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@article{176450,
        author = {Dnyaneshwar Hanumant Nimbhorkar and Vaishnavi Dattatray Dhamal and Pranita Pravin Kanthe and Ankita Khemchand Narute},
        title = {Smart Attendance System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7094-7098},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176450},
        abstract = {Tracking student attendance in educational institutions is often tedious, inaccurate, and susceptible to manipulation when done manually. To address this, we propose a smart attendance system that leverages advanced deep learning methods for automated, real-time monitoring. The system combines FaceNet for robust facial feature extraction and YOLOv8 for detecting mobile phones during class sessions, thereby maintaining student discipline and minimizing distractions. If a mobile phone is detected, the system instantly sends a Pushbullet notification to designate authorities. Attendance is marked based on the cosine similarity between live-captured face embeddings and pre-stored feature vectors, removing the need for manual checking. An SQLite database manages student profiles, attendance logs, and class timetables efficiently. Additionally, a Flask-powered web interface provides user-friendly modules for student registration, schedule management, and visual attendance tracking. The solution also includes automated entry and exit time tracking, mapped to real-time class durations. This contactless and intelligent system significantly boosts administrative productivity and classroom discipline.},
        keywords = {Face Recognition, FaceNet, YOLOv8, Cosine Similarity, Attendance System, Deep Learning, Push Notifications, SQLite, Flask Web App},
        month = {April},
        }