The percentile-matching technique for synthetic eye tracking signal degradation: A biometric case study
Henry Griffith, Dmytro Katrychuk, Mehedi H. Raju, Samantha Aziz, Dillon J. Lohr, Oleg V. Komogortsev, Elochukwu Ukwandu, Elochukwu Ukwandu, Elochukwu Ukwandu, Elochukwu Ukwandu
TL;DR
This paper introduces a new method to simulate lower-quality eye tracking signals, improving realism for biometric authentication tasks.
Contribution
The novel percentile-matching technique enhances synthetic signal degradation realism for biometric applications.
Findings
The percentile-matching technique outperforms baselines in approximating target signal quality metrics.
The model improves median classification accuracy by 35.7% toward the ideal 50%.
The method is validated using EyeLink 1000 and SMI eye tracker data in a VR platform.
Abstract
This manuscript demonstrates an improved model-based approach for synthetic degradation of pre-recorded eye movement signals. Recordings from a high-quality eye tracking sensor are transformed to make their eye tracking signal quality resemble ones captured on a lower-quality target device. The proposed model improves the realism of the degraded signals versus prior approaches by introducing a mechanism for degrading spatial accuracy and temporal precision. Specifically, a percentile-matching technique is developed for mimicking the relative distributional structure of the target data signal quality characteristics. The model is demonstrated to improve realism on a per-feature and per-recording basis using data from EyeLink 1000 and SMI eye tracker embedded within a virtual reality platform. This study is first to show that the percentile-matching technique enables more accurate…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · EEG and Brain-Computer Interfaces · Visual Attention and Saliency Detection