Real Time Face Mask Detection Using MobileNetV2 and InceptionV3 Models

The widespread outbreak of COVID-19 has resulted in the need for efficient face mask detection. This study is focused on achieving real time face mask detection using methods which are not computationally expensive. A dataset which contains images captured in real time was selected for this application. The subjects in the dataset belonged to three different categories consisting of mask worn correctly, mask worn incorrectly and no mask. Two pretrained Convolutional Neural Network models, MobileNetV2 and InceptionV3 were fine-tuned using the transfer learning approach. This paper presents a simple approach in which the Region of Interest is extracted using a pretrained Multi-task Cascaded Convolutional Neural Network, whose output is fed into the classifier model. It was observed that the InceptionV3 model performs better when compared with MobileNetV2 model.