# GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation

arXiv: https://arxiv.org/abs/2505.19802 · HTML: https://mlsanity.com/paper/2505.19802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition](https://mlsanity.com/paper/2205.01782/) (2022)
- [Facial Action Unit Detection Using Attention and Relation Learning](https://mlsanity.com/paper/1808.03457/) (2018)
- [KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis](https://mlsanity.com/paper/2412.16833/) (2024)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video](https://mlsanity.com/paper/1605.00894/) (2016)
- [Uncertain Facial Expression Recognition via Multi-task Assisted Correction](https://mlsanity.com/paper/2212.07144/) (2022)
- [Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition](https://mlsanity.com/paper/2204.11053/) (2022)
- [Automated Pain Detection from Facial Expressions using FACS: A Review](https://mlsanity.com/paper/1811.07988/) (2018)
- [Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning](https://mlsanity.com/paper/1712.01496/) (2017)

## Abstract (arXiv)

Understanding pain-related facial behaviors is essential for digital healthcare in terms of effective monitoring, assisted diagnostics, and treatment planning, particularly for patients unable to communicate verbally. Existing data-driven methods of detecting pain from facial expressions are limited due to interpretability and severity quantification. To this end, we propose GraphAU-Pain, leveraging a graph-based framework to model facial Action Units (AUs) and their interrelationships for pain intensity estimation. AUs are represented as graph nodes, with co-occurrence relationships as edges, enabling a more expressive depiction of pain-related facial behaviors. By utilizing a relational graph neural network, our framework offers improved interpretability and significant performance gains. Experiments conducted on the publicly available UNBC dataset demonstrate the effectiveness of the GraphAU-Pain, achieving an F1-score of 66.21% and accuracy of 87.61% in pain intensity estimation.