ABSTRACT
The significance of social distancing and non-contact habits was emphasized by the Covid-19 pandemic. Even after the pandemic, everyone should adhere to the same hygiene procedures. Preventive measures must be implemented prior to the individuals' return. These include identifying people's presence and monitoring their health. This research focuses on using sensor fusion and deep learning technology to create a contactless individual management system. It is capable of carrying out the attendance routine without compromising the precautionary measures. Persons can be identified without removing the mask by employing random Quick Response (QR) code recognition. While recognising the QR, the system will double-verify the individual by identifying the Media Access Control (MAC) address of the user's mobile Bluetooth at the backend. Then the system employs a pre-trained deep learning model to detect masks. The Convolutional Neural Network (CNN) technique produces a deep learning model that can distinguish between Faces with and without masks. The system then monitors the body temperature with an Infrared (IR) temperature sensor followed by dispensing sanitizer. The response for the entire procedure will be updated in both the person's mobile application and the Management Authority. © 2022 IEEE.