Face Recognition and Mask Detection Technology for Surveillance Systems

S. Srinivasan, R. Sundar, Hemavathi S, Sumathi Sokkanarayanan, Mithileysh Sathiyanarayanan
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Abstract

Corona pandemic has affected the daily routine of life disturbing the trade and economic globally. Wearing a mask has become compulsory and a new tradition. within the close to future, several suppliers can raise the shoppers to wear masks properly. Therefore, detection of face mask has become one of the important tasks to assist the international society. This paper provides a easy and simplified approach to detect the face masks using some of the important Machine Learning packages like TensorFlow, Keras, OpenCV and Scikit-Learn. The projected methodology detects the face from the image properly and so identifies if it's a mask thereon or not. As a police work task performing artist, it may detect a face together with a mask in motion. the tactic gives an accurate output with an accuracy of 96.77% on dataset. The model tendency to find the optimized values of parameters are employed using Convolutional Neural Network (CNN) model to identify whether the masks are worn properly or not while not inflicting over-fitting.
监控系统中的人脸识别和掩码检测技术
冠状病毒大流行影响了日常生活,扰乱了全球贸易和经济。戴口罩已经成为一种强制性的新传统。在不久的将来,一些供应商可以让购物者正确佩戴口罩。因此,口罩检测已成为国际社会协助的重要任务之一。本文提供了一种简单的方法来检测人脸蒙版,使用一些重要的机器学习包,如TensorFlow, Keras, OpenCV和Scikit-Learn。投影方法从图像中正确地检测人脸,从而识别其是否为掩模。作为一名警察工作任务的表演艺术家,它可以在运动中探测到一张带着面具的脸。该策略在数据集上给出了准确的输出,准确率为96.77%。利用卷积神经网络(CNN)模型寻找参数最优值的模型倾向,在不造成过拟合的情况下识别口罩佩戴是否正确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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