RESCNN: A Deep Learning Approach for Unmasking Face Mask
DOI:
https://doi.org/10.24113/ijoscience.v7i1.350Keywords:
COVID-19, Face Mask, Detection, Face Unmasking, CNN, Residual Learning.Abstract
Due to outbreak of COVID-19 pandemic, the trend of wearing mask is rising all over the world. Before such pandemic people wear mask only to protect themselves from pollution. While other people are self-conscious about their looks, they hide their emotions from the public by hiding their faces. But in current scenario, after pandemic, it is compulsory to wear mask everywhere as researchers and doctors have proved that wearing face masks works on impeding COVID-19 transmission. Nowadays, all attendance system or surveillance systems, etc. are integrated with AI technology in which face recognition is considered as input variable. So, there is need to determine all facial landmarks to recognize an individual. In this research work, Residual Convolution Neural Network (ResCNN), network is designed and simulated which unmasks the face mask present on face and restore mask area and recognize an individual. The result analysis is performed in three different cases or scenario, one normal frontal facial region with mask, in another case the masked face is tilted and in third case the noisy masked face is taken as input. The noise in image occurs due to many physical conditions. The dataset for training of ResCNN is prepared by masking facial images taken from CelebA dataset and MFR datasets to prove the efficiency of the proposed model.
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Copyright (c) 2021 Bhusra Fatima, Arun Kumar Jhapate
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