Alaa Adham Ibrahim, Yara Arjuman Hashim, Truska Mustafa Omer, Rebin M. Ahmed
{"title":"Face Mask Detection System using Convolutional Neural Network","authors":"Alaa Adham Ibrahim, Yara Arjuman Hashim, Truska Mustafa Omer, Rebin M. Ahmed","doi":"10.1109/IEC54822.2022.9807543","DOIUrl":null,"url":null,"abstract":"According to the World Health Organization, the coronavirus also known as the COVID-19 pandemic, is causing a global health crisis, WHO and Other health organizations recommended wearing face masks as a protection measurement especially in work, school, university, malls, and any public places. The proposed system consists of a Convolutional Neural Network-based system that can recognize faces with masks and without a mask, The dataset used for this project consists of 3835 images of two types of images, people with masks and people without masks. By utilizing the TensorFlow framework we’re able to get a 98.80% accuracy rate in training time while training the model with 200 epochs.","PeriodicalId":265954,"journal":{"name":"2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEC54822.2022.9807543","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
According to the World Health Organization, the coronavirus also known as the COVID-19 pandemic, is causing a global health crisis, WHO and Other health organizations recommended wearing face masks as a protection measurement especially in work, school, university, malls, and any public places. The proposed system consists of a Convolutional Neural Network-based system that can recognize faces with masks and without a mask, The dataset used for this project consists of 3835 images of two types of images, people with masks and people without masks. By utilizing the TensorFlow framework we’re able to get a 98.80% accuracy rate in training time while training the model with 200 epochs.