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. In the ABAW5 EXPR task, our method achieved excellent results on the official validation set.

翻译：面部表情识别（Facial Expression Recognition, FER）是计算机视觉领域的一项重要任务，在人机交互、智能安防、情感分析等领域具有广泛应用。然而，FER数据集规模有限，制约了表情识别模型的泛化能力，导致模型性能不佳。针对这一问题，我们提出了一种半监督学习框架，能够有效利用无标签人脸数据训练表情识别模型。我们的方法采用动态阈值模块（\textbf{DTM}），该模块可自适应调整置信度阈值，从而充分利用人脸识别（FR）数据生成伪标签，提升模型对面部表情的建模能力。在ABAW5 EXPR任务中，我们的方法在官方验证集上取得了优异的结果。