An Intelligent Facial Recognition-Based Automated Student Attendance System with Schedule Integration and Absentee Notification for Educational Institutions
Keywords:
Attendance system, Face recognition, Haar Cascade, Template matching, OpenCV, Automated attendance, Computer vision, Biometric authentication, Flask, Real-Time processingAbstract
Traditional methods for tracking attendance in schools take a lot of time, have many errors, and can lead to student impersonation for attendance purposes (called proxy attendance). To overcome these issues, they have developed an automated attendance management system that uses face recognition technology. The system uses the Haar Cascade Classifier to detect faces and the OpenCV Template Matching algorithm to recognize them. The overall application was created in Python with the Flask web framework and includes a full-featured SQLite database, an authentication system that is based on each user's role and requires validation of attendance against a student's schedule before attending class, and provides automated notifications via email regarding attendance changes. Using the new system, the time required to take attendance for a class of 60 students dropped from 12-15 minutes to 45-60 seconds (92% time savings). Precision (actual recorded attendance for students) was 97.8%; recall (student's face actually being recorded) was 96.5%; F1-score (measure of accuracy) was 97.1%. In conclusion, this will be an effective, secure, and scalable solution for attendance management in educational institutions.
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