## 3 The Portrait Emotion3D (PE3D) Dataset
### Dataset Properties
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **HR** & **IM** & **NR** & **PI** & **RO** & **UK** & **ND** & **Total** & **DVA Center** \\ \hline Neutral & 105 & 130 & 90 & 112 & 33 & 64 & 132 & 666 & (0, 0, 0) \\ Happy & 3 & 10 & 2 & 2 & 14 & 0 & 14 & 45 & (0.5, 0.05, 0.21) \\ Sadness & 11 & 6 & 5 & 17 & 1 & 0 & 13 & 53 & (\(-\)0.62, \(-\)0.33, \(-\)0.05) \\ Surprise & 14 & 14 & 18 & 8 & 4 & 47 & 4 & 109 & (\(-\)0.27, 0.01, 0.5) \\ Fear & 6 & 8 & 9 & 14 & 3 & 12 & 8 & 60 & (\(-\)0.68, \(-\)0.11, 0.43) \\ Disgust & 5 & 5 & 7 & 2 & 3 & 4 & 6 & 32 & (0.22, \(-\)0.3, 0.34) \\ Anger & 3 & 5 & 4 & 10 & 0 & 0 & 28 & 50 & (\(-\)0.39, \(-\)0.31, 0.21) \\ Contempt & 16 & 26 & 12 & 5 & 58 & 0 & 4 & 121 & (0.68, \(-\)0.16, 0.26) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Emotion distribution of different art movements. Emotion distribution and number of images in each art movement. Figure 8: Examples of PE3D. The 3D labels are dominance, valence, and arousal (D, V, A). (**a**) Happy (IM), (0.49, 0.23, 0.28). (**b**) Sad (IM), (\(-\)0.56, \(-\)0.38, 0.14). (**c**) Surprise (IM), (\(-\)0.55, \(-\)0.01, 0.29). (**d**) Fear (IM), (\(-\)0.43, \(-\)0.41, 0.15). (**e**) Happy (HR), (0.55, 0.08, 0.17). (**f**) Happy (IM), (0.51, 0.14, 0.16). (**g**) Happy (IM), (0.51, 0.18, 0.24). (**h**) Happy (RO), (0.51, 0.21, 0.14). (**i**) Surprise (RO), (0.02, 0.03, 0.28). (**j**) Surprise (UK), (\(-\)0.26, \(-\)0.09, 0.7). (**k**) Fear (NR), (\(-\)0.7, \(-\)0.23, 0.61). (**l**) Fear (UK), (\(-\)0.27, \(-\)0.07, 0.46). Figure 9 demonstrates the superiority of 3D labels (DVA) in the recognition and quantification of facial emotions. Panels (a) and (b) of Figure 9 depict faces categorized as \"Sad\" and \"Anger\", respectively. Notably, the emotion displayed in panel (b) embodies a mixture of both sadness and anger, a nuance that categorical labels fail to represent. The DVA system's three-dimensional coordination successfully illuminates this complexity. In Figure 9e,f, the ambiguous emotional expression in panel (b) is situated near the midpoint between the \"Sad\" and \"Anger\" coordinates in the DVA space, symbolizing its dual nature. Conversely, the expression in panel (a) is positioned closer to the \"Sad\" endpoint, affirming its predominantly sad emotional content. Further examination of panels (c) and (d) in Figure 9, both tagged as \"Happy\", underscores the inability of categorical labels to distinguish varying intensities within a single emotional category. Here, the DVA system proves invaluable: the expression in panel (c) is assigned a higher valence value (0.37) compared to that in panel (d) (0.1), quantitatively capturing the degree of happiness and offering a nuanced understanding that categorical labels cannot provide. Overall, the use of 3D labels via DVA coordination affords a nuanced and precise representation of facial emotions, transcending the limitations of traditional categorical labeling and enhancing our understanding of emotional complexity. Artist Analysis: The images were annotated with the names of the artists who created them, identified through manual searches using Google Image and ChatGPT vision technology. Each cropped face was labeled accordingly. In instances where information about the artist or relevant web resources was unavailable, the label 'Unknown' was used. Figure 10 shows the distribution of the number of collected paintings from different artists. The relationship between three-dimensional (3D) labels and artist styles is detailed in Table 7 and Figure 11, which illustrates the average 3D emotion values attributed to the artists. It has been noted that artists identified with the Rococo style manifest the highest dominance values, outperforming artists associated with other styles, despite exhibiting negative valence values, which are still higher than those found in other categories. Impressionism is ranked next, with eminent artists such as [PERSON], [PERSON], and [PERSON] displaying comparable dominance and valence values.