EER Calculation and DET Approximation in a Multi-Threshold Biometric System
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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