Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition

Understand

Facial Expression Recognition (FER) is an important task in computer vision and has wide applications in human-computer interaction, intelligent security, emotion analysis, and other fields.

• However, the limited size of FER datasets limits the generalization ability of expression recognition models, resulting in ineffective model performance.
• To address this problem, we propose a semi-supervised learning framework that utilizes unlabeled face data to train expression recognition models effectively.
• Our method uses a dynamic threshold module (\textbf{DTM}) that can adaptively adjust the confidence threshold to fully utilize the face recognition (FR) data to generate pseudo-labels, thus improving the model's ability to model facial expressions.