A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic
A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic
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DOI：
10.1016/j.measurement.2020.108288
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发表时间：
2021-01-01
期刊：
影响因子：
5.6
通讯作者：
Khalifa, Nour Eldeen M.
中科院分区：
文献类型：
--
作者：
Loey, Mohamed;Manogaran, Gunasekaran;Khalifa, Nour Eldeen M.
新型冠状病毒COVID-19大流行正在引发全球健康危机。根据世界卫生组织（WHO）的说法，有效的保护方法之一是在公共场所戴口罩。本文将提出一种使用深度和经典机器学习的混合模型来检测人脸。所提出的模式包括两个组成部分。第一个组件是使用Resnet 50进行特征提取的。而第二个组件是专为口罩分类过程中使用决策树，支持向量机（SVM），集成算法。选择了三个面部掩蔽数据集进行调查。这三个数据集分别是真实世界的蒙面人脸数据集（RMFD）、模拟蒙面人脸数据集（SMFD）和野外标记人脸数据集（LFW）。SVM分类器在RMFD中的测试准确率达到99.64%。在SMFD中，它达到了99.49%，而在LFW中，它达到了100%的测试准确性。
The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy.