Efficient Masked Face Recognition Method during the COVID-19 Pandemic

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.

Yüz Görüntülerine Morflemeye Dayalı Maske Giydirme ve Maskeli Yüz Tanıma

“…Birinci grup, maskeli yüzlerden, maskenin kapatmadığı yüz bölgesini (göz ve alın) belirleyip bu bölge üzerinden kişiyi tanımaya çalışmaktadır. [6,7,[9][10][11]. Qi ve Yang, maskeli Şekil 1.…”

“…Mazli Shahar ve Mazalan yüz görüntülerindeki 68 kontrol noktasını belirledikten sonra bu noktalardan göz burun çevresini ifade eden 31 tanesi kullanarak maskeli yüz tanıma yöntemi önermiştir [9]. Hariri maskeli yüzlerin göz ve alın bölgesini kullanarak yüz tanıma gerçekleştirmiştir [10]. VGGNet-16 ağı ile çıkarılan özniteliklerin sınıflandırılmasında çok katmanlı algılayıcı (perceptron) kullanılmıştır.…”