ABSTRACT
The COVID-19 pandemic has quickly had an impact on our day-to-day lives, as well as on the movement of goods and people around the world. It has recently been common practice to shield one's face by using a mask. In the not too distant future, many businesses that provide public services will need their clients to correctly wear masks in order for them to receive those services. As a result, the detection of face masks has evolved into an important mission in the service of worldwide society. In this post, a relatively straightforward approach to achieving this goal is presented using basic machine learning tools like TensorFlow, Keras, OpenCV, and Scikit-Learn. The suggested method accurately locates the face inside the image before determining whether or not it is covered by a mask. While doing a surveillance task, it is capable of detecting a mask as well as a moving face. To properly detect the presence of masks without over-fitting, we look into numerous options for optimizing the values of the parameters in the Sequential Convolutional Neural Network model. © 2022 IEEE.