Fuzzy Computing in Data Science

2A Fuzzy Approach to Face Mask Detection

Vatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy* and Sujoy Datta

School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

Abstract

With the severe outbreak of the pandemic of COVID-19, several guidelines were laid down by the CDC and WHO for the effective containment of the spread. Some of these measures were proper wearing of adequate masks, social distancing, sanitation, frequent washing of hands, various degrees of lockdown and isolation. Here, we have designed a fuzzy based framework for the automated detection to detect and warn if a user is wearing a mask correctly or not, (e.g., covering both mouth and nose) when in public places, using public services or even while talking to someone. This framework can be implemented at public places to monitor the public and warn a user if he/she is not correctly wearing the mask. The architecture used for the object detection purpose is Single Shot Detector (SSD) because of its good performance accuracy and high speed. Transfer learning in neural networks has also been used to finally find out the presence or absence of a face mask in an image or a video stream. Experimental results show that our model performs well on the test data with 100% and 99% precision and recall, respectively.

Keywords: COVID-19, pandemic, fuzzy decision making, mask wearing

2.1 Introduction

With the outbreak of severe acute respiratory syndrome coronavirus 2 ...