Face mask recognition system using MobileNetV2 with optimization function

The world has experienced a health crisis with the outbreak of the COVID-19 virus. The mask has been identified as the most effective way to prevent the spread of the virus. This has led to the need for a face mask recognition device that not only detects the presence of the mask but also provides the accuracy with which a person is wearing the face mask. In addition, the face mask should also be recognized from all angles. The project aims to create a new and improved real-time face mask recognition tool using image processing and computer vision approaches. A dataset consisting of images with and without a mask was used. For the purposes of this project, a pre-trained MobileNetV2 convolutional neural network was used. The performance of the given model was evaluated. The model presented in this project can detect the face mask with an accuracy of 99.21%. The face mask recognition tool can effectively detect the face mask in the side direction, which makes it more useful. The optimization function which contains the learning loops and the optimization function are also used.