demonstrated behaviors indicating higher arousal levels without significant movement of the feet. Horses have a wide range of facial expressions used to express psychological states as well as communicate with other conspecifics, especially with eyes, ears, and head and neck position [15; 16; 35; 36; 37; 38]. Although previous studies of horses have investigated their facial expressions in specific contexts, e.g., pain, until now, there has been no methodology available that documents all the possible facial movements of the horse and provides a way to record all potential facial configurations. This is essential for an objective description of horse facial expressions across a range of contexts that reflect different emotional states. Facial action coding systems (FACSs) provide a systematic methodology of identifying and coding facial expressions on the basis of underlying facial musculature and muscle movement across species [29]. FACSs are anatomically based and document all possible facial movements rather than a configuration of movements associated with a particular situation. Consequently, FACSs can be applied as a tool for a wide range of research questions. The EquineFacial Action Coding System (EquiFACS) provides a system to measure facial expressions in horses based on musculature and skeletal configurations in equids [22]. On its own, EquiFACS enables researchers to look at distinctive facial movements and changes and, when combined with species-specific knowledge of behaviors, contexts, and behavioral ethograms, creates opportunities to develop connections between these facial configurations and emotional expressions [22]. Portions of EquiFACS focus on the appearance of the sclera (the whites of the eyes), shape of the eye, and tension in the nose, lip, and muzzle, and ear position based on the tension and use of different facial muscles in the horse. EquiFACS also looks at additional musculature and shape changes of the face, lip, nose, eye, and ear positions, which are easily differentiated from one another and appear as markers of behavioral patterns indicative of different levels of arousal and emotional expression in known ethograms of equine behavior [15; 16; 22; 36]. By combining distinctive expressions supported by EquiFACS with known behavioral expressions of equines, we were able to generalize basic emotional expressions of equines based on distinctive changes in nose, lip, eye, and ear positions. The reliability of others to be able to learn this system (EquiFACS) and consistently code behavioral sequences was high, and this included people with no previous experience of horses [22]. A wide range of facial movements was identified, including many that are also seen in primates and other domestic animals (dogs and cats).