PortraitEmotion3D: A Novel Dataset and 3D Emotion Estimation Method for Artistic Portraiture Analysis

• Published in: Applied sciences Vol. 14; no. 23; p. 11235
• Main Authors: Liu, Shao, Agaian, Sos, Grigoryan, Artyom
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2024
• Subjects: Artificial intelligence; attention mechanism; Computational linguistics; Computer vision; Datasets; deep learning; Emotions; Entertainment industry; image processing; Language processing; Machine learning; Methods; Natural language interfaces; Neural networks; Portraits; vehicle re-identification
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app142311235

Facial Expression Recognition (FER) has been widely explored in realistic settings; however, its application to artistic portraiture presents unique challenges due to the stylistic interpretations of artists and the complex interplay of emotions conveyed by both the artist and the subject. This study addresses these challenges through three key contributions. First, we introduce the PortraitEmotion3D (PE3D) dataset, designed explicitly for FER tasks in artistic portraits. This dataset provides a robust foundation for advancing emotion recognition in visual art. Second, we propose an innovative 3D emotion estimation method that leverages three-dimensional labeling to capture the nuanced emotional spectrum depicted in artistic works. This approach surpasses traditional two-dimensional methods by enabling a more comprehensive understanding of the subtle and layered emotions often in artistic representations. Third, we enhance the feature learning phase by integrating a self-attention module, significantly improving facial feature representation and emotion recognition accuracy in artistic portraits. This advancement addresses this domain’s stylistic variations and complexity, setting a new benchmark for FER in artistic works. Evaluation of the PE3D dataset demonstrates our method’s high accuracy and robustness compared to existing state-of-the-art FER techniques. The integration of our module yields an average accuracy improvement of over 1% in recent FER systems. Additionally, combining our method with ESR-9 achieves a comparable accuracy of 88.3% on the FER+ dataset, demonstrating its generalizability to other FER benchmarks. This research deepens our understanding of emotional expression in art and facilitates potential applications in diverse fields, including human–computer interaction, security, healthcare diagnostics, and the entertainment industry.