Artificial Intelligence Based on Identifying Face Mask Wearing Detection Using Recurrent Neural Network
Keywords:Artificial intelligence, Face mask detection, Mask detection, Recurrent neural network
COVID-19 has established a brand new frequency, and people have started realizing that they are entering a new world. The society is currently undergoing rapid change and needs to respond rapidly to fresh norms which surround us all. Creating a risk-free environment is a priority for everyone as life has not been as conductive as before brand new plans are methodized daily to adapt to policies and controls. The facial mask detection platform uses artificial intelligence see if the user is wearing a mask. App users can also add faces and phone numbers to warn themselves if they are not wearing a mask. A notice is issued to the administrator if the face captured by the camera is not recognized. Several COVID tracking tools are acceptable and safe in many aspects of the society. The most important tool is mask inspection using the proposed method, the artificial intelligence-based recurrent neural network (RNN) algorithm is enabled to capture the input image from the image classification in the dataset. However, in the overlapping scene, a different size and localization face mask detector provide a plurality of face images of the detected face. Detected faces extracted from this are grouped based on the human face detection. The recurrent neural network (RNN) algorithm using classifier and detection the mask or unmasked faces classifier. If the result is decoded from the stage, the final output is detected using all face images in the image correctly and then a message is sent to the surface and e-mail send that is not either of the mask or unmask.
How to Cite
Copyright (c) 2022 M. Nithya, R. Bharanidharan, M. Dinesh, V. Srinivasan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Asian Pacific Journal of Health Sciences applies the Creative Commons Attribution (CC-BY) license to published articles. Under this license, authors retain ownership of the copyright for their content, but they allow anyone to download, reuse, reprint, modify, distribute and/or copy the content as long as the original authors and source are cited. Appropriate attribution can be provided by simply citing the original article.