Pupil localization for gaze estimation using unsupervised graph-based model
Bibliographic record
Abstract
In this paper, we propose a graph-based model for pupil localization, which is a step towards gaze detection. The proposed model can differentiate the key points located at the eyelashes, eyebrows and eye white regions. We first crop the eye region with an ellipse and then estimate the pupil center within the ellipse, thus reducing the computational complexity. We also consider the light reflections in the pupil region, which could lead to inaccuracy in pupil localization. We construct an undirected graph in the eye region based on the key points consisting of the corner points in the eye region, the centers of light reflection regions, and the multiple pixels with a high intensity in the pupil region. The pupil center is initially estimated as the weighted center of the revised graph after vertex/edge removal. In addition, we shift the initial pupil center to a revised position based on the line segments in the pupil region. We evaluate the proposed method on 850 eye images from a public database. The experimental results demonstrate that the proposed method can achieve a more accurate result compared to the existing work.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".