Explainable graph attention network for stress recognition (StressGAT) via differential action units
Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), often overlook person-specific baselines or lack the representational capacity to model the non-linear temporal progression of distress due to sequential bottlenecks and rigid grid-based constraints. Furthermore, many deep learning models lack the interpretability required for clinical deployment. This study introduces StressGAT, a Graph Attention Network that leverages the relational inductive bias of graph modeling to capture complex facial dynamics that indicate acute stress. B...
| Authors | Thomas Kassiotis, Stefanos Gkikas, Nikolaos Smyrnis et al. |
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| Primary category | cs.CV |
| Published | 23 July 2026 |
| Last updated at source | 27 July 2026 |
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