Effects of Segmentation Routine and Acquisition Environment on Iris Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it