Detection of Face-Mask in Real Time: A Cascaded Bi-Level Feature Extraction Technique Approach

Abstract

Due to COVID-19's rapid spread, millions of people around the world have been affected and there has been extensive destruction. One of the most effective ways of preventing its spread and transmission during the pandemic period wearing of a mask and was required then in most public areas. As a result, this necessitate the use of programmed real-time mask detection devices in place of manual reminders. Face mask detection requires real-time processing of a huge amount of data with constrained processing resources, hence local descriptors that are quick to calculate, quick to match, and cheap to store are highly sought after. To achieve improved matching and reduction in memory use and computational complexity, the study offers a combination of Features from Accelerated Segment Test (FAST) corner detector with Histogram of Oriented Gradient (HOG) feature descriptor to allow faster matching and minimize memory usage and computation cost. The features obtained were then classified into face mask present and face mask absent using SVM, NB and CNN. Results obtained gives an accuracy of 99.41% which was higher than that previous results of 99.27% and 95% accuracy. Furthermore, it took the system only 48secs to extract the features obtained from face for training and testing. This outcome confirmed the suitability of the suggested method for real-time face mask detection.