Smart attendance using deep learning and computer vision

Attendance is an essential part of daily classroom evaluation. Traditional classroom follows a manual attendance marking system, i.e., calling a student's names or by forwarding an attendance sheet. This process is both time-consuming and error-prone, i.e., student proxy, etc. Hence a face recognition based smart classroom attendance management system using computer vision and deep learning implemented on a Raspberry Pi has been proposed. It has been proposed to mount a camera at the top of the blackboard so that the students are visible while they are sitting down. A face detection algorithm followed by face recognition has been used to mark the attendance of the detected student. (c) 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials, Manufacturing and Mechanical Engineering for Sustainable Developments-2020.

Automated summary

Attendance is crucial for daily classroom evaluation, with a proposed smart classroom system utilizing face recognition technology through computer vision and deep learning on a Raspberry Pi for efficient and accurate attendance marking. This method involves mounting a camera on top of the blackboard to detect and recognize student faces, replacing the traditional manual attendance system, saving time and reducing errors.