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

TL;DR

GraphAU-Pain introduces a graph neural network approach to model facial Action Units and their relationships, improving interpretability and accuracy in pain intensity estimation from facial expressions.

Contribution

It presents a novel graph-based framework for modeling Action Units and their interactions, enhancing pain assessment accuracy and interpretability.

Findings

Achieved 66.21% F1-score on UNBC dataset.

Attained 87.61% accuracy in pain intensity estimation.

Demonstrated improved interpretability over existing methods.

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.