# Real-Time Analysis of Facial Expressions for Mood Estimation
## 2 Affective States: Emotions and Moods  
To the present date, the most studied affective elements are emotions. In fact, the definition of the term affective computing, coined by [PERSON], refers to it as the field that \"relates to, arises from, or deliberately influences emotions\" [18]. In everyday life people refer to emotions as discrete categories, which have been defined by different psychologists [19; 20; 21]. The most popular categorization is the one of basic or universal emotions: joy, sadness, anger, disgust, fear, and surprise. Nonetheless, discrete lists of emotions fail to describe the range of emotions that occur in natural communication settings. An alternative is a dimensional description [22; 23; 24], where an affective state is characterized in terms of a number of dimensions rather than in terms of a small number of discrete categories. These dimensions include evaluation, activation, control, power, among others. The idea originated from the observation that some emotions share characteristics that can be seen as different degrees of two or more dimensions. Therefore they do not need to be labelled and categorized, constraining their study and measurement.  
There are also other affective traits that have been explored in affective computing, among which we find mood. According to [PERSON] [25], mood states are defined as \"frequent, relatively long and pervasive, but typically milder in intensity than emotions\". Others like [PERSON] et al. [26] suggest that \"pre-existing mood increases the intensity of affectively congruent emotions while dampening the intensity of incongruent emotions independent of attributional knowledge\". Thus, it is noted that the main difference between emotions and moods is the temporal nature of the latter.  
As it can be seen, some of the works that dealt with multimodal and continuous recognition of affect used the dimensional Pleasure-Arousal (or Valence-Arousal) space for emotion representation. Instead, other researchers used the extended Pleasure-Arousal-Dominance space (PAD), proposed by [PERSON] [27] for emotion recognition. Among these works we find the one of [PERSON] and [PERSON] [28], who introduced a method for affect-based video segmentation taking as cues the visual and background music information of the videos.  
The PAD model is a framework that allows the definition and measurement of different emotional states, emotional traits, and personality traits in terms of three nearly orthogonal dimensions: Pleasure (P), Arousal (A), and Dominance (D). The dimension Pleasure-displeasure distinguishes positive affective states from negative ones. Arousal is defined in terms of a combination of mental alertness and physical activity. Dominance-submissiveness is defined in terms of control versus lack of control over events, one's surroundings, or other people. Thus, from the intersection of the Pleasure, Arousal, and Dominance axes, eight octants can be derived, which represent mood categories (Table 1).  
The PAD model can be used for defining moods and it allows their interrelation with the facial coding in FACS. In other words, PAD can describe a mood in terms of AUs. Particularly, [PERSON] et al. [29] defined the correspondence between AUs and the PAD space octants by means of the PAD model. The main objective of this correspondence was the description of each of the eight moods in AU terms. The Facial Expression Repertoire is known for this description [30]. From this mapping a set of rules were obtained, which computed the activation areas and intensities of each AU in PAD. As a result, they obtained a general mapping of AUs into regions of the PAD space.