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

arXiv: https://arxiv.org/abs/2505.20033 · HTML: https://mlsanity.com/paper/2505.20033/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 51 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 4 of 4)

- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Identifying Implicit Social Biases in Vision-Language Models](https://mlsanity.com/paper/2411.00997/) (2024)
- [FindingEmo: An Image Dataset for Emotion Recognition in the Wild](https://mlsanity.com/paper/2402.01355/) (2024)

## Built on by (most cited first, 6 of 6)

- [Object Detection with Multimodal Large Vision-Language Models: An In-depth Review](https://mlsanity.com/paper/2508.19294/) (2025)
- [EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection](https://mlsanity.com/paper/2506.09827/) (2025)
- [On Applicability of Synthetic Datasets for Facial Expression Recognition](https://mlsanity.com/paper/2605.17483/) (2026)
- [Knowing When Not to Answer: Abstention and Refusal Reasoning in Vision--Language Models](https://mlsanity.com/paper/2609.05540/) (2026)
- [VoiceNet: Fine-Grained Voice Understanding Beyond Emotion at Scale](https://mlsanity.com/paper/2609.32016/) (2026)
- [EmoGenie: Benchmarking and Evaluating Expressed and Evoked Emotion in T2I Generation with MLLMs](https://mlsanity.com/paper/2610.18455/) (2026)

## Abstract (arXiv)

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.