Face Mask Detection Using MobileNetV2 and Implementation Using Different Face Detectors

Face recognition and object detection have been around the artificial intelligence field for a couple of years now and are constantly evolving and being pushed in many devices which we might not be even aware of. Many of such face and object detection techniques use Convolution Neural Network (CNN) architecture at the core for understanding and classifying any image passed on to the system. The neural networks identify many characteristics and distinguishing features present in the image and then provide us with a prediction. In this paper we discuss the implementation of a face mask detection technique using Mobile NetV2, observe the accuracy of our model and compare the performance of the trained model by incorporating three different face detector models. The result achieved from the trained model brings forth the opportunity for implementing such techniques on low computational powerful devices thereby making mask detection algorithm integration much easier than other techniques.