Real Time Face Mask Detection to Prevent Viral Respiratory Infection

The COVID-19 epidemic has interfered with people’s daily lives as well as with international commerce and shipping. The World Health Organization mandated protective clothing against the COVID-19 virus during the epidemic. Guarding one’s face with a mask is now expected. Customers of many different types of public service providers will be encouraged to dress appropriately for the future. On the other side, standing in one place and witnessing many people fail at the same time. To reduce the spread of the Corona virus across public networks, this project presents a deep learning solution for public-facing identity verification.The proposed work’s main contribution is the creation of a real-time system to detect the presence of a hidden camera. The detection rate is dramatically increased, and the success of excessive accuracy is simplified, thanks to the ensemble approach. The incorporation of transfer learning into the provided models and thorough goal testing on the dataset led to the creation of a somewhat dependable and inexpensive option. The results demonstrate the software’s readiness for widespread worldwide deployment to help curb the spread of pandemics. The suggested approach provides better accuracy, specificity, precision, recall, and estimate of the F degree within the three output categories than existing methods. Accuracy, specificity, precision, and memory are just examples of these indicators. There is a reasonable equilibrium between the number of available parameters, the time needed to finish each model, and the total time spent on all of them.