Automated attendance system with anti-spoofing face recognition detection

Chua, Wilson Wai Lun (2025) Automated attendance system with anti-spoofing face recognition detection. Final Year Project, UTAR.

PDF Download (2262Kb)

Abstract

This project creates an automated attendance system that uses computer vision and biometric authentication to make attendance tracking faster and more secure for schools and universities. The system uses face recognition and liveness detection to make sure students are correctly identified, stop fake attempts like using photos or videos, and make attendance reporting easier. It uses the face_recognition library to get facial features, and dlib to check if the face is real by detecting actions like smiling, blinking, and opening the mouth. Student data and attendance records are stored in Google Sheets, and a Flask web app is used for student registration, subject management, and sending email notifications for absentees. Studies of existing systems, such as fingerprint scanners, RFID systems, and other face recognition tools, show that many of them have weak anti-spoofing measures and are not very user-friendly. This project solves those problems with a two-step liveness check and a simple web interface. The final system uses Python for the backend, real-time webcam processing, and cloud storage, providing a secure, easy-to-use, and scalable solution for attendance management.

Actions (login required)