Privacy Preserving Palmprint Recognition via Federated Metric Learning

Huikai Shao; Chengcheng Liu; Xiaojiang Li; Dexing Zhong

  Deep learning-based palmprint recognition methods have made good progress and obtained promising performance. However, most of them are mainly focused on continuously improving the recognition accuracy, while ignore the privacy preserving, which is also extremely significant. In this paper, we propose a novel Federated Metric Learning (FedML) method to address the issue of data privacy and data islands in palmprint recognition. There are several clients with different structures deployed in communities, which cannot access the private data of others. The key is to improve the accuracy of each client by generating understandable knowledge and transferring it to each other but without explicitly sharing its private data or model architecture. A public dataset is introduced and several effective communication losses are constructed at both instance level and relation level to help clients to learn from each other. Furthermore, transfer learning is applied to close the gap between private and public data. Extensive experiments are conducted on eighteen constrained and unconstrained palmprint benchmark datasets. The results demonstrate that FedML can outperform other methods by a large margin and obtain promising performance.