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@article{196563,
        author = {Mrs.Monali B. Kale and Prof.S.P.Shinde},
        title = {Next Generation Face Mask Detection Using Real-Time Deep Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3535-3536},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196563},
        abstract = {The COVID 19 pandemic increased the need for automated monitoring systems to ensure that people follow safety regulations such as wearing face masks in public places. Manual monitoring is inefficient and difficult in crowded environments. This paper presents a next generation face mask detection system using a real time deep neural network. The proposed system uses computer vision techniques to detect human faces and classify whether a mask is present or not. A Convolutional Neural Network model is trained on a dataset containing masked and unmasked faces. Real time video frames are captured through a camera and processed using Open CV. The system automatically detects faces and predicts mask usage with high accuracy. The proposed solution can be deployed in public areas such as hospitals, airports, and workplaces to improve safety monitoring and reduce the spread of infectious diseases.},
        keywords = {deep learning, face mask detection, neural network, real time monitoring.},
        month = {April},
        }