Biometric Quality and its Impact on Template Ageing in a Longitudinal Fingerprint Study
Bibliographic record
Abstract
Biometrics are increasingly deployed in domains ranging from social media authentication right up to border control.An important operational requirement for biometric systems is the supposed uniqueness and permanence of biometric records: physiological changes occurring between enrolment and verication are referred to as template ageing, and increase the likelihood of a misidentication.Its magnitude is hard to estimate, and the factors aecting it are relatively little studied.This work proposes a measure of template ageing, called biometric permanence, and develops a methodology to estimate it in the presence of confounding factors.The measure is applied to a database of ngerprints obtained over a seven year period, using bootstrap resampling to obtain condence intervals for the estimates of eect size.Fingerprint quality metrics are evaluated in terms of their ability to predict classication performance, and the subject-dependence of ngerprint quality is explored using the ideas of a biometric menagerie.Statistically signicant demographic factors underlying biometric quality and template ageing are highlighted and discussed.The results of this work may have implications for the procurement and administration of biometric systems: for example, in ensuring consistent performance across a broad population demographic, and in the choice of credential lifetime and reenrolment policy. 2.2.2Forensic applications . . . . . . .
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".