Face Based Biometric Authentication with Changeable and Privacy Preservable Templates
Why this work is in the frame
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Bibliographic record
Abstract
Changeability, privacy protection, and verification accuracy are important factors for widespread deployment of biometrics based authentication systems. In this paper, we introduce a method for effective combination of biometrics data with user specific secret key for human verification. The proposed approach is based on discretized random orthonormal transformation of biometrics features. It provides attractive properties of zero error rate, and generates revocable and non-invertible biometrics templates. In addition, we also present another scheme where no discretization procedure is involved. The proposed methods are well supported by mathematical analysis. The feasibility of the introduced solutions on a face verification problem is demonstrated using the well known ORL and GT database. Experimentation shows the effectiveness of the proposed methods comparing with existing works.
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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 | 0.000 | 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