PREDICTING HUMANS FROM SPREAD OF DELTA VARIANT TYPE COVID-19 VIRUS BY EMBEDDED WITH FACE MASK DETECTION AND RECOGNITION USING MACHINE LEARNING TECHNIQUES

Computer Science

In the world, viruses like Covid-19 emerge causing health emergencies, pandemics, discomfort and poor financial conditions. Due to changing structure of the virus, pharmaceutical companies are working hard to develop effective treatments. It has taught the importance of safety precautions. Present paper proposes an innovative method to automate face mask detection with biometric attendance. Proposed method works in four phases. In the first phase, images of all individuals are collected and stored in a centralized database. During second phase collected image samples are loaded in to a model for training and a separate suite is maintained for every individual. In the third phase employee’ images are collected and checked with centralized database to prepare a list of employees not wearing face masks. In the fourth phase prepared list is displayed using visualization techniques and implementation is done using machine learning classification algorithms. This method helps to trace the employees not wearing masks during working.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.