计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230800043-9.doi: 10.11896/jsjkx.230800043
王晨卓1, 鲁艳蓉1,2, 沈剑3
WANG Chenzhuo1, LU Yanrong1,2, SHEN Jian3
摘要: 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。
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