Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features
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
The European standard for access control imposes stringent performance requirements on commercial biometric technologies that few existing recognition systems are able to meet. In this correspondence paper, we present the first mouse dynamics biometric recognition system that fulfills this standard. The proposed system achieves notable performance improvement by developing separate models for separate feature groups involved. The improvements are achieved through the use of a fuzzy classification based on the Learning Algorithm for Multivariate Data Analysis and using a score-level fusion scheme to merge corresponding biometric scores. Evaluation of the proposed framework using mouse data from 48 users achieves a false acceptance rate of 0% and a false rejection rate of 0.36%.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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