User-calibration-free remote eye-gaze tracking system with extended tracking range
Bibliographic record
Abstract
A novel general method to extend the tracking range of user-calibration-free remote eye-gaze tracking (REGT) systems that are based on the analysis of stereo-images from multiple cameras is presented. The method consists of two distinct phases. In the brief initial phase, estimates of the center of the pupil and corneal reflections in pairs of stereo images are used to estimate automatically a set of subject-specific eye parameters. In the second phase, these subject-specific eye parameters are used with estimates of the center of the pupil and corneal reflections in images from any one of the systems' cameras to compute the Point-of-Gaze (PoG). Experiments with a system that includes two cameras show that the tracking range for horizontal gaze directions can be extended from ±23.2° when the two cameras are used as a stereo pair to ±35.5° when the two cameras are used independently to estimate the POG.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".