FACE MASK DETECTION BY USING CONVOLUTIONAL NEURAL NETWORK

Abstract

The proposed system is designed to classify people who is wearing face masks or not. The model used in this system is MobilenetV2, a convolutional neural network (CNN). The image dataset contains 7553 images. 3832 images used to train model and 3721 images are used for testing. Firstly, the input images are needed to be processed. Resizing, One-hot Encoding and data Augmentation are applied in preprocessing. The porposed system is constructed with MobilenetV2 model. If a person is wearing a mask, the system displays the face region with a green anchor box. If a person is not wearing a mask, the face region is displayed with a red anchor box. This system can be merged with other applications at airports, railway stations, workplaces, schools, and other public places for safety. The accuracy is 82 % for testing images in dataset.