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Accurate Eye-Tracking from Deflectometric Information using Deep Learning

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Version 3 2024-05-29, 14:35
Version 2 2024-02-15, 09:26
Version 1 2024-02-10, 07:05
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posted on 2024-05-29, 14:35 authored by Jiwon Choi, Jiazhang Wang, Tianfu Wang, Florian Willomitzer
We introduce an accurate eye-tracking method that exploits deflectometric information and uses deep learning to reconstruct the gaze direction. We demonstrate real world experiments with evaluated gaze errors below 1°.

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Preprint ID

111786

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This study proposes a novel eye-tracking approach that uses dense deflectometric data and significantly improves accuracy compared to traditional methods. By training a Convolutional Neural Network model to predict gaze direction from deflectometry correspondence maps, the method achieves a gaze error of less than 1° and requires only a single camera and screen. Real-world experiments confirm its effectiveness, while future research aims to evaluate its performance on human eyes and explore further enhancements. -- Mousa Moradi, Ph.D., Biomedical Engineering, UMASS Amherst

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    Keywords

    Eye-TrackingDeflectometryArtificial intelligenceDeep LearningVirtual RealityComputational Imaging

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    Optica Publishing Group Non-Exclusive License

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