FACEMASK DETECTION USING CONVOLUTIONAL NEURAL NETWORKS (CNN) WITH MOBILENETV2 ARCHITECTURE

Coronavirus Disease (COVID-19), which flared up at the end of 2019, continued to be a source of concern for all organizations and people till date. According to World Health Organization (WHO), around 228 billion people have been affected and seen 4 billion deaths globally. Even after considerable vaccination worldwide, there is still a wide spread of the virus. WHO stated that, the best way to protect ourselves is to avoid exposure to the virus, and also advised proper use of facemasks, i.e., covering the mouth and nose with a mask in public areas and continuous sanitization will avoid exposure to the virus. Implementing the "no mask-no service" method has been recommended by the governments of all countries. Even then, the goal was obstructed by wearing a mask improperly, which led to the spread of the virus. For classification of images, a technique in deep learning called Convolutional Neural Network (CNN) is used. CNN is a part of Artificial Neural Network (ANN), which has an ability in recognizing and classifying the images by processing images in video streams. An image classifier is created for identifying masked and unmasked faces from video streams. The proposed system was carried out by making use of Python, OpenCV, and TensorFlow, which can recognize whether a person is with proper facemask or with no facemask from live video streams. 2000 images are collected from two different datasets, from Kaggle repository, where each dataset contains images with and without masks. During training, the model has attained an accuracy of 98.2%.