Efficient Masked Face Recognition Method during the COVID-19 Pandemic

Abstract

The COVID-19 is an unparalleled crisis leading to huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes the face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on discard masked region and deep learning based features in order to address the problem of masked face recognition process. The first step is to discard the masked face region. Next, we apply a pre-trained deep Convolutional neural networks (CNN) to extract the best features from the obtained regions (mostly eyes and forehead regions). Finally, the Bag-of-features paradigm is applied on the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance.

Integrated Single Shot Multi-Box Detector and Efficient Pre-Trained Deep Convolutional Neural Network for Partially Occluded Face Recognition System

“…In [33], Jiang et al proposed Squeeze and Excitation (SE)-YOLOv3 which outperformed YOLOv3 in mask detection. Face recognition with mask can be further found in [34] and [35]. Previous methods all focus on mask face detection, but the present study not only for mask face detection but also target for occluded face identification.…”

Masked Face Recognition Using Deep Learning: A Review

“…Hariri [172] proposed deep learning-based features to discard masked regions for MFR. They used pre-trained deep CNNs to select the best features from the captured regions, mostly eyes and forehead regions.…”

Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

“…These applications could be designed to be compatible with the capabilities of a smartphone to further speed up their operation. Among these applications, we can find masked face recognition (Hariri 2020), facial mask detection (Chen et al 2020;Chua et al 2020), social distance monitoring (Ahmed et al 2020;Rezaei and Azarmi 2020) and human mobility estimation (Xiong et al 2020). Other mobile-based technique using to fight against COVID-19 has been proposed in .…”