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在物联网中采用基于注意力的多模式生物识别方法

Leyu Lin¹, Yue Zhao¹, Jintao Meng¹

¹Science and Technology on Communication Security Laboratory, Chengdu 610041, China.

Sensors (Basel, Switzerland)

|July 14, 2023

概括

本研究介绍了基于注意力的多式联络生物识别 (AMBR) 网络,以提高物联网安全性. 利用联合学习 (FL),它实现了高准确性,同时保持了用户数据的隐私.

科学领域:

计算机科学计算机科学
人工智能的人工智能
网络安全网络安全

背景情况:

物联网 (IoT) 设备的扩散需要强大的访问控制机制.
单模生物识别系统在物联网应用程序的准确性和稳定性方面存在局限性.
数据隐私问题和法规阻碍了对物联网安全模型的培训数据的收集.

研究的目的:

开发一个先进的多式联网生物识别系统,用于安全的物联网访问控制.
用生物识别中的注意力机制来增强特征提取和融合.
通过采用联邦学习进行模型培训来解决物联网中的数据隐私挑战.

主要方法:

提出了一个基于注意力的多式联络生物识别 (AMBR) 网络,整合了注意力机制.
利用联合学习 (FL) 在分布式数据源上协同训练AMBR模型.
在三个试验列表中对VoxCeleb1数据集评估了AMBR网络的性能.

主要成果:

在VoxCeleb1试验列表中实现了0.68%,0.47%,0.80%的低等错误率 (EER).
与现有的最先进的生物识别方法相比,其表现优越.
在联合学习环境中的实验结果证实了AMBR-FL方法的有效性.

结论:

拟议的AMBR网络有效地提取和融合多模式生物识别特征,以改善识别.
联合学习使物联网系统的生物识别模型的隐私保护培训成为可能.
与FL相结合的AMBR网络为安全和隐私意识的物联网访问控制提供了一个有前途的解决方案.

关键词:

这就是为什么物联网物联网物联网.注意力机制注意力机制联合学习的联合学习多式联网系统多式联网系统人体识别个人识别

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