## 2 Related Works
### Image Emotion Prediction/Annotation
#### 2.2.1 Categorical Emotion States  
[PERSON] and [PERSON] proposed six fundamental emotions [53] through a cross-cultural investigation [54]. Most prevalent Facial Expression Recognition (FER) models adhere to categorical frameworks, where facial expressions are categorized into these foundational emotions. Furthermore, many existing datasets and methodologies rely on static emotion categories, leading to inconsistencies and ambiguities in labeling. To overcome this, researchers have explored multiple annotation techniques such as Emotion Intensity Learning (EIL) [55; 56] as illustrated in Figure 1a; Multilabel Learning (ML) [57] combines several basic emotions to describe complex emotions, such as datasets: BU-3 DFE [51], JAFFE [50], and EmotioNet [15]; and Emotion Distribution Learning (EDL) [57; 58], which aim to capture emotional expressions' complex nature more effectively (see Figure 1b) [57].  
Gradations of Emotion Intensity. Recognizing these intensity gradations across various emotions gives us a more nuanced understanding of the human emotional landscape. This knowledge is crucial for developing more sophisticated models of emotion recognition in artificial intelligence and promoting greater emotional awareness and regulation in individuals. For instance, emotions are often categorized into discrete labels like \"happiness\" or \"surprise\", and it is essential to recognize that they exist on a spectrum of intensity. Understanding these gradations is crucial for capturing the nuances of human emotional experience. It has significant implications for fields like affective computing and artificial intelligence, where accurate emotion recognition is paramount. Here's a closer look at the intensity levels of some common emotions, drawing upon the work of [PERSON] [59], who proposed a psych-evolutionary theory of emotion.  
For instances, happiness can manifest in various intensities, ranging from subtle contentment to overwhelming euphoria [60]. From low-level to high-level intensity, Happiness can be categorized and defined as contentment, joy, elation, and euphoria/ecstasy [59]. Anger is a complex emotional response to perceived threats or provocations, whether real or imagined. It is distinct from other negative emotions, such as fear, sadness, or disgust, due to its unique ability to motivate action rather than avoidance [61]. Anger can be categorized into mild irritation, frustration, rage, and fury [61]. Fear, an adaptive response to perceived threats, can range from mild apprehension, anxiety, and terror to panic [62]. Sadness, a natural response to loss or disappointment, spans a spectrum from mild disappointment, sorrow, and despair to profound grief [63].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Annotation Method** & **Description** & **Metrics** \\ \hline AF3 dE [52] & Single Label \& 3D emotions & Use 3 dimensions (Dominance, valence, and arousal) to estimate the emotion expression & Cross-Entropy, L1, L2, Euclidean Distance \\ \hline
**PE3D(ours)** & Single Label \& 3D emotions & Use 3 dimensions (Dominance, valence, and arousal) to estimate the emotion expression & Cross-Entropy, L1, L2, Euclidean Distance \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 1: **(a) EIL: Emotion (happy) that indicates intensity displays with four levels (low, normal, high, and very high) [51]. (b) EDL. All the expression classes are identified along with their respective prediction values (diagram and data from [52]).**