Please use this identifier to cite or link to this item:
Title: Face Mask-Wearing Classification Using Transfer Learning Technique with MobileNet V2
Authors: Pham, Nguyen Minh Nhut
Keywords: COVID-19
masked face detection
face mask classification
face mask recognition
Deep Learning
Issue Date: Nov-2022
Publisher: Tạp chí Khoa học và Công nghệ Trường Đại học Quảng Bình
Abstract: The COVID-19 pandemic has adversely affected the global economy, politics, society, and other areas. It has dramatically led to a loss of human lives worldwide and is continuing to have a heavy impact on people's health and living conditions. In order to avoid the spread of this pandemic, mask-wearing is required by law or regulation in most places, especially indoors public places. Using Deep Learning techniques to detect and authenticate people wearing masks which can help recognize patterns or behavior of the public, contributing to limiting the rapid spread of COVID-19 pandemic is becoming effective, beneficial, and widespread. In spite of the fact that wearing face masks incorrectly will not protect ourselves, especially children, from virus transmission and will reduce the effectiveness of COVID-19 prevention, a limited study has been done to solve the problem of mask-wearing. We use the technique of transfer learning with MobileNetV2 to train the model from the Face Mask Label Dataset (FMLD) and Flickr Faces HQ (FFHQ) dataset to not only detect if a mask is used or not, but also classify the status of mask wearing over the faces. The results show that the trainning accuracy of experiment model is 99%
Description: Tạp chí Khoa học và Công nghệ, Trường Đại học Quảng Bình; T.11, S.4. trang 117-125.
ISSN: 0866-7683
Appears in Collections:NĂM 2022

Files in This Item:

 Sign in to read

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.