Abstract
The ongoing health crisis continues to impact all walks of life, demanding radical lifestyle changes, and forcing us to comply with a whole new host of precautionary regulations. Poor hygiene practices are in part responsible for the soaring COVID-19 cases, self-inoculation being one of them. Recent studies in this area have highlighted how face-touching provides us with immediate relief from temporary discomforts such as muscle tension, and is a way of regulating emotions and stimulating memory. This paper is aimed at exhibiting the role of Artificial Intelligence and Machine Learning in developing a fully software-based system to help restrict this instinct. A web application has been designed, which takes in real-time input of a person through a built-in PC webcam, and detects the intersection between the hands and facial regions, resulting in a warning alert as an output to the user. Making use of BodyPix 2.0 in TensorFlow, this novel method has been found to have an accuracy of 91.3%, with proper synchronization and minimum delay in detection. The feasibility of such an approach has thus been discussed herein, giving an insight into how smart hygiene control techniques could help in the management of any such future catastrophes stemming from similar deadly biological agents, lest history repeats itself.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Khasawneh, A.I., et al.: Medical students and COVID- 19: knowledge, attitudes, and precautionary measures. a descriptive study from Jordan. Front. Publ. Health 8(263), 2 (2020)
Jayaweera, M., Perera, H., Gunawardana, B., Manatunge, J.: Transmission of COVID-19 virus by droplets and aerosols: a critical review on the unresolved dichotomy. Environ. Res. 188, 4–5 (2020)
Rahman, J., Mumin, J., Fakhruddin, B.: How frequently do we touch facial T-Zone: a systematic review. Ann. Global Health Art 75, 1 (2020)
Kwok, Y.L.A., Gralton, J., McLaws, M.-L.: Face touching: a frequent habit that has implications for hand hygiene. Am. J. Infect. Control 43 (2015)
Mueller, S.M., Martin, S., Grunwald, M.: Self-touch: Contact durations and point of touch of spontaneous facial self-touches differ depending on cognitive and emotional load. PLOS ONE (2019)
Esposito, F., Malerba, D.: Editorial: Machine Learning In Computer Vision. Applied Artificial Intelligence. Italian Association for Artificial Intelligence, Bari (2010)
Simon, A., Deo, M.S., Venkatesan, S., Babu, D.R.R.: An overview of machine learning and its applications. Int. J. Electric. Sci. Eng. 1(1), 22 (2016)
Visée, R.J., Likitlersuang, J., Zariffa, J: IEEE Transactions on Neural Systems and Rehabilitation Engineering, p. 2 (2020)
BodyPix 2.0 – Person Segmentation in the Browser. Adafruit, 20 Nov. 2019. https://blog.adafruit.com/2019/11/20/bodypix-2-0-person-segmentation-in-the-browser-tensorflow-machinelearning-deeplearning-tylerzhu3-oveddan/. Accessed on 15 Oct 2020
[Updated] BodyPix: Real-time Person Segmentation in the Browser with TensorFlow.js. TensorFlow, 18 Nov. 2019. https://blog.tensorflow.org/2019/11/updated-bodypix-2.html. Accessed on 17 Oct 2020
Vino, M.: Edit live video background with WebRTC and TensorFlow.js. Francium Tech, 30 April 2020. https://blog.francium.tech/edit-live-video-background-with-webrtc-and-tensorflow-js-c67f92307ac5. Accessed 15 Oct 2020
[BodyPix] single person detection in latest bodypix. GitHub, 17 Dec. 2019. https://github.com/tensorflow/tfjs/issues/2547. Accessed on 15 Oct 2020
BodyPix: Person Segmentation in the browser. SymmetricalDataSecurity, 10 Oct. 2019. http://symmetricaldatasecurity.blogspot.com/2019/10/bodypix-person-segmentation-in-browser.html. Accessed 15 Oct 2020
BodyPix- Person Segmentation in the Browser. https://github.com/tensorflow/tfjs-models/tree/master/body-pix#person-segmentation. Accessed on 18 Oct 2020
Aniket, M.: Facial recognition algorithms. Engati. https://www.engati.com/blog/facial-recognition-systems. Accessed 15 Oct 2020
Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the conference on Computer Vision and Pattern Recognition, vol. 1, p. 3, Feb 2001
Face detection using Haar Cascades. OpenCV-Python Tutorials. https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_objdetect/py_face_detection/py_face_detection.html. Accessed on 16 Oct 2020
Johnston, B., de Chazal, P.: A review of image-based automatic facial landmark identification techniques. EURASIP J. Image Video Process. 13, 7 (2018)
Arun, P.: CNN based face detector from dlib. Towards Data Science, 17 April 2018. https://towardsdatascience.com/cnn-based-face-detector-from-dlib-c3696195e01c. Accessed on 16 Oct 2020
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multi- task cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1–2 (2016)
Ayoosh, K.: What’s new in YOLO v3? Towards Data Science, 23 April 2018. https://towardsdatascience.com/yolo-v3-object-detection-53fb7d3bfe6b. Accessed 10 Oct 2020
Ranjan, R., et al.: A fast and accurate system for face detection, identification, and verification. IEEE Trans. Biometric. Behav. Ident. Sci. 1(2), 4 (2019)
Padilla1, R., Netto, S.L., da Silva, E.A.B.: A survey on performance metrics for object- detection algorithms. In: Proceedings of International Conference on Systems, Signals and Image Processing (IWSSIP) (2020)
Dwiyantoro, A.: Performance showdown of publicly available face detection model. Nodeflux, 30 April 2018. https://medium.com/nodeflux/performance-showdown-of-publicly-available-face-detection-model-7c725747094a. Accessed on 10 Oct 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that no conflict of interest exists
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Patel, S., Madhani, H., Garg, S., Chauhan, M. (2021). An AI-Based Solution to Reduce Undesired Face-Touching as a Precautionary Measure for COVID-19. In: Chaubey, N., Parikh, S., Amin, K. (eds) Computing Science, Communication and Security. COMS2 2021. Communications in Computer and Information Science, vol 1416. Springer, Cham. https://doi.org/10.1007/978-3-030-76776-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-76776-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76775-4
Online ISBN: 978-3-030-76776-1
eBook Packages: Computer ScienceComputer Science (R0)