Abstract
Facial expression reconstruction technology offers considerable potential in areas like human-computer interaction, affective computing, and virtual reality. To tackle the privacy challenges and environmental constraints inherent in traditional camera-based systems, researchers have recently introduced ear-worn devices as a viable solution. Nevertheless, these methods still demand enhancements, particularly in aspects such as design appeal and energy efficiency, to achieve broader applicability and practical use. This paper introduces a system called IMUFace. It uses inertial measurement units (IMUs) embedded in wireless earphones to detect subtle ear movements caused by facial muscle activities, allowing for covert and low-power facial reconstruction. A user study involving 12 participants was conducted, and a deep learning model named IMUTwinTrans was proposed. The results show that IMUFace can accurately predict users’ facial landmarks with a precision of 2.21 mm, using only five minutes of training data. The predicted landmarks can be utilized to reconstruct a three-dimensional facial model. IMUFace operates at a sampling rate of 30 Hz with a relatively low power consumption of 58 mW. The findings validate the feasibility of IMUFace and highlight potential directions for further research towards its practical adoption in mobile environments.
| Original language | English |
|---|---|
| Pages (from-to) | 419-435 |
| Number of pages | 17 |
| Journal | CCF Transactions on Pervasive Computing and Interaction |
| Volume | 8 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 2026 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- 3D facial reconstruction
- Low-power
- Mobile computing
- Wearable sensing
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