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.

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.

Citation

Hameed, H., Usman, M., Tahir, A., Hussain, A., Abbas, H., Cui, T. J., …Abbasi, Q. H. (2022). Pushing the limits of remote RF sensing by reading lips under the face mask. Nature Communications, 13(1), Article 5168. https://doi.org/10.1038/s41467-022-32231-1

• 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