Towards privacy palmprint recognition via federated hash learning

Nowadays, deep learning-based palmprint recognition methods have achieved great success. However, they are mainly focused on the accuracy and ignore the privacy, which is more important in the practical applications. In this Letter, a novel method, federated hash learning (FHL), is proposed for privacy palmprint recognition. There are several agents deploy in different communities, and they have different models and private data. An available public dataset is introduced to provide communications for each agent. Through appropriate federated loss, the agents are connected to help each other train the models to improve the accuracy. Experiments are conducted on constrained and unconstrained palmprint benchmarks. The results demonstrate that the authors' FHL can outperform other baselines and obtain promising accuracy.