# Objective Class-based Micro-Expression Recognition through Simultaneous Action Unit Detection and Feature Aggregation

arXiv: https://arxiv.org/abs/2012.13148 · HTML: https://mlsanity.com/paper/2012.13148/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Data Leakage and Evaluation Issues in Micro-Expression Analysis](https://mlsanity.com/paper/2211.11425/) (2022)
- [Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition](https://mlsanity.com/paper/2205.00380/) (2022)
- [AULLM++: Structured-Token-Conditioned Large Language Models for Micro-Expression Action Unit Detection](https://mlsanity.com/paper/2603.08387/) (2026)
- [Prior-based Objective Inference Mining Potential Uncertainty for Facial Expression Recognition](https://mlsanity.com/paper/2411.13024/) (2024)
- [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)
- [MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition](https://mlsanity.com/paper/2604.17899/) (2026)

## Abstract (arXiv)

Micro-Expression Recognition (MER) is a challenging task as the subtle changes occur over different action regions of a face. Changes in facial action regions are formed as Action Units (AUs), and AUs in micro-expressions can be seen as the actors in cooperative group activities. In this paper, we propose a novel deep neural network model for objective class-based MER, which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks (GCN). Specifically, we propose two new strategies in our AU detection module for more effective AU feature learning: the attention mechanism and the balanced detection loss function. With those two strategies, features are learned for all the AUs in a unified model, eliminating the error-prune landmark detection process and tedious separate training for each AU. Moreover, our model incorporates a tailored objective class-based AU knowledge-graph, which facilitates the GCN to aggregate the AU-level features into a micro-expression-level feature representation. Extensive experiments on two tasks in MEGC 2018 show that our approach significantly outperforms the current state-of-the-arts in MER. Additionally, we also report our single model-based micro-expression AU detection results.