# Multimodal Emotion Recognition Using Visual, Vocal and Physiological Signals: A Review
## 3 Human Emotion Categorization Models  
The method of representing emotion is essential for understanding affective computing models. There is no common agreement on a unique categorization of emotions, and no ultimate emotion categorization covers all emotions. Psychologists have developed many different affect models. However, three groups of emotion representation models are so far applicable to emotion computing in applications such as affective human-computer interfaces, namely discrete emotion models, dimensional models, and componential models [49,50]. Some emotional states (e.g., shame or guilt) do not have expressions that humans can recognize in human-to-human communication, and they cannot be recognized using current technology. Discrete emotion models or categorical classification approaches such as [PERSON]'s model [51,52] and [PERSON]'s model [53] define a discrete set of emotions. [PERSON]'s model represents six basic emotion states with universal facial expressions according to [PERSON]'s theory (the so-called universal emotion states are anger, disgust, happiness, sadness, fear  
Figure 2: PRISMA flow diagram. and surprise). A non-emotion-neutral state is added to the emotion recognition task. Each facial emotion state is defined by a combination of action units (components of muscle movements). Another example of categorical representations of emotions is the 27 categories of emotions bridged with smooth gradients [54], where emotion is represented in a semantic space (categories, factor loading). Complex emotions are derived from a number of basic emotions. There are different componential models, such as [PERSON]'s model [55]. Dimensional emotion models characterize emotion as a multi-dimensional signal with several dimensions. They utilize continuous values instead of dividing emotions into several categories like the discrete emotion representation model [56]. The dimensional models classify emotions in detail using multiple dimensions of emotion. One example of these models is the wheel of emotions [57, 58], which proposes eight basic bipolar emotions, with intensity presented on three levels. The wheel of emotions also defines combinations of emotions (e.g., love is a combination of joy and trust). A three-dimensional model of affect, called the valence, arousal, dominance (VAD) model (also known either as pleasure, arousal, dominance model (PAD) or evaluation, activation, power (EAP)) [59, 60] represents a multi-dimensional emotion in three independent dimensions. The valence, pleasure or evaluation defines positive or negative emotion and expresses the pleasant or unpleasant. The arousal or activation ranges from sleep/passive to excitement/active and represents the degree of activation. Dominance or power varies from submissive to dominant and indicates the perceived level of control of an emotional state. A two-dimensional valence-arousal model of emotions, also referred to as the circumplex model of affect [61], is the most commonly used for recognition tasks. Since the values of each dimension can vary continuously, the subtle differences between different emotions can be distinguished, and the evolution process of emotional states can be tracked via real-time labeling of emotional states. [PERSON] [62] used the valence-arousal (or evaluation-activation) representation across different scales. Other dimensional emotion models are the Ortony, Clore and Collins (OCC) model [63] and [PERSON]'s model [64]. The hourglass model of emotions [65, 66] argues that emotions are distributed in an hourglass space. The model is an emotion categorization model optimized for polarity detection. The model has empirical evidence in the context of sentiment analysis. The model is a biologically inspired and psychologically motivated emotion categorization model and represents affective states both using labels and four independent but concomitant affective dimensions that can potentially describe the full range of human emotional experiences. [PERSON] et al.