Face Mask Recognition Using Machine Learning

Abstract

Abstract: Face mask detection software saves time and effort by automating various processes. A camera is used to capture visual input from a specific area. Then the graphics are compared to the predefined dataset. Determine whether or not a face mask is present. Once we've done that, have the input in graphic form, it can be presented as an input to a deep learning model for action prediction or this is the output that should be served to the user. Then it is necessary for the model to have patterns in which it may operate. Discern between persons who are masked and those who are not. The model will also include the actions that must be completed. When it detects the existence of a face mask, it proceeds to the next step. In addition, the most fundamental requirement for this software is for implementation, python libraries such as tensor flow, numpy, and sklearn are employed in realm of artificial intelligence. A window will appear in response to the user's command and will show the camera's graphics for identifying the face mask and the output will then be displayed based on whether the user is facing up or down. Is there a mask on or not? Face mask detecting software has been developed. In this pandemic period, there are numerous applications. Keywords: OpenCV, Machine Learning, MobileNetV2, Keras are all terms that can be found in the index.