Lightweight face mask detection and face recognition

https://doi.org/10.31130/ud-jst.2022.243ICT

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Author

  • Tien Ho-Phuoc
    The University of Danang - University of Science and Technology, Danang, Vietnam
    Hanh T. M. Tran
    The University of Danang - University of Science and Technology, Danang, Vietnam
    Duc Hoang Xuan
    The University of Danang - University of Science and Technology, Danang, Vietnam
    Tuan Dao-Duy
    The University of Danang - University of Science and Technology, Danang, Vietnam
    Hoang Le Uyen Thuc
    The University of Danang - University of Science and Technology, Danang, Vietnam

Từ khóa:

Face mask detection
Face recognition
MobileNetV2 model
Random Forest Classifier

Tóm tắt

The Covid-19 pandemic has abruptly changed our daily life: face mask and wearing a mask have become popular, and are occasionally obligatory for some public activities. As a result, several methods were proposed to address the problem of face mask detection and/or face recognition. While these methods showed promising results, they often require high computational resource due to sophisticated deep learning models. In this paper, we will propose lightweight methods to detect face masks and recognize human faces simultaneously. Our proposed methods are based mainly on Random Forest and MobileNetV2, and are trained and tested with our own dataset collected from Vietnamese faces. The experiment shows that Random Forest can address face mask detection and face recognition with high accuracy. Moreover, a combination of Random Forest and MobileNetV2 can still improve the performance of detection and recognition while keeping the method at relatively low computational complexity.

Tài liệu tham khảo

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Đã Xuất bản

Jun 30, 2022
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Cách trích dẫn
Ho-Phuoc, T. ., H. T. M. Tran, D. H. Xuan, T. Dao-Duy, và H. L. U. Thuc. “Lightweight Face Mask Detection and Face Recognition”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 20, số p.h 6.2, Tháng Sáu 2022, tr 38-43, doi:10.31130/ud-jst.2022.243ICT.

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