IDENTIFICATION OF FACE MASK USING CONVOLUTIONAL NEURAL NETWORK-BASED EFFICIENTNET MODEL

Abstract

The highly contagious coronavirus wreaked havoc around the globe. There has been a rapid spread of the virus throughout the world. According to the World Health Organization (WHO), wearing a facemask will help keep the virus from infecting others. Consequently, many governments have adopted the solution of wearing facemasks. In this paper, we use a convolutional neural network (CNN), and a scaling method, namely, EfficientNet with Adam optimizer, for detecting face masks in real-time. A dataset including 10,000 colored images was collected from a public data platform, Kaggle, for training, and testing of the model. Image augmentation is also investigated on the dataset to improve the training, and testing accuracy. Then the binary classifier model is used to detect masks after detecting faces using single shot detection (SSD). From the experimental results, the EfficientNet model outperforms the existing CNN-based methods in terms of accuracy, efficiency, and the validation accuracy of EfficientNet models is above 99 percent. This efficient and highly accurate model can be used to detect facemask anywhere in a real time video surveillance system.