Real time DNN-based Face Mask Detection System using MobileNetV2 and ResNet50

Coronavirus has changed the entire world. Studies indicate that to minimize the spread of the virus it is advisable to use masks for maximizing safety and keeping the community safe by slowing down the spread of the coronavirus. However, it becomes tedious and un-feasible to manually check each and every person who wears a mask or not. In that regard, technology presents digital and innovative solutions for this complex problem. This research study proposes a novel face mask detection algorithm. For face mask detection, Real Face Mask Detection dataset which consists of 4095 images in which mask images are 2165 and without mask 1930 images are taken to train and test the model using various pre-trained algorithms like (VGG19) besides proposing 2 different algorithms. The other models compared and tested are ResNet50, and MobileNetV2. These models are trained and tested in a Google Collaboratory environment with the help of TensorFlow and Keras software. A comparative study is made between these algorithms to decide which is the one that is the most suitable algorithm for the environment based on different parameters. The final model is applied to random images to check the accuracy of the model.