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@article{174159,
        author = {Nandepu Rama Venkata Durga Vamsi and A.Bhagyasri and Vattikuti Raghavendra and Sirigineedi Gamyasri and Sudhana Mukesh},
        title = {AI-Powered Student Proctoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4178-4184},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174159},
        abstract = {This study introduces a real-time computer vision-based system for monitoring children's concentration levels during remote learning. Leveraging OpenCV and Mediapipe's Face Mesh for video capture and facial landmark detection, the system evaluates attention through visual cues such as eye movements, blink rates, and head posture. Lightweight machine learning models process these features to classify focus states, offering real-time feedback via a user-friendly Streamlit interface. Designed for affordability and accessibility, the system operates using standard webcams and computers, making it practical for home environments. By bridging a critical gap in remote education, this solution supports caregivers in enhancing children's study habits and ensuring sustained engagement.},
        keywords = {},
        month = {March},
        }