Rapid Face Mask Detection and Person Identification Model based on Deep Neural Networks
This addresses the need for efficient monitoring of mask-wearing and identity verification during the Covid-19 pandemic, though it appears incremental as it builds on existing deep neural network methods.
The authors tackled the problem of rapid face mask detection and person identification by implementing a new model called RFMPI-DNN, which outperformed the MobileNet_V2 model in all aspects, including time efficiency.
As Covid-19 has been constantly getting mutated and in three or four months a new variant gets introduced to us and it comes with more deadly problems. The things that prevent us from getting Covid is getting vaccinated and wearing a face mask. In this paper, we have implemented a new Face Mask Detection and Person Recognition model named Insight face which is based on SoftMax loss classification algorithm Arc Face loss and names it as RFMPI-DNN(Rapid Face Detection and Peron Identification Model based on Deep Neural Networks) to detect face mask and person identity rapidly as compared to other models available. To compare our new model, we have used previous MobileNet_V2 model and face recognition module for effective comparison on the basis of time. The proposed model implemented in the system has outperformed the model compared in this paper in every aspect