ECG for blind identity verification in distributed systems
Bibliographic record
Abstract
This paper discusses ECG biometric recognition in a distributed system, such as smart cards. In a setting where every card is equipped with an ECG sensor to record heart beats from the fingers, and to subsequently perform identity verification, the interest is in protecting the card holder from a set of unknown/unseen biometric traits. Prior works have examined ECG biometrics in settings where a particular subject was to be identified among a set of enrollees. However, this treatment limits the applicability of this biometric. The Autocorrelation - Linear Discriminant Analysis (AC/LDA) is revisited, to propose a strategic extension of the methodology, in order to account for recognition among unknown individuals (blind verification). The discriminant is trained individually for every smart card, on the samples of the subject to be enrolled, as well as a generic dataset of ECG recordings. This enables the recognizer to protect the template against attacks by biometric samples that have not been used to train the discriminant. In addition, we present a methodology for the selection of the matching threshold, which targets to control false acceptance while being experimentally optimized for a particular smart card.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".