Control The COVID-19 Pandemic: Face Mask Detection Using Transfer Learning

Abstract

Currently, in the face of the health crisis caused by the Coronavirus COVID-19 which has spread throughout the worldwide. The fight against this pandemic has become an unavoidable reality for many countries. It is now a matter involving many areas of research in the use of new information technologies, particularly those related to artificial intelligence. In this paper, we present a novel contribution to help in the fight against this pandemic. It concerns the detection of people wearing masks because they cannot work or move around as usual without protection against COVID-19. However, there are only a few research studies about face mask detection. In this work, we investigated using different deep Convolutional Neural Networks (CNN) to extract deep features from images of faces. The extracted features are further processed using various machine learning classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN). Were used and examined all different metrics such as accuracy and precision, to compare all model performances. The best classification rate was getting is 97.1%, which was achieved by combining SVM and the MobileNetV2 model. Despite the small dataset used (1376 images), we have obtained very satisfactory results for the detection of masks on the faces.

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

“…Similar with [12], the methods [118], [119] also choose SVM as the classifier in the second stage. Buciu [118] took the ratio of color channels into account to discriminate mask and no-mask images.…”

“…However, this method is sensitive to mask types, which is its potential weakness. Oumina et al [119] presented several combinations of multiple CNNs and K-NN or SVM, and conducted experiments. It indicates that the combination of MobileNetV2 and SVM achieves the best performance among the combinations, 97.11% accuracy.…”

Face Mask Detection using Python

“…This review is useful in fighting the spread of the infection and keeping away from contact with the infection. [4] This composition portrays the advancement of a framework for perceiving individuals, in any event, when they are utilizing a facial covering, from photos. An order model dependent on the MobileNetV2 design and the OpenCV's face indicator is utilized.…”

Real-Time Face Mask Detector Using Convolutional Neural Networks Amidst COVID-19 Pandemic

“…This approach achieved an exceptional accuracy of 97.14 %. In [17], the authors presented a system for detecting the presence of a compulsory medical mask in public places. The proposed system achieved 97.1% accuracy.…”