Facial Expression Recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the upcoming 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at CVPR2024. In the facial expression recognition task, The limited size of the FER dataset poses a challenge to the expression recognition model's generalization ability, resulting in subpar recognition performance. To address this problem, we employ a semi-supervised learning technique to generate expression category pseudo-labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to learn and capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved outstanding results on the official validation set, a result that fully confirms the effectiveness and competitiveness of our proposed method.

翻译：面部表情识别（FER）在计算机视觉领域扮演着关键角色，并广泛应用于多个领域。本文旨在介绍我们针对即将于CVPR2024举办的第六届野外情感行为分析（ABAW）竞赛所提出的方法。在面部表情识别任务中，FER数据集规模有限，对表情识别模型的泛化能力构成挑战，导致识别性能欠佳。为解决此问题，我们采用半监督学习技术为未标注的人脸数据生成表情类别伪标签。同时，我们对标注的面部表情样本进行均匀采样，并实施去偏反馈学习策略，以应对数据集中的类别不平衡问题以及半监督学习中可能存在的数据偏差。此外，为弥补仅从静态图像获取特征的局限性与偏差，我们引入时间编码器来学习并捕捉相邻表情图像特征之间的时序关系。在第六届ABAW竞赛中，我们的方法在官方验证集上取得了优异结果，这充分证实了所提方法的有效性与竞争力。