laion/emonet-face-binary

EmoNet-Face: A Fine-Grained, Expert-Annotated Benchmark for Facial Emotion Recognition

Dataset Summary

EmoNet-Face is a comprehensive benchmark suite designed to address critical gaps in facial emotion recognition (FER). Current benchmarks often have a narrow emotional spectrum, lack demographic diversity, and use uncontrolled imagery. EmoNet-Face provides a robust foundation for developing and evaluating AI systems with a deeper, more nuanced understanding of human emotions.

This work was accepted at NeurIPS 2025.

Key Contributions (as recognized by reviewers):

• A Novel 40-Category Taxonomy: A fine-grained emotion taxonomy meticulously derived from foundational psychological research (the "Handbook of Emotions") to capture a rich spectrum of human emotional states.
• High-Quality Synthetic Data: Large-scale datasets generated with state-of-the-art text-to-image models, ensuring clear, full-face expressions with controlled, balanced representation across ethnicity, age, and gender.
• Rigorous Expert Annotation: All evaluation and fine-tuning datasets are annotated by psychology experts, ensuring high-quality, trustworthy labels.
• Reduced Risk: As a fully synthetic dataset, EmoNet-Face mitigates the privacy and consent risks associated with datasets of real individuals.

This repository contains the EmoNet-Face Binary dataset.

Dataset Structure

Data Fields

Each instance in the dataset includes the following fields:

• image: A PIL-compatible image object of the facial expression.
• prompt: The full text prompt used to generate the image, including emotion and demographic details.
• demographics: A dictionary containing the parsed demographic attributes (e.g., ethnicity, gender, age).
• annotations: The emotion annotations for the image. The structure varies by dataset split.

Data Splits and Annotation Format

• For emonet-face-hq (Benchmark/Test Set):

  • Purpose: This dataset is the primary benchmark for evaluation.
  • Size: 2,500 images.
  • Annotations: Each image was annotated by four different psychology experts. The annotations field contains a list of dictionaries, where each dictionary represents one expert's ratings across all 40 emotion categories on a continuous intensity scale from 0 (absent) to 7 (very strong).

• For emonet-face-binary (Fine-tuning Set):

  • Purpose: This dataset is designed for fine-tuning models.
  • Size: 19,999 images.
  • Annotations: Annotations were collected via a multi-stage binary agreement protocol. The annotations field contains binary labels (present/absent) for specific emotions, confirmed by up to three experts to ensure high-consensus positive and negative samples.

• For emonet-face-big (Pre-training Set):

  • Purpose: This large-scale dataset is intended for model pre-training.
  • Size: 203,201 images.
  • Annotations: The annotations field contains synthetically generated labels from a VLM (Gemini-2.5-Flash) designed to provide broad coverage across the 40-category taxonomy.

Dataset Creation

Curation Rationale

The dataset was created to overcome the limitations of existing FER benchmarks, which often lack emotional granularity, demographic diversity, and annotation quality. By using synthetic imagery, we can control for these factors while eliminating contextual confounders and privacy risks.

All images underwent a manual expert review to filter for quality and artifacts. While labor-intensive, this rigorous curation was essential for creating a gold-standard benchmark and distinguishes EmoNet-Face from noisier, automatically collected datasets.

Source Data and Image Generation

The images are fully synthetic and were generated using state-of-the-art text-to-image models: Midjourney v6 (under a paid subscription) and FLUX.1 [dev]. The generation prompts were systematically engineered to ensure a balanced distribution of emotions across diverse demographics (14 ethnic groups, ages 20-80, and three gender identities).

Annotations

Annotations for emonet-face-hq and emonet-face-binary were provided by a team of 13 annotators with verified academic degrees in psychology. The annotation was performed on a custom, open-source platform.

Considerations for Using the Data

Social Impact and Responsible Use

This dataset is intended for academic and research purposes to advance the development of fair, nuanced, and empathetic AI systems.

Prohibited Uses

This dataset and any models trained on it are not intended for and must not be used in sensitive, high-stakes domains where misinterpretation could lead to significant harm. In compliance with emerging regulations like the EU AI Act (Article 5(1)(f)), prohibited uses include, but are not limited to:

• Emotion recognition in the workplace or educational institutions.
• Real-time or post-hoc surveillance in public spaces.
• Systems for law enforcement, border control, or asylum applications.
• Credit scoring, insurance risk assessment, or hiring.
• Any application that could lead to manipulation, deception, or unlawful discrimination.

User Responsibility

Downstream users are solely responsible for ensuring their applications comply with all applicable laws, regulations, and ethical guidelines. The permissive license of this dataset does not override these legal and ethical obligations.

Other Known Limitations

• Synthetic-to-Real Generalization: While the dataset is synthetic, models trained on it have shown strong generalization to real-world data. To address this common concern, we evaluated our EmpathicInsight-Face model on the real-world FERD and AffectNet datasets, with the following results:

	Anger	Contempt	Disgust	Fear	Happy	Neutral	Sad	Surprise	Avg.
FERD	73.68	31.58	78.95	100.00	100.00	84.21	78.95	78.95	78.29
AffectNet	77.05	28.75	40.53	69.08	99.25	78.96	83.94	98.70	75.72

• Subjectivity of Emotion: Some emotion categories show lower inter-annotator agreement. This is not a flaw but a feature, reflecting the inherent subjectivity and psychological complexity of emotion perception. A benchmark with 100% agreement would be unrealistic. EmoNet-Face captures this genuine ambiguity, making it a more robust tool for training AI.

• Static Images: The dataset consists of static images and does not capture temporal cues (e.g., microexpressions, the evolution of an expression). This is a valuable direction for future work.

• Cross-Cultural Scope: While we controlled for diverse ethnic representation in the imagery, the 40-category taxonomy is primarily grounded in Western-centric psychological literature. Its universality is an open question and an important area for future cross-cultural research.

Additional Information

Licensing Information

This dataset is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license.

We are confident in our right to apply this license based on the terms of the source text-to-image models used for generation:

• Midjourney: Images were created under a paid subscription. The Midjourney Terms of Service (Section 4, "Content Rights") state: "You own all Assets You create with the Services to the fullest extent possible under applicable law." This ownership grants us the right to release these images under CC BY 4.0.
• FLUX.1 [dev]: The license for this model explicitly distinguishes between the model (non-commercial use) and its outputs. The FLUX.1 [dev] license (Section 2.d, “Outputs”) states: "We claim no ownership rights in and to the Outputs... You may use Output for any purpose (including for commercial purposes)..."

Citation Information

If you use this dataset in your research, please cite our paper:

@misc{emonetface2025,
      title={EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition}, 
      author={Christoph Schuhmann and Robert Kaczmarczyk and Gollam Rabby and Felix Friedrich and Maurice Kraus and Krishna Kalyan and Kourosh Nadi and Huu Nguyen and Kristian Kersting and Sören Auer},
      year={2025},
      eprint={2505.20033},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.20033}, 
}