• Mr.T.Saran Sujai
• Gokulapriya G
• Brindha R
• Javith Ahamed J

The Covid-19 pandemic began to resurface at the end of 2019, and it continues to be a major source of concern for many individuals and organisations into 2020. Everyone is feeling anxious as the world recovers from the pandemic and prepares to return to a state of routine, but those who intend to carry on with in-person activity are more anxious. Research has indicated that donning a face covering not only provides a sense of security but also significantly reduces the risk of viral transmission. However, it is not possible to physically follow the implementation of this plan. Here, inventiveness is crucial. We describe a method based on Deep Learning that can detect situations in which face veils are clearly not used effectively. Our system consists of a two-stage OPENCV design that can be synchronised with pre-installed CCTV cameras and is capable of differentiating between faces that are covered and those that are not. The OpenCV library and computer vision algorithms are utilised by the suggested face mask identification system to precisely identify people who are wearing face masks. This will ensure a safe working environment, promote the use of face coverings, and assist in tracking violations of people's well-being.

Masked Face Recognition, Deep Learning, Neural Network, Secure Authentication, OpenCV.

"Enhancing Masked Face Recognition Using Opencv In Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.p389-p395, April-2024, Available :http://www.jetir.org/papers/JETIR2404G47.pdf

"Enhancing Masked Face Recognition Using Opencv In Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppp389-p395, April-2024, Available at : http://www.jetir.org/papers/JETIR2404G47.pdf