AI-Based Smart Attendance Management System

Facial recognition technology has gained significant traction in security, authentication, and accessibility applications. This study presents the development of a facial recognition system using Local Binary Patterns Histogram (LBPH) for efficient face detection and recognition. The system integrates OpenCV, NumPy, and PIL for image processing and training, leveraging Haar Cascade classifiers for accurate face detection. The model is trained on labeled datasets and utilizes real-time video streaming for face capture and recognition. This approach ensures fast and efficient identification of individuals while maintaining computational efficiency. The project demonstrates a robust and lightweight solution suitable for real-world applications such as attendance systems, access control, and surveillance. The implementation highlights the effectiveness of LBPH in handling variations in lighting, pose, and facial expressions, ensuring accurate recognition. The system is designed to function independently, making it ideal for standalone environments without requiring cloud-based processing. The method ensures low computational overhead, making it accessible for devices with limited hardware capabilities. Additionally, it offers privacy and security advantages by storing and processing data locally. The real-time face recognition system enhances usability and efficiency, providing seamless identification without manual intervention. The results indicate that the system provides high reliability and accuracy even under varying environmental conditions. The LBPH algorithm proves to be a versatile and effective choice for real-world deployment. This research underscores the potential of facial recognition in enhancing security and automation while ensuring ease of use.