Real-Time Student Attendance System Using AI and Face Recognition

The demand for efficient and accurate attendance systems in educational institutions has grown substantially, motivating the development of a contact-free, AI-driven solution. This research presents a Real-Time Student Attendance System that leverages computer vision and facial recognition to automate attendance recording with high accuracy. The system captures student faces using a standard webcam, applies Haar cascade detection and Local Binary Pattern Histogram (LBPH) recognition, and logs attendance automatically into structured CSV files. A user-friendly Tkinter GUI facilitates module navigation—student registration, model training, real-time recognition, and report generation—while supporting manual override when necessary. The system’s modular architecture ensures seamless integration of components and robust performance under varying environmental conditions. Testing demonstrates over 95% recognition accuracy, with immediate generation of attendance summaries and real-time GUI feedback. The proposed system reduces administrative load, prevents proxy marking, and offers scalability as a practical, low-cost solution for modern classrooms.