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System for Medical Mask Detection in the Operating Room Through Facial Attributes

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9117))

Abstract

This paper introduces a system that detects the presence or absence of the mandatory medical mask in the operating room. The overall objective is to have as few false positive face detections as possible without losing mask detections in order to trigger alarms only for healthcare personnel who do not wear the surgical mask. The medical mask detection is performed with two face detectors; one of them for the face itself, and the other one for the medical mask. Both detectors run color processing in order to enhance the true positives to false positives ratio. The proposed system renders a recall above 95 % with a false positive rate below 5 % for the detection of faces and surgical masks. The system provides real-time image processing, reaching 10 fps on VGA resolution when processing the whole image. The Mixture of Gaussians technique for background subtraction increases the performance up to 20 fps on VGA images. VGA resolution allows for face or mask detection up to 5 m from the camera.

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Acknowledgment

This work was supported by the Galician Ministry of Education under grant EM2014/012, MINECO (Spain) under project TEC2012-38921-C02-02 (European Regional Development Fund (ERDF/FEDER)), and the ERDF/FEDER under the project CN2012/151 of the Galician Ministry of Education.

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Correspondence to M. Mucientes .

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© 2015 Springer International Publishing Switzerland

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Nieto-Rodríguez, A., Mucientes, M., Brea, V.M. (2015). System for Medical Mask Detection in the Operating Room Through Facial Attributes. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-19390-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19389-2

  • Online ISBN: 978-3-319-19390-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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