On Random Transformations for Changeable Face Verification
Why this work is in the frame
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Bibliographic record
Abstract
The generation of changeable and privacy-preserving biometric templates is important for the pervasive deployment of biometric technology in a wide variety of applications. This paper presents a systematic analysis of random transformation-based methods for addressing the changeability and privacy problems in biometrics-based verification systems. The proposed methods transform the original biometric feature vectors using random transformations, and the sorted index numbers (SIN) of the resulting vectors in the transformed domain are stored as the biometric templates. Three types of random transformations, namely, random additive transform, random multiplicative transform, and random projection, are discussed and analyzed. The random transformations, in combination with the SIN approach, constitute repeatable and noninvertible transformations; hence, the generated templates are changeable and provide privacy protection. The effectiveness of the proposed methods is well supported by both detailed analysis and extensive experimentation on a face verification problem.
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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 | 0.000 | 0.001 |
| 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