Identification of a Person From Live Video Streaming Using Machine Learning in the Internet of Things (IoT)

Identification of a Person From Live Video Streaming Using Machine Learning in the Internet of Things (IoT)

Sana Zeba, Mohammad Amjad
Copyright: © 2021 |Pages: 16
DOI: 10.4018/IJMCMC.2021070104
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Abstract

In this paper, the authors develop an efficient face recognition algorithm from images or live video streaming for IoT systems based on K-nearest neighbor and support vector machine learning to recognize the person from the local database and extract the features of the face. Because of the complexity of the conditions, there might be some factors of facing errors like the size; the angle; the distance from the ear, nose, and eyes; etc. This sustainable machine learning-based IoT system is designed for sovereign face recognition with features extraction with improved accuracy near about 96%. The experimental study is done to test the performance of the face recognition in the changes of number of persons in video or images. Finally, this manuscript recognized persons from live video or images with accuracy approximately 96% by using the SVM and KNN classifiers and discussed with the block diagram and proposed algorithm.
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In this segment, the summary of the previously published literature related to the security problems of the IoT system is presented, Blockchain Technology in IoT and solutions of the IoT problems using Blockchain. This literature review has the main focus on the following aspects of the Security provisioning of IoT Network:

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