Real-Time Face Mask Detection Using Raspberry Pi and Camera

Rani, N.; Jain, R.; Patel, S.; Ruhela, T.; Kumari, P.; Kumawat, R.

We all know that this is the ERA of Coronavirus diseases (COVID-19). It is a kind of pandemic which is growing at a fast rate and causing a health crisis. It caused many deaths last years and still counting. So, the doctors and scientists are working on the vaccine for this virus and also, they have suggested some ways to be safe from this virus, face masks are one of the most common measures to fight such virus. In this paper, we have discussed on the project about face mask detection using Raspberry Pi and a live video streaming camera. The face mask detection model was done with the help of a computer vision, CNN, image classification algorithm based on the MobileNetV2 neural network. The steps involved in the project are: firstly, we have taken the data-set of people wearing the face mask and then those not wearing the face mask, after then we pre-processed it, split the data, trained the model using MobileNetV2 neural network, tested the model, and finally implemented the model. The model has been trained with an accuracy of 95.85%. This system automatically opens the door when people are wearing the face mask and sends an alert if they don’t have the face mask, to the authorities or the owner of the place. It can be used in a variety of places, include educational institutions, hospitals, churches, and retail outlets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.