Attendance management is one of the most important administrative tasks in schools, colleges, universities, and organizations. Traditional attendance methods such as manual registers and paper-based systems consume valuable time and are often affected by human errors, duplicate entries, and proxy attendance. With the rapid development of artificial intelligence and computer vision technologies, automated attendance systems have become more practical and reliable.

This research paper presents a Face Recognition Attendance System developed using Python and image processing techniques. The proposed system captures facial images through a webcam, detects faces using OpenCV, and recognizes individuals through facial encoding algorithms. Once a student is identified, attendance is automatically recorded along with the date and time. The system reduces manual effort, improves accuracy, and provides secure attendance management.

The project integrates Python libraries such as OpenCV, NumPy, Pandas, and face_recognition to implement real-time facial detection and recognition. The system also supports database storage for maintaining student records and attendance logs. Experimental results show that the proposed system performs efficiently under normal lighting conditions and significantly reduces attendance management time.