# AU-aware graph convolutional network for Macro- and Micro-expression spotting

arXiv: https://arxiv.org/abs/2303.09114 · HTML: https://mlsanity.com/paper/2303.09114/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [Style Aggregated Network for Facial Landmark Detection](https://mlsanity.com/paper/1803.04108/) (2018)
- [Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection](https://mlsanity.com/paper/1910.10334/) (2019)
- [SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset](https://mlsanity.com/paper/1911.01519/) (2019)
- [MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network](https://mlsanity.com/paper/2004.08915/) (2020)
- [A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challenges](https://mlsanity.com/paper/1806.05781/) (2018)
- [3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame](https://mlsanity.com/paper/2105.06340/) (2021)
- [Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos](https://mlsanity.com/paper/2106.06489/) (2021)
- [Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation](https://mlsanity.com/paper/1612.05038/) (2016)

## Built on by (most cited first, 9 of 9)

- [Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey](https://mlsanity.com/paper/2412.17616/) (2024)
- [Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting](https://mlsanity.com/paper/2403.15994/) (2024)
- [SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting](https://mlsanity.com/paper/2407.20799/) (2024)
- [Causal-Ex: Causal Graph-based Micro and Macro Expression Spotting](https://mlsanity.com/paper/2503.09098/) (2025)
- [Infused Suppression Of Magnification Artefacts For Micro-AU Detection](https://mlsanity.com/paper/2504.09326/) (2025)
- [MPFNet: A Multi-Prior Fusion Network with a Progressive Training Strategy for Micro-Expression Recognition](https://mlsanity.com/paper/2506.09735/) (2025)
- [Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition](https://mlsanity.com/paper/2603.04766/) (2026)
- [End-to-End Facial Expression Detection in Long Videos](https://mlsanity.com/paper/2504.07660/) (2025)
- [Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling](https://mlsanity.com/paper/2511.16952/) (2025)

## Abstract (arXiv)

Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs. When solving this problem, previous works generally lack in considering the structures of human faces and the correspondence between expressions and relevant facial muscles. To address this issue for better performance of ME spotting, this paper seeks to extract finer spatial features by modeling the relationships between facial Regions of Interest (ROIs). Specifically, we propose a graph convolutional-based network, called Action-Unit-aWare Graph Convolutional Network (AUW-GCN). Furthermore, to inject prior information and to cope with the problem of small datasets, AU-related statistics are encoded into the network. Comprehensive experiments show that our results outperform baseline methods consistently and achieve new SOTA performance in two benchmark datasets,CAS(ME)^2 and SAMM-LV. Our code is available at https://github.com/xjtupanda/AUW-GCN.