EyeEcho: Continuous and Low-power Facial Expression Tracking on Glasses
February 13, 2024 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Ke Li, Ruidong Zhang, Siyuan Chen, Boao Chen, Mose Sakashita, François Guimbretière, Cheng Zhang
arXiv ID
2402.12388
Category
cs.HC: Human-Computer Interaction
Citations
26
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
In this paper, we introduce EyeEcho, a minimally-obtrusive acoustic sensing system designed to enable glasses to continuously monitor facial expressions. It utilizes two pairs of speakers and microphones mounted on glasses, to emit encoded inaudible acoustic signals directed towards the face, capturing subtle skin deformations associated with facial expressions. The reflected signals are processed through a customized machine-learning pipeline to estimate full facial movements. EyeEcho samples at 83.3 Hz with a relatively low power consumption of 167 mW. Our user study involving 12 participants demonstrates that, with just four minutes of training data, EyeEcho achieves highly accurate tracking performance across different real-world scenarios, including sitting, walking, and after remounting the devices. Additionally, a semi-in-the-wild study involving 10 participants further validates EyeEcho's performance in naturalistic scenarios while participants engage in various daily activities. Finally, we showcase EyeEcho's potential to be deployed on a commercial-off-the-shelf (COTS) smartphone, offering real-time facial expression tracking.
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