Exploring Facial Expression Recognition through Semi-Supervised Pre-training and Temporal Modeling

Jun Yu, Zhihong Wei, Zhongpeng Cai, Gongpeng Zhao, Zerui Zhang, Yongqi Wang, Guochen Xie, Jichao Zhu, Wangyuan Zhu, Qingsong Liu, Jiaen Liang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4880-4887

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 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 capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition our method achieved the third place in the official test set a result that fully demonstrates the effectiveness and competitiveness of our proposed method.