ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2505.19802
16
0
v1v2 (latest)

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

26 May 2025
Zhiyu Wang
Yang Liu
Hatice Gunes
ArXiv (abs)PDFHTML
Main:8 Pages
2 Figures
Bibliography:4 Pages
1 Tables
Abstract

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.

View on arXiv
@article{wang2025_2505.19802,
  title={ GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation },
  author={ Zhiyu Wang and Yang Liu and Hatice Gunes },
  journal={arXiv preprint arXiv:2505.19802},
  year={ 2025 }
}
Comments on this paper