Camera (Webcam) Based Facial Attendance System Using Machine Learning

n the current digital landscape, facial recognition technology plays a crucial role across various sectors. It stands as one of the most prevalent biometric techniques, utilized for purposes like security, authentication, and identification, among others. While it may not match the accuracy of iris and fingerprint recognition, its non-invasive and contactless qualities make it highly appealing. Additionally, facial recognition systems are beneficial for tracking attendance in educational settings and workplaces. This project aims to create an attendance system that harnesses facial recognition technology to overcome the inefficiencies and challenges of traditional manual attendance methods, which can be slow and prone to proxy attendance. Consequently, the need for such a system is on the rise. The proposed solution involves four primary phases: creating a database, detecting faces, recognizing faces, and updating attendance records. The database will consist of images of students in the classroom. Face detection will utilize the Haar-Cascade classifier, and face recognition will employ the Local Binary Pattern Histogram algorithm. Faces will be identified and recognized from live video feeds in the classroom, with attendance records emailed to the appropriate instructors at the conclusion of each session.