Drift Calibration in Geometric Eye Tracking Systems

• URL: http://arxiv.org/abs/2608.29739v1
• Date: Sun, 30 Aug 2026 12:05:44 GMT
• Title: Drift Calibration in Geometric Eye Tracking Systems
• Abstract summary: We present a calibration-focused dataset containing 163 trials from 12 participants.<n>We use it to evaluate global, local, and composite correction functions under a common spatial-extrapolation protocol.
• Score: 34.12106856838315
• License: http://creativecommons.org/licenses/by/4.0/
• 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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