Using infrared illumination to improve eye & face tracking in low quality video images
Bibliographic record
Abstract
We propose a novel eye and face tracking algorithm using active infrared (IR) illumination. Most eye trackers based on active IR illumination require bright pupil images to successfully detect eyes in image sequences. However, due to factors such as eye closure, head rotation, variation in illumination and occlusion, most trackers tend to fail in these situations where pupil shows weak reflections. Our proposed method overcomes these limitations by making use of the dark-bright pupil difference images as well as using adaptive thresholding techniques. The core computational module of the algorithm is based on the Kalman filter and adaptive template matching to find and update the most probable eye position in the current frame. Our eye tracker can robustly detect faces and track eyes in a sequence of images under variable lighting conditions and face orientations. Experiments show good performance in challenging image sequences with low quality and occluded images with the subject showing considerable head movements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".