Efficient Masked Face Recognition Method during the COVID-19 Pandemic

TL;DR

This paper introduces an efficient masked face recognition method during COVID-19 by removing masks, extracting deep features with pre-trained CNNs, and classifying with MLP, achieving high accuracy on real-world masked face data.

Contribution

It proposes a novel approach combining occlusion removal, deep feature extraction, and Bag-of-features with MLP classification for masked face recognition.

Findings

High recognition accuracy on Real-World-Masked-Face-Dataset

Outperforms existing state-of-the-art methods

Effective use of pre-trained CNNs and Bag-of-features

Abstract

The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes 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 occlusion removal and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to remove the masked face region. Next, we apply three pre-trained deep Convolutional Neural Networks (CNN) namely, VGG-16, AlexNet, and ResNet-50, and use them to extract deep features from the obtained regions (mostly eyes and forehead regions). The Bag-of-features paradigm is then applied to 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 compared to other state-of-the-art methods.