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license: apache-2.0
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### **[AAAI 2026] Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis**
**Dataset Summary**
FEA-20K is a large-scale, fine-grained dataset for Facial Emotion Analysis (FEA), containing approximately 20,000 samples. It was created using the novel **Facial-R1** framework, a three-stage training process designed to align reasoning and recognition in Vision-Language Models.
The dataset is built to support explainable AI by breaking down emotion analysis into three distinct but interrelated sub-tasks. It was generated with a low-cost iterative process, starting from only 300 high-quality seed samples and using reinforcement learning to synthesize a large, high-quality corpus.
**Supported Tasks**
The dataset is designed to benchmark models on three core tasks:
* **Facial Emotion Recognition**: Classifying the primary emotion of a facial image (e.g., "disgust", "happiness").
* **Facial Action Unit (AU) Recognition**: Detecting the presence of specific facial muscle movements (e.g., AU4: brow lowerer).
* **AU-based Emotion Reasoning**: Generating natural language explanations that link the detected AUs to the final emotion prediction, explaining *why* a certain emotion was recognized.
**Dataset Structure**
* **Total Samples**: ~20,000
* **Training Set**: 17,737 samples automatically constructed via the Facial-R1 synthesis strategy.
* **Test Set**: 1,688 high-quality samples that have been manually verified for accuracy.
**Citation**
If you use this dataset in your research, please cite the original paper:
```
@misc{wu2025facialr1aligningreasoningrecognition,
title={Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis},
author={Jiulong Wu and Yucheng Shen and Lingyong Yan and Haixin Sun and Deguo Xia and Jizhou Huang and Min Cao},
year={2025},
eprint={2511.10254},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2511.10254},
}
``` |