Haar Cascade Classifier-Based System for Student Attendance Through Face Recognition

Attendance systems are essential in academic and organizational settings to track individual participation and discourage absenteeism. Traditional methods, such as paper-based attendance sheets, are tedious and susceptible to fraud, as well as impersonation and having someone else stand in for you. To resolve these problems, this study developed a haar cascade classifier-based system for student attendance through face recognition using Python and a webcam to detect and identify students in real-time. The web application applies the Haar Cascade Classifier from the OpenCV library, a machine learning-based algorithm that detects facial features. After students are enrolled, the system captures facial images, extracts features, and stores them in a database. During lectures, the system matches real-time images with the database, automatically recording attendance if a match is found.  Performance evaluation showed that both enrollment and attendance processes were completed in under one minute, offering significant efficiency improvements over manual methods while usability testing with ten participants confirmed high satisfaction ratings averaging above 4.0 on a five-point Likert scale across navigation, clarity, form layout, and overall experience. This system effectively eliminates impersonation, reduces lecturer workload, and encourages punctuality, thereby contributing to improved academic integrity and institutional efficiency.