Streamlining Attendance with Fast Face Recognition Model
Koushik Sundar, Siddharth Ravikumar, J. Jeyalakshmi, Eugene Berna I, Prithi Samuel · 2024
Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a Redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and Dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment.