A Framework for Masked-Image Recognition System in COVID-19 Era

ABSTRACT

Face Recognition techniques have been widely developed and used for many years. Several approaches and models are adopted and successfully used to perform face recognition in airports, supermarkets, banks, etc. However, with the emergence of the COVID-19 pandemic, the whole world came across the requirement to use face masks. The mask’s partial covering of the face makes some well-known face recognition algorithms perform poorly or even fail. This paper has developed a real-time framework to detect, recognize, and identify people to authenticate them before accessing an app, device, or location. The newly created framework offers a unique set of capabilities, including the ability for users to select from various authentication methods based on their preferences or circumstances. The application’s face recognition section uses cutting-edge AI and computer vision algorithms to offer the user accurate face detection and recognition, even when the face is partially hidden behind a mask. © 2022, Springer Nature Switzerland AG.