Multi-Facial Automated Attendance System using Haar Cascade, LBPH, and OpenCV-Based Face Detection and Recognition

In the 21st century, everything around us is dependent upon technology to make our lives much easier. Daily tasks are increasingly being computerized. Nowadays more people prefer to do their work electronically. To the extent of our knowledge, students' attendance at some universities is manual. The attendance management system computerizes the traditional attendance process. Utilizing OpenCV (Open-source Computer Vision) and some face detection and recognition algorithms, we propose a system to measure student attendance in classes. By using face detection algorithms and face recognition algorithms, we will keep track of how many people are present in the class and attendance of each one. This project intends to provide a system that automates and streamlines the process of collecting and monitoring student attendance using facial recognition technology. To find, identify, and validate collected faces, preprocessing methods like the Haar Cascade approach will be used. To make the attendance process quicker and more accurate, we aim to offer a method.