A stroke based algorithm for dynamic signature verification
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Bibliographic record
Abstract
Dynamic signature verification (DSV) uses the behavioral biometrics of a hand-written signature to confirm the identity of a computer user. This paper presents a novel stroke-based algorithm for DSV. An algorithm is developed to convert sample signatures to a template by considering their spatial and time domain characteristics, and by extracting features in terms of individual strokes. Individual strokes are identified by finding the points where there is a: 1) decrease in pen tip pressure, 2) decrease in pen velocity, and 3) rapid change in pen angle. A significant stroke is discriminated by the maximum correlation with respect to the reference signatures. Between each pair of signatures, the local correlation comparisons are computed between portions of pressure and velocity signals using segment alignment by elastic matching. Experimental results were obtained for signatures from 10 volunteers over a four-month period. The result shows that stroke based features contain robust dynamic information, and offer greater accuracy for dynamic signature verification, in comparison to results without using stroke features.
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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