# MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection

arXiv: https://arxiv.org/abs/2204.01349 · HTML: https://mlsanity.com/paper/2204.01349/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 76 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [JhatAA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention](https://mlsanity.com/paper/2003.08834/) (2020)
- [Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment](https://mlsanity.com/paper/1803.05588/) (2018)
- [Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition](https://mlsanity.com/paper/1904.09939/) (2019)
- [Facial Action Unit Detection Using Attention and Relation Learning](https://mlsanity.com/paper/1808.03457/) (2018)
- [Semantic Object Parsing with Graph LSTM](https://mlsanity.com/paper/1603.07063/) (2016)
- [Deep Reasoning with Knowledge Graph for Social Relationship Understanding](https://mlsanity.com/paper/1807.00504/) (2018)
- [Deep Structure Inference Network for Facial Action Unit Recognition](https://mlsanity.com/paper/1803.05873/) (2018)
- [Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing](https://mlsanity.com/paper/1704.03067/) (2017)
- [Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition](https://mlsanity.com/paper/1910.11012/) (2019)
- [Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection](https://mlsanity.com/paper/1910.10334/) (2019)
- [Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition](https://mlsanity.com/paper/2112.05851/) (2021)
- [AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection](https://mlsanity.com/paper/1812.05788/) (2018)
- [GeoConv: Geodesic Guided Convolution for Facial Action Unit Recognition](https://mlsanity.com/paper/2003.03055/) (2020)
- [Deep Multi-Center Learning for Face Alignment](https://mlsanity.com/paper/1808.01558/) (2018)

## Built on by (most cited first, 6 of 6)

- [Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation](https://mlsanity.com/paper/2210.15878/) (2022)
- [Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning](https://mlsanity.com/paper/2408.00644/) (2024)
- [Hierarchical Vision-Language Interaction for Facial Action Unit Detection](https://mlsanity.com/paper/2602.14425/) (2026)
- [Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2405.16701/) (2024)
- [LRA-GNN: Latent Relation-Aware Graph Neural Network with Initial and Dynamic Residual for Facial Age Estimation](https://mlsanity.com/paper/2502.05423/) (2025)
- [DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding](https://mlsanity.com/paper/2610.17015/) (2026)

## Abstract (arXiv)

The Facial Action Coding System (FACS) encodes the action units (AUs) in facial images, which has attracted extensive research attention due to its wide use in facial expression analysis. Many methods that perform well on automatic facial action unit (AU) detection primarily focus on modeling various types of AU relations between corresponding local muscle areas, or simply mining global attention-aware facial features, however, neglect the dynamic interactions among local-global features. We argue that encoding AU features just from one perspective may not capture the rich contextual information between regional and global face features, as well as the detailed variability across AUs, because of the diversity in expression and individual characteristics. In this paper, we propose a novel Multi-level Graph Relational Reasoning Network (termed MGRR-Net) for facial AU detection. Each layer of MGRR-Net performs a multi-level (i.e., region-level, pixel-wise and channel-wise level) feature learning. While the region-level feature learning from local face patches features via graph neural network can encode the correlation across different AUs, the pixel-wise and channel-wise feature learning via graph attention network can enhance the discrimination ability of AU features from global face features. The fused features from the three levels lead to improved AU discriminative ability. Extensive experiments on DISFA and BP4D AU datasets show that the proposed approach achieves superior performance than the state-of-the-art methods.