Work

Face Mask Detection Model Using Convolutional Neural Network

Year: 2023

Type: article

Abstract: In current times, after the rapid expansion and spread of the COVID-19 outbreak globally, people have experienced severe disruption to their daily lives. One idea to manage the out-break is to enforce people wear a face mask in public places. Therefore, automated and efficient face detection methods are essential for such enforcement. In this paper, a face mask detection model for images has been presented which classifies the images as “with mask” and “without mask”. The model is trained and evaluated using the three datasets Real-World Masked Face Dataset (RMFD), Simulated Masked Face Dataset (SMFD), and Labeled Faces in the Wild (LFW), and attained a performance accuracy rate of 99.72% for first dataset, and 100% for the second and third datasets. This work can be utilized as a digitized scanning tool in schools, hospitals, banks, and airports, and many other public or commercial locations.

Source: Machine Learning and Applications An International Journal

DOI: 10.5121/mlaij.2023.10303

Authors: Mamdouh M. Gomaa, Mahmoud M. Elsherif, Alaa Elnashar, Alaa M. Zaki

Institutions: Minia University, Sinai University

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4387654820

FWCI:

Cites: 16

Cited by:

Related to: 10