面向公平性联邦学习的指纹识别算法

Study on Fingerprint Recognition Algorithm for Fairness in Federated Learning

现有的指纹识别方法大多是基于机器学习,在对海量数据集中训练时忽视了数据本身的隐私性和异质性,从而导致用户信息泄漏和识别率降低。为在隐私保护下协同优化模型精度,提出了一个全新的基于联邦学习的指纹识别算法(Federated Learning-Fingerprint Recognition,Fed-FR)。首先,通过联邦学习迭代聚合来自各终端的参数,从而提高全局模型的性能;其次,将稀疏表示理论用于低质量指纹图像去噪处理,来增强指纹的纹理结构;再次,针对客户端异构而导致的分配不公问题,提出基于水库抽样的客户端调度策略;最后,在3个真实数据集上进行仿真实验,对Fed-FR的有效性进行对比分析。实验结果表明,Fed-FR精度比局部学习提高5.32%,比联邦平均算法提高8.56%,接近于集中学习的精度;在隐私保护水平、评估准确率及可扩展性等方面具有良好的表现。研究成果首次展现了联邦学习与指纹识别结合的可行性,增强了指纹识别算法的安全性和可扩展性,给联邦学习应用于生物识别技术提供了参考。

Most existing fingerprint recognition methods rely on machine learning,which neglects the privacy and heterogeneity of the data when training on massive databases,resulting in user information leakage and reduced recognition accuracy.To cooperatively optimize model accuracy under privacy protection,this paper proposes a novel fingerprint recognition algorithm based on federated learning,termed federated learning-fingerprint recognition(Fed-FR).Firstly,the algorithm iteratively aggregates parameters from each terminal through federated learning,thereby improving the performance of the global model.Secondly,sparse representation theory is applied to low-quality fingerprint image denoising to enhance the texture structure of the fingerprint.Thirdly,in response to the allocation inequity issue caused by client heterogeneity,this paper proposes a client scheduling strategy based on reservoir sampling.Finally,experimental results on three real-world databases show that Fed-FR significantly outperforms local learning by 5.32%and federated average by 8.56%,approaching the accuracy of centralized learning.The results demonstrate the effectiveness of Fed-FR in privacy protection,accuracy evaluation,and scalability.This study demonstrates for the first time the feasibility of combining federated learning with fingerprint recognition,enhancing the security and scalability of fingerprint recognition algorithms,and providing a reference for the application of federated learning in biometric technologies.