Federated Multidomain Learning With Graph Ensemble Autoencoder GMM for Emotion Recognition

Facial expression cognition technology continues to face challenges from certain perspectives despite the fact that there have been significant recent learning advances in computer vision in the areas involving posture, orientation, and viewing mode of photos or videos that affects the device performance. In particular, the current distributed machine learning schemes do not consider the privacy issue in face monitoring data. Hence, this paper proposes a new federated learning framework for unsupervised multidomain face recognition of postexercise. It is a graph AE design base to ensure multiple edge devices can cooperate with each other to ensure the optimization of the common objective function of the model to enhance the efficiency and speed of the global model. In addition, a multidomain learning loss function is proposed to share the common feature representation with other related tasks to improve domain adaptability. Adversarial learning is used to improve the recognition effect of the federated framework in each domain. The proposed scheme is validated on different multidomains expression datasets and the experimental results indicate a 19% higher F1 score than the benchmark scheme in multidomain face recognition tasks.