## 2 Related Works
### FER Datasets
#### 2.3.2 Portrait FER Datasets  
Art has long been recognized as a powerful medium for evoking emotions, sparking contemplation, and fostering a profound connection between the artist and the audience [11]. Affective computing introduces computational methodologies, often based on machine learning and computer vision, to decode the emotional content embedded in artworks. In the realm of visual arts, affective computing may involve analyzing emotion expressed through facial expressions in portraits and other elements such as color palettes,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Emotion** & **Dimension** & **Images** & **Annotations** \\ \hline CK+ [35] & 6 & 1 & 327 & Anger, Contempt, Disgust, Fear, Joy, and Sadness \\ FER+ [48] & 7 & 1 & 35,887 & Angery, Disgusted, Fear, Happy, Sad, Surprised, and Neutral. \\ AffectNet [46] & Continuous & 2 & 1,000,000+ & Basic emotions with valence and arousal, age, gender, and ethnicity. \\ EmotiW [43] & 6 & 1 & 1339 & Angery, Disgust, Fear, Happy, Sad, and Surprise \\ BP4D [44] & 8 & 1 & 7500 & Happiness, Sadness, Startle, Embarrassment, Fear, Physical Pain... \\ SEWA [47] & Continuous & 2 & 2000 min videos & Valence and Arousal \\ MMI [39] & 7 & 1 & 2900 min videos & MMI contains recordings of the full temporal pattern of facial expressions. \\ DISFA [37] & Intensity of 12 AUs & 1 & 130,000 video frames & continuous annotation of graded changes \\ AF3 dE [52] & 8 + Continuous & 3 & 927 & Neutral, Happy, Sadness, Surprise, Fear, Disgust, Anger, Contempt... \\ \hline
**PE3D(Ours)** & 8 + Continuous & 3 & 1136 & Single emotion labels and 3D labels \\ \hline \hline \end{tabular}
\end{table}
Table 3: Review of FER datasets (adapted from [52]).  
Figure 3: Annotation inconsistency of the same image in FER2013 [40] and FER+ [48].  
brushstroke techniques, and composition, enabling the discernment of emotional themes and expressions within paintings or sculptures.  
Portrait painting, a domain still in its infancy of academic exploration, has begun to reveal its potential through recent studies. For instance, the research conducted by [PERSON] (2022) [68] delves into identifying mixed emotions within realistic facial expressions, analyzing eleven self-portraits by [PERSON]. This study asserts that [PERSON]'s self-portraits serve as a valuable resource for examining mixed emotions, owing to their authentic depiction of varied emotions pertinent to conceivable scenarios. [PERSON] (2017) [69] applies an emotion face recognition algorithm to British portrait art from 1500 to 1910, aiming to detect and measure the presence of the six fundamental human emotions alongside neutral expressions (see Table 4). The absence of diversity in art portrait emotion recognition datasets can lead to biased models. Art frequently embodies complex and nuanced emotions, eluding simple categorization into predefined labels. Discordant emotions or subtle expressions present significant challenges for models regarding recognition and classification. Understanding and interpreting these complex emotional cues necessitates advanced techniques and more comprehensive datasets.