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.
Original language | English |
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Article number | 5168 |
Number of pages | 9 |
Journal | Nature Communications |
Volume | 13 |
DOIs | |
Publication status | Published - 7 Sep 2022 |
Keywords
- Lip-reading
- face masks
- COVID-19
- lipreading
- neural networks, computer
- humans
- personal protective equipment
- COVID-19/prevention & control
- masks
ASJC Scopus subject areas
- General
- Physics and Astronomy(all)
- Chemistry(all)
- Biochemistry, Genetics and Molecular Biology(all)