Mask Detection Using IoT - A Comparative Study of Various Learning Models

Abstract

Wearing a mask is an effective measure that prevents the spread of respiratory droplets into the air and thereby curtails the dissemination of coronavirus. Unfortunately, despite the proven effectiveness, the idea of wearing a face mask has difficulty being accepted by part of the population. To address this significant health concern, we present a monitoring system that automatically detects whether a mask is put appropriately over a face. The system annotates the videos that are provided by cameras. In this article, we present a comparative study of machine learning models (i.e., SVM, RNN, LSTM, CNN, auto-encoder, MobileNetV2, Net-B3, VGG-16, VGG-19, Resnet-152).