An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms
An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms
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DOI：
10.1109/southeastcon51012.2023.10115205
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Megh Pudyel;Mustafa Atay
中科院分区：
文献类型：
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作者：
Megh Pudyel;Mustafa Atay
自动人脸识别是一种广泛采用的机器学习技术，用于在自动边境控制、电子设备安全登录、社区监控、跟踪学校出勤、工作场所上下班打卡等各种流程中对人员进行非接触式识别。随着最近新冠肺炎 (COVID-19) 在全球范围内大流行，使用口罩在我们的日常生活中变得至关重要。口罩的使用导致传统人脸识别技术的性能大幅下降。戴口罩对人脸识别的影响尚未得到充分研究。在本文中，我们通过评估多个人脸识别模型的性能来解决这个问题，这些模型通过识别蒙版和未蒙版的人脸图像进行测试。我们使用六种传统的机器学习算法，即 SVC、KNN、LDA、DT、LR 和 NB，找出在存在蒙版人脸图像的情况下表现最好的算法以及表现较差的算法。局部二值模式（LBP）被用作特征提取算子。我们生成并使用合成的蒙面人脸图像。我们准备了未遮蔽、遮蔽和半遮蔽的训练数据集，并针对遮蔽和未遮蔽的图像评估了人脸识别性能，以呈现这一关键问题的广泛视图。我们相信，除了评估大量传统机器学习算法（与文献中的其他研究相比）之外，我们的研究在阐述几乎所有可能场景（包括 half_masked-to-masked 和 half_masked-unmasked）的掩模感知面部识别方面是独一无二的。
Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature.