Face Recognition Based Automated Attendance

  The Face Recognition Based Automated Attendance system provides an efficient and intelligent solution for automating attendance management in educational institutions. By integrating Artificial Intelligence, Computer Vision, and web technologies, the system eliminates manual attendance procedures and significantly improves the accuracy and reliability of attendance recording.   The application uses OpenCV and the Local Binary Pattern Histogram (LBPH) algorithm to recognize registered students through a webcam and automatically record attendance in a centralized SQLite database. In addition to attendance recording, the system provides facilities for administrator authentication, student management, department management, timetable management, and attendance report generation.   The modular architecture adopted during development improves software maintainability and simplifies future enhancements. The proposed system reduces administrative workload, minimizes proxy attendance, and enables quick generation of attendance reports. It also provides a secure and user-friendly interface that allows administrators to manage academic information efficiently while maintaining the accuracy and consistency of attendance records.   The successful testing of all modules demonstrates that the application satisfies its functional and non-functional requirements and performs reliably under normal operating conditions. The system offers an effective alternative to traditional attendance methods by reducing human errors, saving time, and improving overall efficiency.

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