AI-based face mask detection system: a straightforward proposition to fight with Covid-19 situation

Abstract

The whole world is suffering from a novel coronavirus, which has become an epidemic. According to a World Health Organization report, this is a communicable disease, i.e., it transfers from an infected person to a healthy person. Therefore, wearing a mask is the most important precaution to protect from COVID-19. This paper presented a deep learning-based approach to design a Face Mask Detection framework to predict whether a person is wearing a mask or not. The proposed method uses a Single Shot Multibox detector as a face detector model and a deep Inception V3 architecture (SSDIV3) to extract the pertinent features of images and discriminate them in mask and without masks labels. Optimizing the SSDIV3 approach using different modeling parameters is a genuine contribution of this work. In addition to this, the system is tested and analyzed on VGG16, VGG19, Xception, Mobilenet V2 models at different modeling parameters. Furthermore, two synthesized novel Face Mask Datasets are introduced containing diversified masks (2d_printed, 3d_printed, handkerchief, transparent, natural-looking mask appearance masks) and unmask images of humans collected in outdoor and indoor environments such as parks, homes, laboratories. The experiment outcomes demonstrate that the proposed system has achieved an accuracy of 98% on the synthesized benchmark datasets, which comparatively outperforms other state-of-art methods and datasets in a real-time environment.