  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.
