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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.
Highlights
Face detection has become a very interesting problem in image processing and computer vision
In this sub-section, comparisons with existing work is performed with proposed Residual Convolution Neural Network (ResCNN) based face mask detection technique on a static scene
The simulation result is performed on MATLAB platform in which two datasets is taken for reference i.e. celebA and Masked Faces in Real-World (MFR) datset
Summary
Face detection has become a very interesting problem in image processing and computer vision. It has a range of applications from facial motion capture to facial recognition which initially requires face detection with efficient accuracy. Face detection is more relevant today because it is used on images and in video applications such as real-time surveillance and face detection in videos. Pixel-level information is often needed after face detection, which most face detection methods do not provide[1]-[3]. Getting pixel-level detail was a difficult part of semantic segmentation. Semantic segmentation is the process of assigning a label to each pixel of the image. Semantic segmentation is used to separate the face by classifying each pixel of the front or background image. Most of the widely used face detection algorithms tend to focus on front face detection[4][5]
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