Ding, Hu;
Yan, Yan;
Lu, Yang;
Xue, Jing-Hao;
Wang, Hanzi;
(2024)
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition.
IEEE Transactions on Circuits and Systems for Video Technology
10.1109/tcsvt.2024.3513973.
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Abstract
Most facial expression recognition (FER) models are trained on large-scale expression data with centralized learning. Unfortunately, collecting a large amount of centralized expression data is difficult in practice due to privacy concerns of facial images. In this paper, we investigate FER under the framework of personalized federated learning, which is a valuable and practical decentralized setting for real-world applications. To this end, we develop a novel uncertainty-Aware label refineMent on hYpergraphs (AMY) method. For local training, each local model consists of a backbone, an uncertainty estimation (UE) block, and an expression classification (EC) block. In the UE block, we leverage a hypergraph to model complex high-order relationships between expression samples and incorporate these relationships into uncertainty features. A personalized uncertainty estimator is then introduced to estimate reliable uncertainty weights of samples in the local client. In the EC block, we perform label propagation on the hypergraph, obtaining high-quality refined labels for retraining an expression classifier. Based on the above, we effectively alleviate heterogeneous sample uncertainty across clients and learn a robust personalized FER model in each client. Experimental results on two challenging real-world facial expression databases show that our proposed method consistently outperforms several state-of-the-art methods. This indicates the superiority of hypergraph modeling for uncertainty estimation and label refinement on the personalized federated FER task. The source code will be released at https://github.com/mobei1006/AMY.
| Type: | Article |
|---|---|
| Title: | Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tcsvt.2024.3513973 |
| Publisher version: | https://doi.org/10.1109/tcsvt.2024.3513973 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Facial expression recognition, Federated learning, Hypergraph Networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10202704 |
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