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@article{203141,
        author = {Dr. Nazirkar S. B. and Sandip Dadaso Gadadare and Prathamesh Sopan Awalkar and Aditya dayanand kavitake and Dr. Shah Saloni Niranjan},
        title = {Real-Time Attendance System Using Face Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9083-9088},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203141},
        abstract = {Attendance management is an essential administrative task in educational institutions and organizations. Traditional attendance methods are time-consuming, vulnerable to human error, and prone to proxy attendance. This paper presents an Artificial Intelligence based real-time attendance system using face recognition technology to automate and secure the atten-dance management process.
The proposed system uses computer vision and machine learning techniques to detect and recognize student faces through a webcam in real time. Facial features are extracted and encoded using deep learning-based recognition models and matched with stored facial datasets for accurate identification. A timetable validation mechanism is integrated into the system to ensure that attendance is marked only during authorized lecture sessions.
The system is implemented using Python, OpenCV, Flask, SQLite, and the face recognition library. Experimental analysis demonstrates high recognition accuracy, fast real-time performance, reduced manual effort, and improved attendance au-thenticity under normal environmental conditions. The proposed framework provides a secure, scalable, and contactless attendance management solution suitable for modern smart classrooms and institutions.},
        keywords = {Retina Authentication, OpenCV, Biometric Secu-rity, Image Processing, Python, Machine Learning},
        month = {May},
        }