# Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection

arXiv: https://arxiv.org/abs/1910.10334 · HTML: https://mlsanity.com/paper/1910.10334/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 25 of 25)

- [Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition](https://mlsanity.com/paper/2205.01782/) (2022)
- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation](https://mlsanity.com/paper/2210.15878/) (2022)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection](https://mlsanity.com/paper/2308.07770/) (2023)
- [GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features](https://mlsanity.com/paper/2211.12482/) (2022)
- [Self-supervised Facial Action Unit Detection with Region and Relation Learning](https://mlsanity.com/paper/2303.05708/) (2023)
- [Two-stage Temporal Modelling Framework for Video-based Depression Recognition using Graph Representation](https://mlsanity.com/paper/2111.15266/) (2021)
- [AU-aware graph convolutional network for Macro- and Micro-expression spotting](https://mlsanity.com/paper/2303.09114/) (2023)
- [Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning](https://mlsanity.com/paper/2408.00644/) (2024)
- [MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection](https://mlsanity.com/paper/2204.01349/) (2022)
- [Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample](https://mlsanity.com/paper/2410.01251/) (2024)
- [Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis](https://mlsanity.com/paper/2203.12570/) (2022)
- [Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting](https://mlsanity.com/paper/2403.15994/) (2024)
- [Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition](https://mlsanity.com/paper/2102.12154/) (2021)
- [Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition](https://mlsanity.com/paper/2204.00379/) (2022)
- [A Survey of Deep Learning for Group-level Emotion Recognition](https://mlsanity.com/paper/2408.15276/) (2024)
- [CausalAffect: Causal Discovery for Facial Affective Understanding](https://mlsanity.com/paper/2512.00456/) (2025)
- [Occlusion-Robust FAU Recognition by Mining Latent Space of Masked Autoencoders](https://mlsanity.com/paper/2212.04029/) (2022)
- [Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition](https://mlsanity.com/paper/2107.14399/) (2021)
- [Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling](https://mlsanity.com/paper/2205.08787/) (2022)
- [Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning](https://mlsanity.com/paper/2205.14643/) (2022)
- [Global-to-local Expression-aware Embeddings for Facial Action Unit Detection](https://mlsanity.com/paper/2210.15160/) (2022)
- [HypomimiaCoach: An AU-based Digital Therapy System for Hypomimia Detection & Rehabilitation with Parkinson's Disease](https://mlsanity.com/paper/2410.09772/) (2024)
- [MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition](https://mlsanity.com/paper/2604.17899/) (2026)

## Abstract (arXiv)

Most existing AU detection works considering AU relationships are relying on probabilistic graphical models with manually extracted features. This paper proposes an end-to-end deep learning framework for facial AU detection with graph convolutional network (GCN) for AU relation modeling, which has not been explored before. In particular, AU related regions are extracted firstly, latent representations full of AU information are learned through an auto-encoder. Moreover, each latent representation vector is feed into GCN as a node, the connection mode of GCN is determined based on the relationships of AUs. Finally, the assembled features updated through GCN are concatenated for AU detection. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for facial AU detection. The proposed framework is also validated through a series of ablation studies.