Face Mask Detection using Convolutional Neural Network

Abstract

During pandemic CoVID 19, people must use face masks in public areas to prevent and reducing the risk of transmission and spread of the virus. Computer Vision can help to monitor the use of face masks based on images captured via CCTV. Several public areas have installed CCTV that can monitor using masks, but too many people in the area would create problems. Face and side masked face detection is a challenge, given the removal of facial features such as the mouth and nose. A previous study built a mask detection system using Convolutional Neural Networks (CNN) based models, which produced high accuracy but was limited to the front face. This research proposed the CNN method to detect masks based on facial images taken from cameras in public areas. Images containing faces from CCTV are segmented, each faces first using the Retina Face. Experiments were carried out on a single face image in mask detection, resulting in an accuracy of 97.33%. These excellent results are not surprising given CNN's ability to recognize patterns. The most important thing is the segmentation of the face region from one image, which is then tested to produce an accuracy of 82.46%. We selected the best configuration from the two experiments, combined into a mask detection from an image containing multiple faces. The results also showed a significant effect between the face detection method and the learning rate value on the accuracy of the mask use detection system, with the best results of 79.45% using the RetinaFace face detection model.