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Pushing the limits of remote RF sensing by reading lips under the face mask

Hameed, Hira; Usman, Muhammad; Tahir, Ahsen; Hussain, Amir; Abbas, Hasan; Cui, Tie Jun; Imran, Muhammad Ali; Abbasi, Qammer H.

Authors

Hira Hameed

Muhammad Usman

Ahsen Tahir

Hasan Abbas

Tie Jun Cui

Muhammad Ali Imran

Qammer H. Abbasi



Abstract

The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so far are camera-based, which require video recording of the target. However, these technologies have well-known limitations of occlusion and ambient lighting with serious privacy concerns. Furthermore, vision-based technologies are not useful for multi-modal hearing aids in the coronavirus (COVID-19) environment, where face masks have become a norm. This paper aims to solve the fundamental limitations of camera-based systems by proposing a radio frequency (RF) based Lip-reading framework, having an ability to read lips under face masks. The framework employs Wi-Fi and radar technologies as enablers of RF sensing based Lip-reading. A dataset comprising of vowels A, E, I, O, U and empty (static/closed lips) is collected using both technologies, with a face mask. The collected data is used to train machine learning (ML) and deep learning (DL) models. A high classification accuracy of 95% is achieved on the Wi-Fi data utilising neural network (NN) models. Moreover, similar accuracy is achieved by VGG16 deep learning model on the collected radar-based dataset.

Journal Article Type Article
Acceptance Date Jul 21, 2022
Online Publication Date Sep 7, 2022
Publication Date 2022
Deposit Date Sep 14, 2022
Publicly Available Date Sep 14, 2022
Journal Nature Communications
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 13
Issue 1
Article Number 5168
DOI https://doi.org/10.1038/s41467-022-32231-1
Public URL http://researchrepository.napier.ac.uk/Output/2913691

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http://creativecommons.org/licenses/by/4.0/

Copyright Statement
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.





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