Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition

TL;DR

This paper introduces a novel uncertainty-aware hypergraph-based label refinement method for personalized federated facial expression recognition, effectively handling data privacy and heterogeneity across clients.

Contribution

It proposes a hypergraph-based uncertainty estimation and label propagation framework for personalized federated FER, improving robustness and accuracy.

Findings

Outperforms state-of-the-art methods on real-world FER datasets

Effectively models high-order relationships with hypergraphs

Enhances label quality and model personalization

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.