# SynPAIN: A Synthetic Dataset of Pain and Non-Pain Facial Expressions

arXiv: https://arxiv.org/abs/2507.19673 · HTML: https://mlsanity.com/paper/2507.19673/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 68 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://mlsanity.com/paper/2407.03168/) (2024)
- [Arc2Face: A Foundation Model for ID-Consistent Human Faces](https://mlsanity.com/paper/2403.11641/) (2024)
- [Py-Feat: Python Facial Expression Analysis Toolbox](https://mlsanity.com/paper/2104.03509/) (2021)
- [AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors](https://mlsanity.com/paper/2403.04697/) (2024)
- [DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer](https://mlsanity.com/paper/2406.09622/) (2024)
- [FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features](https://mlsanity.com/paper/2404.09736/) (2024)
- [Representation Learning and Identity Adversarial Training for Facial Behavior Understanding](https://mlsanity.com/paper/2407.11243/) (2024)
- [MagicFace: Training-free Universal-Style Human Image Customized Synthesis](https://mlsanity.com/paper/2408.07433/) (2024)
- [Limitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia](https://mlsanity.com/paper/1905.07446/) (2019)
- [Unobtrusive Pain Monitoring in Older Adults with Dementia using Pairwise and Contrastive Training](https://mlsanity.com/paper/2101.03251/) (2021)
- [FaceCrafter: Identity-Conditional Diffusion with Disentangled Control over Facial Pose, Expression, and Emotion](https://mlsanity.com/paper/2505.15313/) (2025)
- [Toward an Automatic System for Computer-Aided Assessment in Facial Palsy](https://mlsanity.com/paper/1910.11497/) (2019)
- [Fully-attentive and interpretable: vision and video vision transformers for pain detection](https://mlsanity.com/paper/2210.15769/) (2022)
- [Pain Detection in Masked Faces during Procedural Sedation](https://mlsanity.com/paper/2211.06694/) (2022)

## Built on by (most cited first, 3 of 3)

- [Pain in 3D: Generating Controllable Synthetic Faces for Automated Pain Assessment](https://mlsanity.com/paper/2509.16727/) (2025)
- [LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training](https://mlsanity.com/paper/2509.20786/) (2025)
- [On Applicability of Synthetic Datasets for Facial Expression Recognition](https://mlsanity.com/paper/2605.17483/) (2026)

## Abstract (arXiv)

Accurate pain assessment in patients with limited ability to communicate, such as older adults with severe dementia, represents a critical healthcare challenge. Robust automated systems of pain behavior detection may facilitate such assessments. Existing pain detection datasets, however, suffer from limited ethnic/racial diversity, privacy constraints, and underrepresentation of older adults who are the primary target population for clinical deployment. We present SynPAIN, a large-scale synthetic dataset containing 10,710 facial expression images across five ethnicities/races, representing two age groups, and two genders. Using commercial generative AI tools, we created demographically balanced synthetic identities with clinically meaningful pain expressions. Our validation demonstrates that synthetic pain expressions exhibit expected pain patterns, scoring significantly higher than neutral and non-pain expressions using clinically validated pain assessment tools based on facial action unit analysis. We experimentally demonstrate SynPAIN's utility in identifying algorithmic bias in existing pain detection models. Through comprehensive bias evaluation, we reveal substantial performance disparities across demographics characteristics. These performance disparities were previously undetectable with smaller, less diverse datasets. Furthermore, we demonstrate that age-matched synthetic data augmentation improves pain detection performance on real clinical data, achieving a 2.4 percentage point improvement in average precision. SynPAIN addresses critical gaps in pain assessment research by providing the first publicly available, demographically diverse synthetic dataset specifically designed for older adult pain detection, while establishing a framework for measuring and mitigating algorithmic bias. The dataset, code, and trained models is available at https://mmzml.github.io/SynPAIN