EER Calculation and DET Approximation in a Multi-Threshold Biometric System
Bibliographic record
Abstract
Identity security in Health Information Systems is a major concern in order to guarantee patients confidentiality. Biometrics has been used in order to improve traditional identity authentication. In this work, we present a method to get the Detection Error Trade-off—DET—graph when two or more thresholds are involved in a biometric system. Consequently, we also provide a method to calculate the Equal Error Rate—EER—in a multi-threshold system. In general, we can calculate the EER visually through the DET graph or with a formula. However, the formula is applicable only with normal distributed data. Biometric data distribution is usually not normal; therefore, we cannot always use the formula to calculate the EER. We will be able to use this method in a biometric system with any type of data distribution and with any number of thresholds. The obtained results show that the difference of EER calculated with our method and the one calculated with the formula have a standard deviation of 0.62%. These findings will contribute in the medicine field to better ensure the health information privacy of the patient.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".