# Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems
###### Abstract  
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. IoT monitoring system; video face recognition; recognition performance optimization; attention mechanism; anchor point; large-size video +
Footnote †: journal: Medical Image Analysis
(server) for storage, or even alarming. In such a manner, we can not only effectively reduce the amount of data transmission and meet the real-time requirements, but also gain higher security, because the transmission data no longer comprise the original data but the analysis results. However, this scheme has high requirements for the intelligent algorithms to be deployed, that is, the intelligent algorithms need to have fewer parameters (because the memory of the edge device is limited), faster computing speed and higher recognition accuracy. In view of this practical demand, this paper will focus on the research of video face-recognition methods applicable to IoT monitoring systems. So far, face recognition is already widely used in everyday life, including sign-in systems, fugitive tracking systems, etc. Many related algorithms have been developed [4; 5], such as the principal component analysis (PCA) algorithm [6], linear regression classification (LRC) algorithm [7], support vector machine (SVM) algorithm [8], linear discriminant analysis (LDA) algorithm [9], k-nearest neighbor (K-NN) approach [10], canonical correlation analysis (CCA) method [11], and the sparse representation-based classifications (SRC) algorithm [12]. All of these single image-based image recognition algorithms have achieved satisfactory performances; however, with the development of imaging technology, a great number of videos is being produced everyday, and how to recognize video [13] data, i.e., how to measure the distance between videos, is still a challenging problem. Many researchers have pointed out that temporal information is not important for face recognition; hence, the term video face recognition in this study refers to set-based video face recognition, i.e., where temporal information is not considered. When compared to a single image, videos and image sets contain useful information, such as varying expressions, varying poses, and varying illumination conditions, that describe the objects within in the images. Hence, it is very important to study the classification problem from the perspective of image sets, i.e., the image set classification task. The purpose of image set classification (or set-based video recognition) is to assign labels to the probe set (or video) by measuring the similarities between the gallery videos (or sets) and the probe videos (or sets).