Integrating monomodal biometric matchers through logistic regression rank aggregation approach
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
Biometric system relies on person's behavioral and/or physiological characteristics as an alternative means of person authentication (traditional means being password, smart card, ID etc.). However, biometric system based solely on a single biometric may not always meet security requirements. Thus multibiometric systems are emerging as a trend which helps in overcoming limitations of single biometric solutions, such as when a user does not have a quality sample to present to the system and reduces the ability of the system to be tricked fraudulently. A reliable and successful multibiometric system needs an effective fusion scheme to integrate the information presented by multiple matchers. In this research, we integrate results of three monom.odal biometric matchers (face, ear and iris) with the logistic regression approach of rank level fusion method. In this approach, not only the outcomes of the three mono-modal matchers are considered, but also their effectiveness, based on previous research, are also considered for final rank aggregation. Experiment results indicate that Logistic Regression method outperform Borda count method or plurality voting method. The system can be a contribution to the homeland and border security or other security applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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