Optimizing Attendance: Real-Time Mobile Notifications Through Facial Recognition with LBPH

The conventional methods for tracking attendance often involve manual processes, which can be inefficient and error-prone. This abstract introduces a Real-Time Smart Atten- dance System utilizing Face Recognition technology to overcome these challenges. The primary objective is to create an intuitive system that automates attendance tracking through computer vision and machine learning. This solution involves the development of a robust system capable of real-time face capture and recognition. Users can interact with a user-friendly interface to input essential information such as ID, name, and phone number. The system utilizes a face recognition model trained on a dataset of facial images, aiming to enhance accuracy and efficiency in attendance tracking. It employs the Local Binary Pattern Histogram (LBPH) Face Recognizer for training, learning distinctive facial patterns. The real-time face recognition system captures live video frames, identifies individuals, and records attendance alongside associated IDs and timestamps. This technology has wide applications in educational institutions, work- places, and events, enhancing precise and efficient attendance tracking for management and security purposes. By automating attendance management, this paper offers a contemporary and reliable alternative to traditional manual methods. The Real- Time Smart Attendance System using Face Recognition addresses the limitations of existing attendance tracking systems, combining user-friendly design with machine learning and computer vision to revolutionize attendance management.