Human Vs. Automatic Measurement of Biometric Sample Quality
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 systems are designed to identify a person based on physiological or behavioral characteristics. In order to predict the utility of a particular image for identification, there is an interest in measures to calculate the biometric image quality. Such measures often assume (implicitly or explicitly) that human image quality evaluations are a gold standard. In order to test this assumption, we measured biometric image quality for face and iris recognition by 8 human volunteers and from 6 face recognition and 1 iris recognition algorithm. Algorithm quality measures were based on a log-linear fit of quality to genuine score values. Results indicate that human quality scores correlate strongly with each other (r=0.723 (iris), r=0.613 (face), p<0.001). Algorithm scores also correlate strongly with each other (r=0.534, p<0.001 (face)). However, human quality scores do not correlate with those from algorithms (r=0.234 (face), r=0.175 (iris))
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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