PDF(Pubmed)
Abstract：
Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.
摘要：
手指静脉识别方法,作为新兴的生物识别技术，由于其高精度和实时检测功能，在身份验证方面引起了越来越多的关注。然而,随着隐私保护意识的提高，传统的集中式手指静脉识别算法面临隐私和安全问题。联合学习，一种分布式训练方法，在不跨端点共享数据的情况下保护数据隐私，正在逐步推广和应用。然而，它的性能受到数据集之间异质性的严重限制。为了解决这些问题,提出了一种双解耦的手指静脉识别个性化联邦学习框架(DDP-FedFV)。DDP-FedFV方法结合了泛化和个性化。在第一阶段,DDP-FedFV方法实现了涉及模型和特征解耦的双重解耦机制，以优化特征表示并增强全局模型的泛化性。在第二阶段,DDP-FedFV方法实现了个性化的权重聚合方法，联邦个性化重量比降低(FedPWRR)，基于数据分布信息优化参数聚合过程,从而增强客户模型的个性化。为了评估DDP-FedFV方法的性能，基于六个公共手指静脉数据集进行了理论分析和实验。实验结果表明，该算法在不增加通信成本和隐私泄露风险的情况下优于集中式训练模型。
