Nine Years of Pediatric Iris Recognition: Evidence for Biometric Permanence
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
Biometric permanence in pediatric populations remains poorly understood despite widespread deployment of iris recognition for children in national identity programs such as India's Aadhaar and trusted traveler programs like Canada's NEXUS. This study presents a comprehensive longitudinal evaluation of pediatric iris recognition, analyzing 276 subjects enrolled between ages 4-12 and followed up to nine years through adolescence. Using 18,318 near-infrared iris images acquired semi-annually, we evaluated commercial (VeriEye) and open-source (OpenIris) systems through linear mixed-effects models that disentangle enrollment age, developmental maturation, and elapsed time while controlling for image quality and physiological factors. False non-match rates remained below 0.5% across the nine-year period for both matchers using pediatric-calibrated thresholds, approaching adult-level performance. However, we reveal significant algorithm-dependent temporal behaviors: VeriEye's apparent decline reflects developmental confounding across enrollment cohorts rather than genuine template aging, while OpenIris exhibits modest but genuine temporal aging (0.5 standard deviations over eight years). Image quality and pupil dilation constancy dominated longitudinal performance, with dilation effects reaching 3.0-3.5 standard deviations, substantially exceeding temporal factors. Failures concentrated in 9.4% of subjects with persistent acquisition challenges rather than accumulating with elapsed time, confirming acquisition conditions as the primary limitation. These findings justify extending conservative re-enrollment policies, potentially to 10-12 year validity periods for high-quality enrollments at ages 7+, and demonstrate iris recognition remains viable throughout childhood and adolescence with proper imaging control.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.026 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it