Annals of Computer Science and Information Systems, Volume 21

DOI: http://dx.doi.org/10.15439/2020F143

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 521–524 (2020)

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Abstract. This work proposes a fog computing-based system for face mask detection that controls the entry of a person into a facility. The proposed system uses fog nodes to process the video streams captured at various entrances into a facility. Haar-cascade-classifiers are used to detect face portions in the video frames. Each fog node deploys two MobileNet models, where the first model deals with the dichotomy between mask and no mask case. The second model deals with the dichotomy between proper mask wear and improper mask wear case and is applied only if the first model detects mask in the facial image. This two-level classification allows the entry of people into a facility, only if they wear the mask properly. The proposed system offers performance benefits such as improved response time and bandwidth consumption, as the processing of video stream is done locally at each fog gateway without relying on the Internet.

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