Balasubaramanian Sundaravadivazhagan, Cyriac Robin, Roshan Sahana, Maruthamuthu Paramasivam Kulandaivel, Chellanthara Jose Boby
University of Technology and Applied Sciences - Al Mussanah, Department of Information Technology, Al Muladdah, 314, South Al Batinah, Oman.
Array (N Y). 2023 Sep;19:100294. doi: 10.1016/j.array.2023.100294. Epub 2023 Jun 5.
The COVID-19 pandemic has been infecting the entire world over the past years. To prevent the spread of COVID-19, people have acclimatised to the new normal, which includes working from home, communicating online, and maintaining personal cleanliness. There are numerous tools required to prepare to compact transmissions in the future. One of these elements for protecting individuals from fatal virus transmission is the mask. Studies have indicated that wearing a mask may help to reduce the risk of viral transmission of all kinds. It causes many public places to take efforts to ensure that its guests wear adequate face masks and keep a safe distance from one another. Screening systems need to be installed at the doors of businesses, schools, government buildings, private offices, and/or other important areas. A variety of face detection models have been designed using various algorithms and techniques. Most of the articles in the previously published research have not worked on dimensionality reduction in conjunction with depth-wise separable neural networks. The necessity of determining the identities of people who do not cover their faces when they are in public is the driving factor for the development of this methodology. This research work proposes a deep learning technique to determine if a person is wearing mask or not and identifies whether it is properly worn or not. Stacked Auto Encoder (SAE) technique is implemented by stacking the following components: Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). PCA is used to reduce the irrelevant features in the images and resulted high true positive rate in the detection of mask. We achieved an accuracy score of 94.16% and an F1 score of 96.009% by the application of the method described in this research.
在过去几年里，新冠疫情一直在全球蔓延。为防止新冠病毒传播，人们已经适应了新常态，包括居家办公、在线交流和保持个人清洁。为未来应对疫情传播，需要众多工具。其中一项保护个人免受致命病毒传播的要素就是口罩。研究表明，佩戴口罩可能有助于降低各类病毒传播风险。这使得许多公共场所努力确保其客人佩戴合适的口罩并相互保持安全距离。在企业、学校、政府大楼、私人办公室和/或其他重要区域的门口需要安装筛查系统。人们使用各种算法和技术设计了多种面部检测模型。此前发表的研究中的大多数文章并未结合深度可分离神经网络进行降维处理。确定在公共场所未蒙面者身份的必要性是这种方法发展的驱动因素。这项研究工作提出了一种深度学习技术，用于确定一个人是否佩戴口罩，并识别口罩佩戴是否正确。堆叠自动编码器（SAE）技术通过堆叠以下组件来实现：主成分分析（PCA）和深度可分离卷积神经网络（DWSC-NN）。PCA用于减少图像中的无关特征，并在口罩检测中获得了较高的真阳性率。通过应用本研究中描述的方法，我们取得了94.16%的准确率和96.009%的F1分数。
