Real-Time Face Recognition Attendance System using OpenCV

This paper presents an automated face recognition–based attendance system designed to enhance efficiency and reduce the possibility of proxy attendance in educational environments. Traditional attendance methods are often time-consuming and prone to human error, which motivates the need for a reliable automated solution. The proposed system utilizes computer vision techniques implemented through OpenCV, along with the Local Binary Patterns Histogram (LBPH) algorithm for real-time face detection and recognition. A dataset of facial images is collected, preprocessed, and used to train the model, enabling accurate identification under varying lighting conditions and facial expressions. The system automatically records attendance with minimal manual intervention, thereby improving operational efficiency. Experimental evaluation demonstrates approximately 85% accuracy, indicating a notable improvement over conventional methods in reliability and time management. The proposed solution is cost-effective, scalable, and easily deployable in real-world academic settings.