THE NOVEL APPROACH FOR AUTOMATIC FACIAL MASK RECOGNITION FOR VISITORS AND EMPLOYEE IN AN ORGANIZATION TO PROVIDE FACE MASK
Surveillance has become an active research topic. Video analytics enhance video surveillance systems by performing tasks of real-time event detection and post-event analysis. This can save Human resources, costs and increase the effectiveness of the surveillance system operation. One of the common requirements of Video Analytics for security is to detect the presence of a masked person automatically. In this project, we propose a technique for masked face detection using four different steps of estimating distance from the camera, eye line detection, facial part detection, and eye detection. The paper outlines the principles used in each of these steps and the use of commonly available algorithms of people detection and face detection. This unique approach for the problem has created a method simpler in complexity thereby making real-time implementation feasible. Analysis of the algorithm’s performance on test video sequences gives useful insights to further improvements in the masked face detection performance.
Surveillance, face detection, Video Analytics, people detection.