FedFV: federated face verification via equivalent class embeddings

Lingyun Liu, Yifan Zhang, Haoyuan Gao, Xingtao Yu, Jian Cheng

Abstract: Face verification models based on centralized training on large face datasets have achieved excellent performance on various test benchmarks. However, due to the increasingly sophisticated privacy protection law, centrally collecting large amount of face images becomes more difficult. We consider learning a face verification model in the federated setting, where each client has access to the face images of only one class and class embeddings cannot be shared to other clients because of data privacy. In this paper, we propose Federated face verification (FedFV), in which server transfers some equivalent class embeddings to clients so that the clients’ class embeddings can be separated far away from each other. We show that our proposed method FedFV outperforms the existing approaches in several face verification benchmarks.