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SacCalib: Reducing Calibration Distortion for Stationary Eye Trackers Using Saccadic Eye Movements

MPS-Authors
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Huang,  Michael Xuelin
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1903.04047.pdf
(Preprint), 2MB

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Citation

Huang, M. X., & Bulling, A. (2019). SacCalib: Reducing Calibration Distortion for Stationary Eye Trackers Using Saccadic Eye Movements. Retrieved from http://arxiv.org/abs/1903.04047.


Cite as: http://hdl.handle.net/21.11116/0000-0003-2BF3-B
Abstract
Recent methods to automatically calibrate stationary eye trackers were shown to effectively reduce inherent calibration distortion. However, these methods require additional information, such as mouse clicks or on-screen content. We propose the first method that only requires users' eye movements to reduce calibration distortion in the background while users naturally look at an interface. Our method exploits that calibration distortion makes straight saccade trajectories appear curved between the saccadic start and end points. We show that this curving effect is systematic and the result of distorted gaze projection plane. To mitigate calibration distortion, our method undistorts this plane by straightening saccade trajectories using image warping. We show that this approach improves over the common six-point calibration and is promising for reducing distortion. As such, it provides a non-intrusive solution to alleviating accuracy decrease of eye tracker during long-term use.