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

• URL: http://arxiv.org/abs/2505.20033v2
• Date: Tue, 27 May 2025 07:26:21 GMT
• Title: EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition
• Authors: Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby, Felix Friedrich, Maurice Kraus, Krishna Kalyan, Kourosh Nadi, Huu Nguyen, Kristian Kersting, Sören Auer,
• Abstract summary: EmoNet Face is a comprehensive benchmark suite for developing and evaluating AI systems.<n>A novel 40-category emotion taxonomy captures finer details of human emotional experiences.<n>Three large-scale, AI-generated datasets with explicit, full-face expressions.<n>EmpathicInsight-Face is a model achieving human-expert-level performance on our benchmark.
• Score: 18.8101367995391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 built EmpathicInsight-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.

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