Conventional eye trackers are difficult to deploy in fashion-runway settings because they require head stabilization, wearable devices, close-range cameras, or infrared illumination. This study develops a far-field, non-contact eye-tracking framework and prototype system for measuring target-directed visual attention i...
Advances in computer vision and mobile sensing enable the study of development in naturalistic environments, yet existing motion tracking systems lack the integration of automated analysis and accessibility needed in developmental research. Current approaches force trade-offs between precision and ecological validity,...
Appearance-based gaze estimation offers a low-cost alternative to infrared eye tracking for screen-based behavioral and clinical applications, however existing models are typically developed for wide ranges of gaze angle and head pose. Prolonged screen viewing presents a distinct regime in which gaze remains near the s...
Eye-movement authentication in virtual reality (VR) needs longitudinal training data, yet most such datasets come from lab trackers. We convert lab recordings to a VR headset's signal quality while keeping their eye movement, using only unpaired VR recordings of other people. Measurements show that the headset's added...
Eye gaze is the fastest natural pointing signal available to interactive systems, but for three decades accurate gaze interaction has remained tied to dedicated infrared eyetracking hardware. The convergence of deep appearance-based gaze estimation, ubiquitous front-facing cameras, and on-device neural inference has op...
Results from four repeated experiments demonstrate that DG²C consistently outperforms baseline methods across all evaluation metrics, achieving a pixel error of 31.49 px, an angular error of 0.76°, and an intersession coefficient of variation of variation of 19.8%.
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