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@article{173758,
        author = {Bora Sanchit Reddy and S. Palavelli and S. Sudha Sree and CH. Yaswanth Kumar},
        title = {An Optimized Facial Recognition-Based Attendance System with Enhanced Security for Educational Institutions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1435-1440},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173758},
        abstract = {Facial recognition technology has emerged as a promising solution for automated attendance systems. Traditional attendance tracking methods, such as manual roll calls and RFID-based systems, are prone to errors, time-consuming, and susceptible to proxy attendance. This paper introduces An Optimized Facial Recognition-Based Attendance System with Enhanced Security for Educational Institutions, an adaptive facial recognition-based attendance system that leverages incremental learning, fine-tuning, and automated data management to overcome these limitations. The proposed system ensures high accuracy, real-time attendance tracking, and seamless adaptability to new student entries without requiring complete retraining. Using state-of-the-art machine learning techniques, An Optimized Facial Recognition-Based Attendance System with Enhanced Security for Educational Institutions improves recognition under varying environmental conditions, making it a robust and scalable solution for educational institutions and organizations.},
        keywords = {Facial Recognition, Attendance System, Incremental Learning, Machine Learning, Security},
        month = {March},
        }