A school attendance management system by using facemask recognition based on the internet of things (IoT) approach.

This project focuses primarily on developing a student attendance management system using a Raspberry Pi machine with Deep Learning and Computer Vision technologies. The main objective of this project is to assist school authorities who are struggling to maintain student attendance, especially during this global pandemic of COVID-19. This is done by implementing technology of facemask recognition, Quick Response (QR) code as data identification, temperature detection, storing the data in the database, and lastly, user monitoring. In addition, students can protect themselves from Covid-19 disease using this method based on the Standard Operating Procedure (SOP) implementation. The initial stage of this system is recognizing whether the students wear masks properly or not. Then, the QR code is scanned to provide the identity of the students and the data is saved. The Raspberry Pi is programmed to continuously monitor students at the school entrance. The board is set up to collect temperature data from the K3 Pro device and eventually uploaded the data to the Google Sheet database together with QR code identity data. Later, the data can be viewed through an android smartphone and the system can be controlled by the school authorities in charge. Every single process of this system is run and updated based on real-time data recording. [ABSTRACT FROM AUTHOR]