maskedFaceNet: A Progressive Semi-Supervised Masked Face Detector

maskedFaceNet: A Progressive Semi-Supervised Masked Face Detector
Title:
maskedFaceNet: A Progressive Semi-Supervised Masked Face Detector
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IEEE Xplore
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Publication Date:
07 May 2021
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Abstract:
To reduce the risk of infecting or being infected by the recent COVID-19 virus, wearing mask is enforced or recommended by many countries. AI based system for automatically detecting whether individuals are wearing face mask becomes an urgent requirement in high risk facilities and crowded public places. Due to lacking of existing masked face datasets and the urgent low-cost application requirement, we propose a progressive semi-supervised learning method – called maskedFaceNet to minimize the efforts on data annotation and letting deep models to learn by using less annotated training data. With this method, the detection accuracy is further improved progressively while adapting to various application scenarios. Experimental results show that our maskedFaceNet is more efficient and accurate compared to other methods. Furthermore, we also contribute two masked face datasets for benchmarking and for the benefit of future research.
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Funding Info:
This research is supported by the Institute for Infocomm Research (I2R), A*STAR. Grant number is not applicable. Project details: “Masked face detection and recognition” under “MFDR”, FS-2020-002. In Visual Intelligence Department (VI).
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ISSN:
1803-7232
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