IRIS Segmentation: Detecting Pupil, Limbus and Eyelids
Bibliographic record
Abstract
This paper presents an active contour model to accurately detect pupil boundary in order to improve the performance of iris recognition systems. The contour model takes into consideration that an actual pupil boundary is a near-circular contour rather than a perfect circle. Two types of controlling force models, introduced as internal and external forces, are designed to properly activate the contour and locate it over the pupil boundary. The internal forces are designed to smooth the curve as well as to keep it close to a circular shape by pushing the contour vertices to their local radial mean. The external forces, which are responsible for pulling the contour vertices toward the pupil boundary, are designed based on a circular-curve gradient measurement with a proper angular range with respect to the contour center. In addition, an iterative algorithm has been developed in order to capture limbus and eyelids. The developed algorithm iteratively searches the limbus and eyelids boundaries and excludes the detected eyelids areas that cover the iris. Excluding the eyelids leads to a more precise search for limbus in the next iteration and the search is completed when the circular parameters of the limbus converge to fixed values. The eyelid contours are modeled as elliptic curves considering the spherical shape of an eyeball and the search is based on the expected contour in different degrees of eye openness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".