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

• URL: http://arxiv.org/abs/2505.19802v1
• Date: Mon, 26 May 2025 10:35:42 GMT
• Title: GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation
• Authors: Zhiyu Wang, Yang Liu, Hatice Gunes,
• Abstract summary: Existing data-driven methods of detecting pain from facial expressions are limited due to interpretability and severity.<n>By utilizing a graph neural network, our framework offers improved interpretability and significant performance gains.<n>Experiments conducted on the publicly available UNBC dataset demonstrate the effectiveness of the GraphAU-Pain.
• Score: 14.267177649888994
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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