Abstract
The COVID-19 pandemic requires everyone to wear a face mask in public areas. This situation expands the ability of a service robot to have a masked face recognition system. The challenge is detecting multi-view faces. Previous works encountered this problem and tended to be slow when implemented in practical applications. This paper proposes a real-time multi-view face mask detector with two main modules: face detection and face mask classification. The proposed architecture emphasizes light and robust feature extraction. The two-stage network makes it easy to focus on discriminating features on the facial area. The detector filters non-faces at the face detection stage and then classifies the facial regions into two categories. Both models were trained and tested on the benchmark datasets. As a result, the proposed detector obtains high performance with competitive accuracy from competitors. It can run 20.60 frames per second when working in real-time on Jetson Nano.
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Acknowledgment
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (2020R1A2C2008972).
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Putro, M.D., Nguyen, DL., Jo, KH. (2021). Real-Time Multi-view Face Mask Detector on Edge Device for Supporting Service Robots in the COVID-19 Pandemic. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_40
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DOI: https://doi.org/10.1007/978-3-030-73280-6_40
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