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@article{175092,
        author = {Nitin Pal Singh and Praveen Kumar Dhangar and Manish Kumar Singh and Ayesha farooq},
        title = {Face Mask Detection using MobileNetV2 &CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1862-1867},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175092},
        abstract = {The widespread use of face masks has become a critical public health measure in controlling the transmission of airborne diseases such as COVID-19. This paper presents a machine learning-based face mask detection system that automatically identifies whether an individual is wearing a mask. The system utilizes deep learning techniques, specifically convolutional neural networks (CNN), to classify images captured by cameras or video feeds. The proposed model is trained on a large dataset of masked and unmasked faces, enabling it to accurately differentiate between the two categories. The system achieves high accuracy in detecting face masks, even in varying lighting conditions, orientations, and partial obstructions. The implementation of this system can aid in enforcing mask mandates in public spaces, ensuring compliance, and enhancing public health safety. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with results indicating the robustness of the system for real-time applications.},
        keywords = {},
        month = {April},
        }