Covert monitoring of the point-of-gaze
Bibliographic record
Abstract
Gaze estimation systems use calibration procedures that require active subject participation to estimate the point-of-gaze accurately. Consequently, these systems do not support covert monitoring of visual scanning patterns. This paper presents a novel gaze estimation methodology that does not use calibration procedures that require active user participation. This methodology uses multiple infrared light sources for illumination and a stereo pair of video cameras to obtain images of the eyes. Each pair of images is analyzed and the centers of the pupils and the centers of curvature of the corneas are estimated. These points, which are estimated without a personal calibration procedure, define the optical axis of each eye. To estimate the point-of-gaze, which lies along the visual axis, the angle between the optical and visual axes is estimated by a procedure that minimizes the distance between the intersections of the visual axes of the left and right eyes with the surface of a display while subjects look naturally at the display (e.g., watching a video clip). Simulation results demonstrate that for a subject sitting 75 cm in front of an 80 cm × 60 cm display (40" TV) the RMS error of the estimated point-of-gaze is 17.8 mm (1.3°).
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".