Double random phase encoding for cancelable face and iris recognition
Bibliographic record
Abstract
Most modern security systems depend on biometrics. Unfortunately, these systems have suffered from hacking trials. If the biometric databases have been hacked and stolen, the biometrics saved in these databases will be lost forever. Thus, there is a desperate need to develop new cancelable biometric systems. The basic concept of cancelable biometrics is to use another version of the original biometric template created through a one-way transform or an encryption scheme to keep the original biometrics safe and away from utilization in the system. In this paper, the optical double random phase encoding (DRPE) algorithm is utilized for cancelable face and iris recognition systems. In the proposed cancelable face recognition scheme, the scale invariant feature transform is used for feature extraction from the face images. The extracted feature map is encrypted with the DRPE algorithm. The proposed cancelable iris recognition system depends on the utilization of two iris images for the same person and features are extracted from both images. The features extracted from one of the iris images are encrypted with the DRPE algorithm, provided that the second phase mask used in the DRPE is generated from the other iris image features. This trend guarantees some sort of feature fusion between the two iris images into a single cancelable iris code and increases user privacy. Simulation results show good performance of the two proposed cancelable biometric schemes even in the presence of noise, especially with the proposed cancelable face recognition scheme.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".