## 1 Introduction  
Emotions are fundamental to human existence, arising from complex interactions between conscious experiences, bodily sensations, and behavioral responses [1; 2]. Their accurate recognition and analysis are critical in diverse fields, impacting mental health assessment [3; 4], human-computer interaction [1; 2], social security [4], and even artistic interpretation [5; 6; 7; 8]. However, emotions' definition, measurement, and classification remain challenging despite their undeniable importance. Many models and theories offer diverse perspectives, contributing to the complexity of understanding these intricate phenomena. Images are a vital medium for emotional expression, readily interpreted by humans with speed and ease. However, extracting accurate emotional content from images presents a significant challenge for machines. This difficulty arises from the inherent subjectivity and nuance in emotional expression, often influenced by cultural backgrounds, individual experiences, and contextual factors. Facial Expression Recognition (FER), a subfield of affective computing, typically categorizes emotions in images of real faces. Conversely, recognizing emotions in artistic portraits introduces unique challenges. Unlike purely visual representations, emotion in a portrait encompasses artistic style, cultural background, and the intentions of both the artist and the subject. The stylized presentation of faces, coupled with unique colors, textures, and the possibility of exaggeration, distinguishes them from natural expressions [9; 10]. A comprehensive analysis of emotion in portraits, coupled with the study of portrait FER, promises to greatly enhance research in both art and machine learning for numerous reasons: (1) Underexplored field: portrait painting remains notably underexplored in emotion recognition research, but it offers profound insights into the content, creation process, aesthetic experience, psychological and physiological responses, artist's emotions, and social and environmental context [5]. Detecting emotions within portraits is pivotal for comprehending the intricate dynamics between artists and their works and the interaction between viewers and the art pieces [11]. (2) Unique task: recognizing emotions in portraits presents a unique challenge due to their stylized and complex nature, which diverges significantly from real human emotions and requires specialized recognition systems. (3) Complexity and robustness: compared to natural faces, the variation in color and texture in portraits presents a challenge for FER [9; 10]. Nonetheless, this diversity could ultimately enhance the robustness of recognition models by increasing their adaptability to various artistic styles and expressions. Studying emotions in portraits offers a unique and valuable opportunity to deepen our understanding of art and human feelings, alongside posing challenges that can promote advancements in machine learning and emotion recognition technologies. Popular Facial Expression Recognition (FER) models and datasets are typically based on actual faces and use categorical labels for emotions, which can lead to uncertainty and overlap between categories. This situation makes the development of specialized instruments and methodologies necessary to identify emotions in portrait art accurately. Existing FER models and datasets, predominantly trained on actual faces and categorized by emotion labels, may not capture the subtleties and complexities of emotional expression in artistic representations [12]. Another limitation is the inconsistency in the annotation methods, which leads to variability in labeling the same expressions by different annotators. Last but not least, low-quality images in existing FER datasets may blur the details of facial expressions, especially in artistic drawings. However, a three-dimensional (3D) Dominance-Valence-Arousal (DVA) space can effectively depict the nuanced and dynamic nature of emotional expressions.