On-line signature verification by using most discriminating points
Bibliographic record
Abstract
In this paper, we propose a novel approach that looks for the most discriminating points in horizontal, vertical, velocity and pressure trajectories. Then based on these discriminating points, we have made two composite feature sets i.e. (horizontal; vertical) and (velocity; pressure). Out of these two composite feature sets, one will be declared as the most discriminating composite feature set during the training phase. Finally the verification based on the selected discriminating composite feature will be performed. We believe that every signer has some discriminating points that a forger cannot mimic with certain pressure and velocity whereas these points are maintained by the genuine signer. So the verification based on these discriminating points can lead us to a better performance of the verification system. For a reliable verification system, comparison between forgery and genuine signer should be made on the basis of discriminating points rather than using all the sample points of each trajectory in the verification which means that all the points are given equal weights and there is always a possibility that the non-discriminating points can overshadow the effect of discriminating points. Experimental results demonstrate superiority of our approach in On-line signature verification in comparison with other techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".