Smarter., Faster, Better: Evaluating a Next-Gen Face Recognition System Attendance Tracking System using AI

Management systems operate in various industries. This paper presents the development and implementation of a facial recognition system specifically designed for real-time attendance monitoring. By integrating widely used libraries such as OpenCV and Dlib with machine learning techniques, this system aims to address the inefficiencies found in traditional attendance systems, providing a faster, more reliable, and secure alternative. The challenges faced in the deployment of such systems, including varying lighting conditions, face angles, and privacy concerns, are explored. The system prioritizes accuracy in real-time settings and scalability for large datasets while complying with stringent data privacy laws such as GDPR. Our findings demonstrate that with optimization, the system can achieve over 95%.