Facetrace: AI Based Student Presence Identification System

Attendance management is an important activity in schools and colleges because it helps institutions maintain proper academic records and monitor student participation during classes. Attendance tracking is necessary in educational institutions for maintaining student participation records and academic monitoring. Conventional methods such as roll calls, paper registers, RFID cards, and fingerprint devices are still used in many colleges, but these approaches require additional time and may sometimes lead to proxy attendance or recording mistakes. This paper presents FaceTrace, a facial recognition- based attendance system developed for automatic attendance marking. The proposed model uses OpenCV, DeepFace, and FaceNet to recognize students through a webcam feed. A liveness verification step based on head movement is also included to confirm physical presence before attendance is recorded. The system stores attendance information automatically along with timestamps using a Flask- based web application. During testing, the model was able to identify students effectively in real-time classroom conditions while reducing manual attendance handling. During testing, the system was able to record attendance correctly and reduce fake attendance attempts in classroom environments. The developed model can be used in classrooms to simplify attendance handling and reduce dependency on manual recording methods.