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@article{180369,
        author = {NAVIN KISHAN K},
        title = {FACE RECOGNITION ATTENDANCE MANAGEMENT SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2829-2833},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180369},
        abstract = {The Face Recognition Attendance Management System (FRAMS) is a comprehensive, AI-driven solution designed to modernize and automate the attendance tracking process in educational institutions. Traditional attendance systems—whether manual registers or RFID-based tools—are often inefficient, error-prone, and susceptible to manipulation through proxy attendance. This research presents a robust and secure alternative that leverages cutting-edge technologies such as Python, OpenCV, Dlib, and Flask, along with a MySQL database backend to ensure high accuracy, speed, and reliability.
The system captures real-time video input through a webcam, detects and recognizes student faces using deep learning models, and marks attendance automatically with timestamps. To support flexibility and real-world complexity, the system includes manual attendance marking options, provisions for recording half-day attendance, and an intuitive admin dashboard where authorized personnel can edit or update student records. Additionally, it incorporates automated SMS and email alerts based on behavioral patterns, such as frequent absenteeism or low monthly attendance, thereby encouraging consistent engagement. A responsive web interface allows students and administrators to interact with the system seamlessly. Admins can monitor attendance trends, download filtered reports in CSV format, and perform student management operations from a centralized dashboard. The overall architecture is scalable, secure, and suitable for institutions aiming to transition to intelligent, data-driven attendance systems.},
        keywords = {Face Recognition, Online Attendance System, Convolutional Neural Network (CNN), RFID, Face Detection, Facial Feature Extraction, Deep Learning, Open CV, Dlib, Facial Feature Extraction, Student Monitoring System, Anti-proxy Attendance System.},
        month = {June},
        }