An Intelligent Attendance Monitoring System Using AI and Computer Vision

Background: Manual roll-call, paper registers, and RFID terminals remain the predominant attendance tools in

academic and corporate settings. Each carries well-documented liabilities: they burden instructors, introduce transcription

errors, and offer no reliable defence against proxy substitution [1]. In large classrooms or organisations, these traditional

methods often consume valuable time that could otherwise be used for teaching or productive work. Additionally, manual

systems require continuous supervision and maintenance, and the collected records are prone to loss, manipulation, or human

error. RFID-based systems, although partially automated, still require individuals to carry identification cards and physically

interact with a scanner, which may lead to card swapping or misuse.

A passive, camera-based approach resolves many of these shortcomings by capturing biometric identity non-intrusively from

commodity hardware, without requiring any cooperative gesture from subjects. Advances in artificial intelligence and computer

vision have enabled facial recognition systems to identify individuals accurately in real time, making them a practical solution

for automated attendance monitoring. By leveraging these technologies, attendance can be recorded automatically when a

person appears in front of the camera, thereby eliminating manual effort and minimizing the chances of proxy attendance.

Methods: The system is built exclusively in Python and combines OpenCV for live video streaming, face_recognition for 128-

dimensional ResNet-based face embedding, and dlib's 68-point landmark predictor for Eye Aspect Ratio (EAR) liveness

verification. Confirmed attendees are written to an SQLite3 database under a session-lock constraint that prevents duplicate

entries. A Streamlit dashboard presents a live recognition feed, historical attendance charts generated by matplotlib, and oneclick Excel export via openpyxl—all accessible through a browser on any networked device. The system also includes real-time

logging, automated timestamp generation, and dataset management for registering new users, ensuring reliable data storage,

faster recognition performance, and easy scalability for institutional deployment.

Results: Testing across 50 subjects under three controlled lighting conditions yielded a weighted mean accuracy of 95.8 %,

peaking at 97.4 % under standard fluorescent light. Per-subject check-in latency averaged 0.8 s—a 5.4× improvement over

manual roll-call. All 40 photographic spoofing attempts were blocked by the liveness module. Administrative reporting time

decreased by 60 % through automated dashboard generation