对于物联网应用程序的安全和分散的混合多面识别.
Erëza Abdullahu1, Holger Wache2, Marco Piangerelli1,3
1Computer Science Division, School of Science and Technology, University of Camerino, Via Madonna Delle Carceri 7, 62032 Camerino, Italy.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究介绍了一种混合系统,用于物联网 (IoT) 环境中高效的私人多面识别. 这种轻量级的框架在资源有限的设备上实现了超过95%的准确性,提高了安全性和可扩展性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 边缘计算 边缘计算
背景情况:
- 智能环境和物联网增加了对高效,私人多人脸识别的需求.
- 集中式系统面临着物联网中的延迟,可扩展性和安全问题.
- 对于资源有限的物联网设备,需要分散的解决方案.
研究的目的:
- 展示一个轻量级的混合系统,用于物联网中的分散式多面部识别.
- 验证拟议的物联网约束框架的可行性和有效性.
- 提供保护隐私和可扩展的边缘AI解决方案.
主要方法:
- 使用预训练的卷积神经网络 (VGG16) 进行特征提取.
- 使用支持矢量机 (SVM) 进行轻量分类.
- 在资源有限的设备上实现,如物联网摄像头和Raspberry Pi.
主要成果:
- 在定制数据集上实现了超过95%的平均准确性.
- 在各种条件下同时成功识别多个面孔.
- 在边缘设备上展示了实时识别能力.
结论:
- 混合系统有效用于物联网中的去中心化多面部识别.
- 该架构将计算负载和服务器依赖性降至最低.
- 该解决方案增强了隐私,并为监控和访问控制提供了可扩展的边缘人工智能方法.
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