A Naive and Semantic Approach for Detecting Face Mask Region Based Convolutional Neural Networks (R-CNN)

Abstract

Due to Covid-19, most of the health care organizations and governments has ordered their citizens to wear face mask to protect themself. This novel research presents a tactical methodology for rapid detection whether a person is wearing a face mask or not. It is entirely different from the existing system; the singular aim of the proposed system is to train the deep learning model with a minimum number of image samples and to operate face mask instance segmentation and along with object box detection. In this work, we proposed a novel and semantic pixel-to-pixel region based deep network which can detect no of face mask instances in different categories pixel wise to organize the segment bounding box and the confidence of the various categories for each pixel. This system experimental output demonstrate that this approach can effectively and precisely detect the face mask with multi-feature combination. It is also reported that our application performance outperforms the existing system.