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@article{195099,
        author = {Mr.M.Ganesh Gowd and K.Mohan Krishna and U.Harika and M.Chandini and T.Chaitanya Varma},
        title = {Face Mask Detection A Computer Vision Approach for Object Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8165-8170},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195099},
        abstract = {In recent times, maintaining public health safety has become extremely important, particularly during global outbreaks such as COVID-19. Wearing face masks plays a vital role in reducing the spread of infectious diseases. However, manually checking whether people follow this rule in crowded areas is difficult and inefficient.
This paper proposes an automated face mask detection system that uses deep learning and computer vision techniques. The model is built using Convolutional Neural Networks (CNN) to classify whether a person is wearing a mask or not. Face detection is performed using pre-trained models, and the system works in real time through live video streams.
The experimental results indicate that the model achieves high accuracy and performs efficiently in real-world conditions. This system can be applied in public places such as hospitals, schools, airports, and shopping centres to ensure safety compliance and reduce human effort.},
        keywords = {Face Mask Detection, Deep Learning, Convolutional Neural Network (CNN), Computer Vision, Real-Time Monitoring, COVID-19.},
        month = {March},
        }