Video based Human Face Mask Recognition System for Access Control

Abstract

Face mask recognition is the most basic and important security method for identifying people. The most important objective of this research is to apply this method at school entrances, airports, hospitals, and workplaces, where the risk of COVID -19 spreading via contagion is quite significant. According to the report, wearing face masks while at work significantly lowers the risk of transmission. It's an issue of object detection and classification with two classes (With Mask and Without Mask). For identifying masks, a hybrid model that combines deep and conventional machine learning is used. This face mask detector is made using Python, OpenCV, Tensor Flow, and Keras and is based on an information set. Everyone should check their face before entering the ant location to ensure they have a mask with them. If someone is discovered without a facemask, a beep alarm will be triggered. As all of the establishments open, the number of cases continues to rise throughout the nation. It may be brought to a close if everyone adheres to the safety and security procedures. As a result, we anticipate that this module will assist in identifying masks when people come to figure out.