# Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition

arXiv: https://arxiv.org/abs/2205.00380 · HTML: https://mlsanity.com/paper/2205.00380/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 67 references that are in the map.
- First posted on arXiv 1 May 2022.

## Builds on (most cited first, 21 of 21)

- [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)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)
- [Learning-based Video Motion Magnification](https://mlsanity.com/paper/1804.02684/) (2018)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods](https://mlsanity.com/paper/1511.00423/) (2015)
- [Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition](https://mlsanity.com/paper/2006.09674/) (2020)
- [Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition](https://mlsanity.com/paper/1805.08417/) (2018)
- [LEARNet Dynamic Imaging Network for Micro Expression Recognition](https://mlsanity.com/paper/1904.09410/) (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)
- [Objective Classes for Micro-Facial Expression Recognition](https://mlsanity.com/paper/1708.07549/) (2017)
- [Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks](https://mlsanity.com/paper/2012.04927/) (2020)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Micro-expression spotting: A new benchmark](https://mlsanity.com/paper/2007.12421/) (2020)
- [Objective Class-based Micro-Expression Recognition through Simultaneous Action Unit Detection and Feature Aggregation](https://mlsanity.com/paper/2012.13148/) (2020)
- [A comparative study on movement feature in different directions for micro-expression recognition](https://mlsanity.com/paper/2102.08068/) (2021)
- [A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework](https://mlsanity.com/paper/1901.07765/) (2019)

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

- [Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition](https://mlsanity.com/paper/2310.04664/) (2023)
- [Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks](https://mlsanity.com/paper/2403.17175/) (2024)
- [Dual Stream Independence Decoupling for True Emotion Recognition under Masked Expressions](https://mlsanity.com/paper/2603.16760/) (2026)
- [Micro-AU CLIP: Fine-Grained Contrastive Learning from Local Independence to Global Dependency for Micro-Expression Action Unit Detection](https://mlsanity.com/paper/2603.16302/) (2026)
- [STAG: Spatio-temporal Evolving Structural Representation of Action Units for Micro-expression Recognition](https://mlsanity.com/paper/2606.28083/) (2026)
- [MoTop: Motion-Topological Model For Micro AU Detection](https://mlsanity.com/paper/2609.31285/) (2026)

## Abstract (arXiv)

Micro-expression recognition (MER) is valuable because micro-expressions (MEs) can reveal genuine emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged in unrelated information. Instead, the facial landmark is a low-dimensional and compact modality, which achieves lower computational cost and potentially concentrates on ME-related movement features. However, the discriminability of facial landmarks for MER is unclear. Thus, this paper explores the contribution of facial landmarks and proposes a novel framework to efficiently recognize MEs. Firstly, a geometric two-stream graph network is constructed to aggregate the low-order and high-order geometric movement information from facial landmarks to obtain discriminative ME representation. Secondly, a self-learning fashion is introduced to automatically model the dynamic relationship between nodes even long-distance nodes. Furthermore, an adaptive action unit loss is proposed to reasonably build the strong correlation between landmarks, facial action units and MEs. Notably, this work provides a novel idea with much higher efficiency to promote MER, only utilizing graph-based geometric features. The experimental results demonstrate that the proposed method achieves competitive performance with a significantly reduced computational cost. Furthermore, facial landmarks significantly contribute to MER and are worth further study for high-efficient ME analysis.