Smart Face Recognition Attendance System

This research paper presents a Smart Face Recognition Attendance System developed using Python, OpenCV, Haar Cascade face detection, and the LBPH face recognition algorithm. The system automatically detects and recognizes students in real time and marks their attendance with high accuracy, reducing manual effort and preventing proxy attendance. The workflow includes dataset creation, face detection, LBPH model training, real-time recognition, and automated attendance logging. Experimental evaluation across multiple test conditions showed accuracy between 80–100%. The proposed system is lightweight, efficient, and suitable for classroom deployment. Future enhancements include deep-learning-based face embeddings, cloud database integration, and liveness detection for improved security.