Face Mask Detection at the Fog Computing Gateway

• Autorzy: Rudraraju Srinivasa Raju , Suryadevara Nagender Kumar , Negi Atul

Identyfikatory

DOI

10.15439/2020F143

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

distributed processing; image classification; internetworking; network servers; video streaming

PL

przetwarzanie rozproszone; klasyfikacja obrazu; intersieć; serwery sieciowe; streaming

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 521--524
• Opis fizyczny: Bibliogr. 16 poz.,

Twórcy

autor

Rudraraju Srinivasa Raju

• School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

autor

Suryadevara Nagender Kumar

• School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

autor

Negi Atul

• School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

Bibliografia

• 1. J. Howard, A. Huang, Z. Li, Z. Tufekci, V. Zdimal, H. van der Westhuizen et al., "Face Masks Against COVID-19: An Evidence Review", Preprints 2020, 2020040203, http://dx.doi.org/10.20944/preprints202004.0203.v1
• 2. S. Sarkar, R. Wankar, S. Srirama and N.K Suryadevra, “Serverless Management of Sensing Systems for Fog Computing Framework”, IEEE Sensors Journal, ISSN: 1530-437X, 20(3):1564-1572, 2020, http://dx.doi.org/10.1109/JSEN.2019.2939182
• 3. S. R. Rudraraju, N. K. Suryadevara and A. Negi, "Face Recognition in the Fog Cluster Computing," IEEE International Conference on Signal Processing, Information, Communication & Systems, 2019, pp. 45-48, http://dx.doi.org/10.1109/SPICSCON48833.2019.9065100
• 4. N. N. Khan, “Fog computing: A better solution for IoT,” International Journal of Engineering and Technical Research., vol. 3, no. 2, pp. 298–300, 2015, ISSN: 2321-0869
• 5. M. Aazam, S. Zeadally, and K. A. Harrass, “Fog Computing Architecture, Evaluation, and Future Research Directions”, IEEE Communications Magazine (2018), pp. 46-52
• 6. A. Nag, J. N. Nikhilendra, and M. Kalmath, "IOT Based Door Access Control Using Face Recognition", International Conference for Convergence in Tech., http://dx.doi.org/10.1109/i2ct.2018.8529749
• 7. P. Hu, H. Ning, T. Qiu, Y. Zhang, and X. Luo, "Fog Computing Based Face Identification and Resolution Scheme in Internet of Things", IEEE Transactions on Industrial Informatics, 13(4), 1910–1920, 2017, http://dx.doi.org/10.1109/tii.2016.2607178
• 8. COVID-19: Face Mask Detector, Last accessed 05 Jun 2020, URL:https://www.pyimagesearch.com/2020/05/04/covid-19-face-mask-detector-with-opencv-keras-tensorflow-and-deep-learning/
• 9. Face Mask Detection, Last accessed 10 Jun 2020, URL: https://www.towardsdatascience.com/covid-19-face-mask-detection-using-tensorflow-and-opencv-702dd833515b
• 10. OpenCV, Last accessed 19 May 2020, URL: https://opencv.org/about
• 11. Keras Guide, Last accessed 9 May 2020, URL: https://keras.io/guides/
• 12. TensorFlow Tutorials, Last accessed 10 Jun 2020, URL: https://www.tensorflow.org/tutorials
• 13. MobileNet, Last accessed 20 May 2020, URL: https://www.tensorflow.org/api_docs/python/tf/keras/applications/mobilenet
• 14. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand et al., “Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” https://arxiv.org/abs/1704.04861, 2017
• 15. Face Mask Dataset, Last accessed 21 Jun 2020, URL: https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset
• 16. Transfer Learning, Last accessed 21 May 2020, URL: https://www.tensorflow.org/tutorials/images/transfer_learning

Uwagi

1. Track 3: Network Systems and Applications

2. Technical Session: 4th Workshop on Internet of Things - Enablers, Challenges and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).