Face Mask Detection with Automated Door Entry Control Using Convolutional Neural Network

Abstract

COVID-19, the coronavirus infection of 2019, has infected over 172 million people globally and killed over 3.7 million. One of the most prevalent ways for people to protect themselves in public areas is to wear masks. Face mask detection has become a critical computer vision task to control the infection, yet mask detection research is limited. So, real-time detection of the mask is proposed using a deep learning approach with an automated door entry control system. Deep learning plays a major role in Natural Language Processing, object identification, and facial recognition. The proposed system with the face net model incorporates mask detection efficiently and accurately. To detect the presence of a face mask, the trained face mask classifier is applied to the live video stream. The data from the detection phase is passed to an automated door entry control mechanism to control the entry of the people. The experiment is conducted, and the results are obtained. From the results, it is inferred that the system recognizes the presence of a mask and ensures safety through the automated door entry mechanism.