# Emotion Identification in Movies through Facial Expression Recognition
## 2 Related Work  
### Emotion Description and Representation  
In their exploratory work, [PERSON] argued [1] that facial expressions are universal and provide sufficient information to predict emotions. His studies suggest that emotions evolved through natural selection into a limited and discrete set of basic emotions: anger, disgust, fear, happiness, sadness, and surprise. Each emotion is independent of the others in its behavioral, psychological and physiological manifestations, and each is born from the activation of unique areas in the central nervous system. The criterion used was the assumption that each primary emotion has a distinct facial expression that is recognized even between different cultures [2]. This proposal set the grounds to other studies that tried to expand the set of emotions to non-basic ones, such as fatigue, anxiety, satisfaction, confusion, or frustration (e.g., [PERSON], [PERSON]'s Model of Emotion (OCC)) [3; 4; 5]. To bridge emotion theory and visual observations from facial expressions, [PERSON] also proposed a Facial Action Coding System (FACS) [6]. FACS is an anatomically based system used to describe all visually discernible movement of face muscles, from which it is possible to objectively measure the frequency and intensity of facial expressions using a scale based on Action Unit (AU), i.e., the smallest distinguishable unit of measurable facial movement, such as brow lowering, eyes blinking or jaw dropping. The system has a total of 46 action units, each with a five-point ordinal scale, used to measure the degree of contraction. FACS is strictly descriptive and does not include an emotion correspondence. Therefore, the same authors proposed an Emotional Facial Action Coding System (EMFACS) [7] based on the six-basic discrete emotion model, thus making a connection between emotions and facial expressions. Recent studies have proposed a new classification system based on simple and compound emotions [8]. Despite being the dominant theory in psychology and neuroscience research, recent studies have pointed out some limitations in the six-basic emotion's model. Certain facial expressions are associated with more than one emotion, which suggests that the initial proposed taxonomy is not adequate [9]. Other studies suggest that there is no correlation between the basic emotions and the automatic activation of facial muscles [10], while other claims suggest that this model is culture-specific and not universal [11]. These drawbacks caused the emergence of additional methods that intend to be more exhaustive and universally accepted regarding emotion classification. Some studies have assessed people's difficulty in evaluating and describing their own emotions, which points out that emotions are not discrete and isolated entities, but rather ambiguous and overlapping experiences [12]. This line of thought reinforced a dimensional model of emotions, which describes them as a continuum of highly interrelated and often ambiguous states. The model that gathered the most consensus among researchers--the Circumplex Model of Emotion--argues that there are two fundamental dimensions: valence, which represents the hedonic aspect of emotion (that is, how pleasurable it is for the human being), and arousal, which represents an enthusiasm or tension dimension, (i.e., the energy level) [13]. Hence, each emotion is represented using coordinates in a multi-dimensional state. Other approaches propose either bi-dimensional [14] or tri-dimensional (e.g., Pleasure-Arousal (PA) and Pleasure-Arousal-Dominance (PAD) [15]) models for representing emotions. The utility of a third dimension remains unclear, as several studies revealed that the valence and arousal axes are sufficient to model emotions, particularly when handling emotions induced by videos [16]. However, [PERSON], after proposing a model with four dimensions, concluded that the optimal number of dimensions depends on the specificity of the targeted application/study [17].