# FEAFA+: An Extended Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation

arXiv: https://arxiv.org/abs/2111.02751 · HTML: https://mlsanity.com/paper/2111.02751/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond](https://mlsanity.com/paper/1804.10938/) (2018)
- [AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection](https://mlsanity.com/paper/1812.05788/) (2018)
- [RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations](https://mlsanity.com/paper/2008.05196/) (2020)
- [FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation](https://mlsanity.com/paper/1904.01509/) (2019)

## Built on by (most cited first, 7 of 7)

- [ExpLLM: Towards Chain of Thought for Facial Expression Recognition](https://mlsanity.com/paper/2409.02828/) (2024)
- [AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels](https://mlsanity.com/paper/2410.22506/) (2024)
- [NeutrEx: A 3D Quality Component Measure on Facial Expression Neutrality](https://mlsanity.com/paper/2308.09963/) (2023)
- [AU-Blendshape for Fine-grained Stylized 3D Facial Expression Manipulation](https://mlsanity.com/paper/2507.12001/) (2025)
- [Towards Unified Facial Action Unit Recognition Framework by Large Language Models](https://mlsanity.com/paper/2409.08444/) (2024)
- [AUHead: Realistic Emotional Talking Head Generation via Action Units Control](https://mlsanity.com/paper/2602.09534/) (2026)
- [Efficient Expression Neutrality Estimation with Application to Face Recognition Utility Prediction](https://mlsanity.com/paper/2402.05548/) (2024)

## Abstract (arXiv)

Nearly all existing Facial Action Coding System-based datasets that include facial action unit (AU) intensity information annotate the intensity values hierarchically using A--E levels. However, facial expressions change continuously and shift smoothly from one state to another. Therefore, it is more effective to regress the intensity value of local facial AUs to represent whole facial expression changes, particularly in the fields of expression transfer and facial animation. We introduce an extension of FEAFA in combination with the relabeled DISFA database, which is available at https://www.iiplab.net/feafa+/ now. Extended FEAFA (FEAFA+) includes 150 video sequences from FEAFA and DISFA, with a total of 230,184 frames being manually annotated on floating-point intensity value of 24 redefined AUs using the Expression Quantitative Tool. We also list crude numerical results for posed and spontaneous subsets and provide a baseline comparison for the AU intensity regression task.