@article{Zhou2025, 
author = {Ling Zhou and Qirong Mao and Ming Dong},
title = {Objective Class-Based Micro-Expression Recognition Through Simultaneous Action Unit Detection and Feature Aggregation},
year = {2025},
journal = {Tsinghua Science and Technology},
volume = {30},
number = {5},
pages = {2114-2132},
keywords = {Micro-Expression Recognition (MER), action unit detection, self-attention, Graph Convolutional Network (GCN)},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010095},
doi = {10.26599/TST.2024.9010095},
abstract = {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 these 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 outperforms the current state-of-the-art methods in MER. Additionally, we also report our single model-based micro-expression AU detection results.}
}