A High-Performance Eye Tracking Method Based on Event Camera and Dual-Channel Differential Illumination

SONG Sishun; FENG Junchi; PU Chengyu; GUO Yu; LIU Shijie; HE Xin; CHENG Yuwei

doi:10.11999/JEIT251162

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SONG Sishun, FENG Junchi, PU Chengyu, GUO Yu, LIU Shijie, HE Xin, CHENG Yuwei. A High-Performance Eye Tracking Method Based on Event Camera and Dual-Channel Differential Illumination[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251162

Citation:

SONG Sishun, FENG Junchi, PU Chengyu, GUO Yu, LIU Shijie, HE Xin, CHENG Yuwei. A High-Performance Eye Tracking Method Based on Event Camera and Dual-Channel Differential Illumination[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251162

Citation:

SONG Sishun, FENG Junchi, PU Chengyu, GUO Yu, LIU Shijie, HE Xin, CHENG Yuwei. A High-Performance Eye Tracking Method Based on Event Camera and Dual-Channel Differential Illumination[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251162

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A High-Performance Eye Tracking Method Based on Event Camera and Dual-Channel Differential Illumination

doi: 10.11999/JEIT251162 cstr: 32379.14.JEIT251162

Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China

Funds: Zhejiang Provincial “Jianbing Lingyan” Research and Development Program of China (2025002039), Research Funds of Hangzhou Institute for Advanced Study, UCAS (B02006C021026)

Received Date: 2025-10-31
Accepted Date: 2026-03-05
Rev Recd Date: 2025-12-01

Available Online: 2026-03-18

Abstract

Abstract

Objective Eye tracking has become an essential technology in human–computer interaction, medical diagnostics, cognitive neuroscience, and augmented/virtual reality applications. However, traditional eye tracking systems often suffer from two major limitations: low spatial accuracy and restricted temporal resolution, particularly in high-speed eye movement scenarios. These limitations hinder precise gaze estimation and reduce the reliability of real-time interactive systems. To address these challenges, this research integrates an event camera with the dual-channel differential illumination strategy to enhance the signal-to-noise ratio of corneal reflection events. By introducing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, accurate localization of corneal reflection points is achieved. On this basis, the corneal reflection point coordinates are utilized in combination with Singular Value Decomposition (SVD) and the least-squares method to determine the corneal curvature center, thereby significantly improving the accuracy of gaze direction estimation. This research provides an efficient technical pathway for next-generation eye tracking systems and offers theoretical support for their deployment in complex interactive environments. Methods The proposed event-camera-based gaze tracking method integrates asynchronous eye movement event data through a dual-channel differential illumination framework, thereby enhancing gaze direction estimation accuracy under high-speed and dynamic conditions. Firstly, the event camera asynchronously captures brightness-change events with microsecond-level temporal resolution, enabling precise tracking of rapid eye movements, while the dual-channel differential illumination mechanism suppresses redundant reflections and enhances the contrast of corneal reflection points. Secondly, the DBSCAN algorithm is employed to process event data, effectively removing noise and optimizing the spatial localization accuracy of corneal reflection features. Finally, a ray-tracing model is reconstructed using SVD and least-squares fitting to determine the corneal curvature center, thereby achieving robust and high-precision gaze direction estimation. Experimental results on a biomimetic eye movement dataset demonstrate that the proposed method achieves high temporal resolution, localization accuracy, and robustness in dynamic tracking scenarios. Results and Discussions Experiments demonstrate that the proposed method achieves a temporal resolution of 25 kHz (Fig. 6), far exceeding conventional cameras. Differential illumination significantly improves the signal-to-noise ratio of corneal reflection events. The DBSCAN algorithm localizes corneal reflection points more efficiently than K-Means, Agglomerative Clustering, Mean Shift, and OPTICS, achieving accurate results within 10 ms without requiring predefined clusters (Fig. 8, Table 3). For gaze estimation, the proposed method maintains stable accuracy across sampling frequencies from 2 kHz to 25 kHz. At a 15° cone angle, the mean error (ME) and root mean square error (RMSE) are approximately 0.66° and 0.67°, respectively, while at 25° they increase slightly to 0.87° and 0.90° (Table 4). Compared with existing state-of-the-art (SOTA) gaze tracking methods, the proposed approach demonstrates superior overall performance in terms of both temporal resolution and accuracy (Table 5) Trajectory results (Fig. 9) show close alignment between estimated and ground truth gaze paths, and distribution analyses (Fig. 10) confirm concentrated error ranges below 1°. Conclusions This paper presents a novel eye tracking method integrating event cameras, dual-channel differential illumination. The method achieves high temporal resolution (25 kHz), enhances event signal quality, and reduces localization errors, yielding gaze estimation errors of less than 1°. The proposed approach provides a reliable technical pathway for next-generation high-performance eye tracking systems. Future work should consider sensor noise modeling and computational optimization to further improve real-world applicability.

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References(31)

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