RadEye: Tracking Eye Motion Using FMCW Radar

Eye motion tracking plays a vital role in many applications such as human-computer interaction (HCI), virtual reality, and disease detection.Camera-based eye tracking, albeit accurate and easy to use, may raise privacy concerns and appear to be unreliable in poor lighting conditions.In this paper, we present RadEye, a radar system capable of detecting fine-grained human eye motions from a distance.RadEye is realized through an integrated hardware and software design.It customizes a sub-6GHz FMCW radar so as to detect millimeter-level eye movement while extending its detection range using low frequency.It further employs a deep neural network (DNN) to refine the detection accuracy through camera-guided supervisory training.We have built a prototype of RadEye.Extensive experimental results show that it achieves 90% accuracy when detecting human eye rotation directions (up, down, left, and right) in various scenarios. CCS Concepts

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Publication Details

Published
2025-04-25
DOI
https://doi.org/10.1145/3706598.3713775
Citations
2
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
3.87

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article

RadEye: Tracking Eye Motion Using FMCW Radar

Huacheng Zeng, Qiben Yan, Shichen Zhang, Qingping Wang et al.
2 citations
Gaze Tracking and Assistive Technology
3.87
article

RadEye: Tracking Eye Motion Using FMCW Radar

Huacheng Zeng, Qiben Yan, Shichen Zhang, Qingping Wang, Kunzhe Song
article en
2 citations

Abstract

Eye motion tracking plays a vital role in many applications such as human-computer interaction (HCI), virtual reality, and disease detection.Camera-based eye tracking, albeit accurate and easy to use, may raise privacy concerns and appear to be unreliable in poor lighting conditions.In this paper, we present RadEye, a radar system capable of detecting fine-grained human eye motions from a distance.RadEye is realized through an integrated hardware and software design.It customizes a sub-6GHz FMCW radar so as to detect millimeter-level eye movement while extending its detection range using low frequency.It further employs a deep neural network (DNN) to refine the detection accuracy through camera-guided supervisory training.We have built a prototype of RadEye.Extensive experimental results show that it achieves 90% accuracy when detecting human eye rotation directions (up, down, left, and right) in various scenarios. CCS Concepts

Michigan State University (US)
National Science Foundation, Division of Electrical, Communications and Cyber Systems
Affordable and clean energy
Openalex Percentile: Top 8%
Gaze Tracking and Assistive Technology
3.87
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