# Explainable graph attention network for stress recognition (StressGAT) via differential action units

arXiv: https://arxiv.org/abs/2607.20819 · HTML: https://mlsanity.com/paper/2607.20819/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 13 of 13)

- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [TransFER: Learning Relation-aware Facial Expression Representations with Transformers](https://mlsanity.com/paper/2108.11116/) (2021)
- [DAiSEE: Towards User Engagement Recognition in the Wild](https://mlsanity.com/paper/1609.01885/) (2016)
- [Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines](https://mlsanity.com/paper/2302.00431/) (2023)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data](https://mlsanity.com/paper/2409.06154/) (2024)
- [Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks](https://mlsanity.com/paper/2403.17175/) (2024)
- [A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities](https://mlsanity.com/paper/2607.19716/) (2026)
- [ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment](https://mlsanity.com/paper/2607.19722/) (2026)
- [Employing Multimodal Machine Learning for Stress Detection](https://mlsanity.com/paper/2306.09385/) (2023)
- [Combining Facial Videos and Biosignals for Stress Estimation During Driving](https://mlsanity.com/paper/2601.04376/) (2026)
- [Action Unit Enhance Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2507.07678/) (2025)
- [Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech](https://mlsanity.com/paper/2510.08586/) (2025)

## Built on by (most cited first, 5 of 5)

- [Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection](https://mlsanity.com/paper/2609.22095/) (2026)
- [A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2608.09088/) (2026)
- [FUSE: Frame-Unified Stress Estimation from Facial Video](https://mlsanity.com/paper/2608.10442/) (2026)
- [MUPA2E: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment](https://mlsanity.com/paper/2608.15999/) (2026)
- [UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing](https://mlsanity.com/paper/2609.29553/) (2026)

## Abstract (arXiv)

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. By using Differential Action Units, the framework normalizes individual responses relative to neutral baselines to achieve personalized recognition. The proposed model achieves 88.62% accuracy on a diverse stress-induction cohort (58 participants) using a subject-independent, Leave-One-Subject-Out (LOSO) cross-validation protocol. Beyond predictive accuracy, the architecture integrates a Multiple Instance Learning (MIL) attention mechanism to identify peak stress intervals and reveal distinct expressivity phenotypes. By simultaneously optimizing for accuracy and interpretability, this framework provides a robust, explainable solution for personalized affective monitoring.