"Whatever mask you wear, it is still important that you wear it as effectively as possible. Touch it as infrequently as possible, particularly in the area where filtration is occurring."
Dr Santarpia
Desktop Application
Made using a python library called OpenCV. The camera first identifies the face and the mouth in every frame input. It then splits that input, in different scales of colour, and creates rectangles on each of those inputs and compares the dimensions of each of those rectangles. Based on the said comparisons, the application detects the presences of a face mask.
Mobile Application
Software freedom is an essential part of the 21st century. So we replicated our initial idea of detecting facemasks using the iOS platform. So we designed an application using Swift4. We took the assistance of Vision and UIKit packages. The application uses the back camera of the phone and leverages a custom made ML model to identify a mask on a person's face.