Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems

被引:0
|
作者
Gao, Xizhan [1 ]
Hu, Wei [1 ]
Chu, Yu [1 ]
Niu, Sijie [1 ]
机构
[1] Univ Jinan, Sch Informat Sci & Engn, Jinan 250022, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 07期
基金
中国国家自然科学基金;
关键词
IoT monitoring system; video face recognition; recognition performance optimization; attention mechanism; anchor point; large-size video; SPARSE REPRESENTATION; FACE RECOGNITION; IMAGE; EIGENFACES; FUSION;
D O I
10.3390/app13074209
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems.
引用
收藏
页数:14
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