Effects of Segmentation Routine and Acquisition Environment on Iris Recognition
Bibliographic record
Abstract
Every year we see a growing use of iris recognition, with it now utilized as a means of border control in a number of countries, including the United Kingdom, Canada, and the United Arab Emirates. As this technology becomes more common and more relied upon, the importance of algorithms that can identify subjects in a robust, consistent, and accurate manner becomes all-important. Working with a collection of over 20,000 iris images captured in 2008, we determine optimal parameters for different elements of the texture encoding process. Additional work was done to improve the segmentation process, both to handle the introduction of images captured with the LG 4000 and to improve iris segmentation and eyelid masking. Furthermore, we study the relative biometric performance of images captured with the LG 2200 based on which of three illuminants were used to light the eye in each image, and determine the same- and cross-sensor performance of the LG 2200 compared with the LG 4000.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 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".