Detection of face mask using sequential convolution neural network framework.

Many people around the world have been affected by COVID-19. It has caused major economic and trade disruptions. Covering a person's face with a mask has become the accepted norm. Today's world, every public service provider requires their customers to wear proper face masks in order to use their assistance. As a result, the concept of identifying face masks has become a key role in assisting global civilization. A more straightforward approach to achievingthis goal is presented in this paper, which makes use of TensorFlow, Keras, OpenCV, and Scikit-Learn, as well as some fundamental machine learning packages. Using this method, you can find the face in an image and then figure out if it's covered by a mask. It should be able to detect a face as well as a mask in motion as part of its monitoring role. The approachachieves an accuracy of up to 92 percent on the dataset, respectively. The framework of Sequential Convolution Neural Networks is used to investigate optimal parameter values for detection and identification of masks without over fitting. [ABSTRACT FROM AUTHOR]