Face mask recogniser using image processing and computer vision approach

Abstract

The world saw a health crisis with the onset of the COVID-19 virus outbreak. The mask has been identified as the most efficient way to prevent the spread of virus [1]. This has driven the necessity for a face mask recogniser that not only detects the presence of a mask but also gives the accuracy to which a person is wearing the face mask. Also, the face mask should be recognised in all angles as well. The goal of this study is to create a new and improved real time face mask recogniser using image processing and computer vision approach. A Kaggle dataset which consisted of images with and without masks was used. For the purpose of this study a pre-trained convolutional neural network Mobile Net V2 was used. The performance of the given model was assessed. The model presented in this paper can detect the face mask with 98% precision. This Face mask recogniser can efficiently detect the face mask in side wise direction which makes it more useful. A comparison of the performance metrics of the existing algorithms is also presented. Now with the spread of the infectious variant OMICRON, it is necessary to implement such a robust face mask recogniser which can help control the spread.