Person Identification using Electrocardiograms
Bibliographic record
Abstract
In this paper, we demonstrate that the electrocardiogram (ECG) can be used as a biometric. While previous studies have shown the potential of an ECG biometric, this research demonstrates it under conditions that include intra-individual variations and a simple user interface, consisting of electrodes held on the pads of the subject's thumbs. ECG person identification was accomplished through quantitative comparisons of an unknown signal to enrolled signals. The quantitative comparisons were: the correlation coefficient and a wavelet distance measure. It was found that the combination of these two methods provided improved performance, relative to either individual method. ECG person identification accuracy was 90.8%. While this accuracy is relatively low compared to conventional biometrics, such as fingerprints, the ECG can be used as supplementary information for a multi-modal biometric system. A multi-modal system that includes the ECG would have increased accuracy and robustness, without necessarily requiring any change to the perceived user interface. At minimum, the ECG would be useful in providing liveness detection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".