Securing handheld devices and fingerprint readers with ECG biometrics
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the feasibility of collecting viable biometric electrocardiogram (ECG) signals from fingertips. A system for biometric recognition on handheld electronic devices is proposed and analyzed from a flexibility and a permanence point of view. The recognition algorithm is based on the previously proposed Autocorrelation/ Linear Discriminant Analysis (AC/LDA) [3]. In order to assess the flexibility of the acquisition procedure from the fingertips, various electrode configurations are examined along with signal permanence over different signal collection periods. The experimental results indicate very promising error rates (EER of 8.7% over 22 subjects), for ECG data obtained when touching a sensing surface with the fingertips. The envisioned applications include security on handheld electronic devices, such as smartphones and smart cards, as well as multimodal systems which combine ECG and fingerprint biometric readers.
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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 | 0.000 | 0.000 |
| 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