Masked Face Detection using Lightweight Deep Learning based Model for IoT based Healthcare applications

ABSTRACT

One of the best measures to enforce in epidemiological scenarios, such as the present COVID-19 epidemic, is the usage of masks. For a while, this will be a regular part of life, notably in public places. In order to deal with these unusual circumstances where people who wear mask are being watched, there is a need for an effective face identification technology. In order to precisely identify people wearing masks, we provide a deep learning algorithm based on YOLO architecture in this study. Unlike traditional CNNs, the proposed system uses a convergence layer to record numerous facial emotions while also using a number of convolutional filters to construct the faces for masked images. The presented design has numerous layers, including convolutional, max pooling, dropout, and softmax, and is both straightforward and effective. On the publicly accessible Real-World Masked Face Dataset, we assess the effectiveness of masked-faces detection (RWMFD). The investigational outcomes demonstrate an accurateness of 99.9%, demonstrating the effectiveness of our proposed methodology in classifying individuals wanting to wear facemasks. © 2022 IEEE.