Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection
This work addresses facial expression analysis for human-computer interaction, but it is incremental as it builds on existing methods with minor improvements.
The paper tackles facial action unit detection by proposing a single-stage framework that combines local region perception, graph-based relationship learning, and feature fusion, achieving competitive results in the CVPR 2023 ABAW competition.
Human affective behavior analysis plays a vital role in human-computer interaction (HCI) systems. In this paper, we introduce our submission to the CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW). We propose a single-stage trained AU detection framework. Specifically, in order to effectively extract facial local region features related to AU detection, we use a local region perception module to effectively extract features of different AUs. Meanwhile, we use a graph neural network-based relational learning module to capture the relationship between AUs. In addition, considering the role of the overall feature of the target face on AU detection, we also use the feature fusion module to fuse the feature information extracted by the backbone network and the AU feature information extracted by the relationship learning module. We also adopted some sampling methods, data augmentation techniques and post-processing strategies to further improve the performance of the model.