ABSTRACT
In this paper, a simple and computationally efficient approach as per the complexity has been presented for Face mask detection using a Deep Learning architecture called MobileNetV2 including some additional specifications for the improvisation of the results. The secondary objective is to keep the pre/post-processing of the images minimal. The presented model is trained on images from the RMFD dataset which includes masked and non-masked people images. The aim is to achieve an accuracy above 95% with a minimum number of parameters after evaluation.
