An Automatic Personal Calibration Procedure for Advanced Gaze Estimation Systems
Bibliographic record
Abstract
Gaze estimation systems use calibration procedures to estimate subject-specific parameters that are needed for the calculation of the point-of-gaze. In these procedures, subjects are required to fixate on a specific point or points in space at specific time instances. Advanced remote gaze estimation systems can estimate the optical axis of the eye without any personal calibration procedure, but use a single calibration point to estimate the angle between the optical axis and the visual axis (line-of-gaze). This paper presents a novel calibration procedure that does not require active user participation. To estimate the angles between the optical and visual axes of each eye, this procedure minimizes the distance between the intersections of the visual axes of the left and right eyes with one or more observation surfaces (displays) while subjects look naturally at these displays (e.g., watching a video clip). Theoretical analysis and computer simulations show that the performance of the proposed procedure improves when the range of angles between the visual axes and vectors normal to the observation surfaces increases. Experiments with four subjects show that the subject-specific angles between the optical and visual axes can be estimated with an rms error of 0.5 degrees.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".