EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition

Effective human-AI interaction relies on AI's ability to accurately perceive and interpret human emotions. Current benchmarks for vision and vision-language models are severely limited, offering a narrow emotional spectrum that overlooks nuanced states (e. g. , bitterness, intoxication) and fails to distinguish subtle differences between related feelings (e. g. , shame vs. embarrassment). Existing datasets also often use uncontrolled imagery with occluded faces and lack demographic diversity, risking significant bias. To address these critical gaps, we introduce EmoNet Face, a comprehensive benchmark suite. EmoNet Face features: (1) A novel 40-category emotion taxonomy, meticulously derived from foundational research to capture finer details of human emotional experiences. (2) Three large-scale, AI-generated datasets (EmoNet HQ, Binary, and Big) with explicit, full-face expressions and controlled demographic balance across ethnicity, age, and gender. (3) Rigorous, multi-expert annotations for training and high-fidelity evaluation. (4) We build Empathic Insight Face, a model achieving human-expert-level performance on our benchmark. The publicly released EmoNet Face suite—taxonomy, datasets, and model—provides a robust foundation for developing and evaluating AI systems with a deeper understanding of human emotions.

Authors

• Christoph Schuhmann
• Robert Kaczmarczyk
• Gollam Rabby
• Maurice Kraus
• Felix Friedrich
• Huu Nguyen
• Kalyan Sai Krishna
• Kourosh Nadi
• Kristian Kersting
• Sören Auer AKSW Research Group University of Leipzig

Keywords

No keywords are indexed for this paper.