Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909141342093312 |
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| author | Yu, Jun Wei, Zhihong Cai, Zhongpeng Zhao, Gongpeng Zhang, Zerui Wang, Yongqi Xie, Guochen Zhu, Jichao Zhu, Wangyuan |
| author_facet | Yu, Jun Wei, Zhihong Cai, Zhongpeng Zhao, Gongpeng Zhang, Zerui Wang, Yongqi Xie, Guochen Zhu, Jichao Zhu, Wangyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11942 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling Yu, Jun Wei, Zhihong Cai, Zhongpeng Zhao, Gongpeng Zhang, Zerui Wang, Yongqi Xie, Guochen Zhu, Jichao Zhu, Wangyuan Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2403.11942 |