Drift Calibration in Geometric Eye Tracking Systems

Jiaqi Liu; Zixuan Wang; Yuhong Zhang; Dingkang Liang; Jane Hanqi Li; Tzyy-Ping Jung; Gert Cauwenberghs

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arXiv · 2608.29739

Drift Calibration in Geometric Eye Tracking Systems

Jiaqi Liu·Zixuan Wang·Yuhong Zhang·Dingkang Liang·Jane Hanqi Li·Tzyy-Ping Jung·Gert Cauwenberghs

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Abstract

Geometric eye trackers can provide the spatial accuracy required for gaze-based interaction and multimodal studies, but their measurements remain sensitive to residual session-specific calibration error. Research on correcting this error is difficult to compare because methods are typically evaluated with different devices, target layouts, and error definitions. We present a calibration-focused dataset containing 163 trials from 12 participants, with separate 18-point fitting and 32-point test grids, and use it to evaluate global, local, and composite correction functions under a common spatial-extrapolation protocol. We further introduce a lightweight neural refiner that combines ranked predictions from complementary calibrators. On this controlled dataset, post-vendor correction reduces the mean angular error from $1.53^\circ$ to $1.03^\circ$ with the strongest classical composite and to $0.96^\circ$ with the refiner. In a closed-loop gaze task, lower residual error is associated with higher performance across four online correction conditions. These results provide a reproducible data-quality benchmark for using gaze as a behavioral signal in interactive modeling.

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Jiaqi Liu, Zixuan Wang, Yuhong Zhang, Dingkang Liang, Jane Hanqi Li, Tzyy-Ping Jung, Gert Cauwenberghs. 2026-08-30. Drift Calibration in Geometric Eye Tracking Systems. https://arxiv.org/abs/2608.29739

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