Abstract:
Late Checker is a real-time attendance tracking system that uses face recognition
technology to record employee presence through live video surveillance. This system was
developed to reduce manual attendance processes and provide a more efficient and contactless
solution. It consists of a web-based interface for administrators and a facial recognition module
that automatically identifies individuals from camera feeds and logs their arrival times.
The AI component of this project focuses on detecting and recognizing faces using
Python-based libraries such as OpenCV and face_recognition. The system captures face
images, encodes them into numerical data, and matches them against a trained dataset. Once a
face is successfully identified, the attendance is logged into the system in real time.
This project was built using Flask for the backend, ReactJS for the frontend, and
PostgreSQL as the database. It aims to provide a practical and scalable solution for attendance
monitoring in office or classroom environments. Throughout the development, several
challenges related to lighting conditions, recognition accuracy, and real-time performance were
addressed. The final product demonstrates how AI can be applied effectively to solve everyday
administrative tasks.
