Image Processing and Deep Neural Networks for Face Mask Detection

ABSTRACT

The proper use of a mask is crucial for lowering COVID 19 and transmission. According to the research, transmission is completely decreased when the mask is used appropriately. Factors like sunlight and several items can affect how appropriatel y applied face masks are classified and detected. Cotton masks, sponge masks, scarves, and other options greatly lessen the effect of personal protection in such circumstances. The research suggests a novel modified formula for classifying masks into three categories—a proper mask, a no mask, and an erroneous mask—using deep learning and machine learning. First, we provide a brand-new face mask classification and detection algorithm that combines deep learning, the viola Jones method, and Efficient-Yolov3 Wearing a mask, not wearing a mask, or wearing the wrong mask are the three options. On the dataset with or without mask pictures, the suggested system outperforms and is more accurate when compared to existing techniques. The results of experiments and analysis are also based on the classification knowledge set. In comparison to the present methodology's categorization accuracy of 84%, the anticipated formula boosted it to 97%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.