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@article{195164,
        author = {Mohammed Shabeena Ghori and Tandyala Sasi Kumar and Mekala Veda Varsha and Patchigolla Sampath and Tapashi Trupti},
        title = {Daily attendance via facial recognition referencing CCTV / Webcam footage},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6844-6847},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195164},
        abstract = {Daily Attendance via Facial Recognition using CCTV/Webcam Footage is an automated system designed to record attendance accurately and efficiently without manual intervention. The system leverages computer vision and machine learning techniques to detect and recognize human faces in real-time video streams captured through CCTV cameras or webcams.
The proposed system utilizes the OpenCV library for face detection and the Local Binary Pattern Histogram (LBPH) algorithm for face recognition. A dataset of registered individuals is created and used to train the recognition model.},
        keywords = {Facial Recognition, OpenCV, LBPH, Machine Learning, Attendance system, CCTV},
        month = {March},
        }