Real-Time Student Attendance Management System using Deep Learning-Based Facial Recognition (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
Student attendance management is an undertaking, which consumes a great deal of resources, and whose results can still be marred by proxy fraud and clerical error. This proposal is an AttendAI, full-stack, real-time, automated attendance management system, a blend of facial recognition, which is deep learning-associated and a Flask REST API on a MongoDB Atlas cloud database. Using the InsightFace ArcFace pipeline, multiple students can be recognized and identified right in real-time on live webcam images so that the teacher makes them available through a web browser interface. Poor attendance journalizing is only written on a legit timetable slot which gives a schedule integrity to the server level. Offline enrollment averages per-student embeddings on a dataset of reference images, and enhances resistance to pose and expression change. To get the highest recognition rate a cosine-similarity parameter ( τ ) = 0.35 produces a highest recognition rate of 96% with a per-frame CPU processing latency of 120178ms and a maximum number of a total of 6 students simultaneously. The entire system encompasses the complete attendance cycle - selecting the classes and recognizing them in real time, going through a teacher review verification step, and permanent storage in the database, along with student, faculty, and timetable-management multiple views
Keywords: ArcFace; attendance automation; biometric identification; deep learning; facial recognition; Flask; InsightFace; MongoDB Atlas; proxy prevention; real-time systems