• Dr. Tryambak Hiwarkar
• Dr. B. Santhosh Kumar
• Ch. Chakradhara Rao

Covid-19 has made us appreciate the need of Face Masks in the present pandemic, and we need to comprehend the critical consequences of not wearing one now more than ever. At the moment, there are no face mask detectors in use in populated areas. However, we feel that at transportation intersections, densely populated residential areas, markets, and educational institutions and healthcare areas, it is now very important to set up face mask detectors to ensure the safety of the public. In this paper, we attempted to construct a two-phased face mask detector that would be simple to install at the aforementioned locations. It is now possible to detect and apply this on a wide scale using Computer Vision. For the implementation of our model, we employed the MobileNet V2 architecture. The implementation is done in Python, and the python script implementation will train our face mask detector on our selected dateset using TensorFlow and Keras. We included more robust features and trained our model on multiple variations, ensuring that the dataset was large, varied, and enhanced such that the model could clearly identify and detect face masks in real-time recordings. The trained model was tested on both real-time videos and static pictures and in both the cases the accuracy was more than the other designed models.

MobileNet V2, VGG 16, ADAS, TensorFlow and Keras

"REAL TIME FACE MASK DETECTION USING MOBILENET V2", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.c85-c87, June-2022, Available :http://www.jetir.org/papers/JETIR2206209.pdf

"REAL TIME FACE MASK DETECTION USING MOBILENET V2", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppc85-c87, June-2022, Available at : http://www.jetir.org/papers/JETIR2206209.pdf