An extended-shadow-code based approach for off-line signature verification. II. Evaluation of several multi-classifier combination strategies
Why this work is in the frame
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Bibliographic record
Abstract
For pt.I see Proc. 12th ICPR, p.450-3. In a real situation, the choice of the best representation R(/spl gamma/) for the implementation of a signature verification system able to cope with all types of handwriting is a very difficult task. This study is original in that the design of the integrated classifiers E(x) is based on a large number of individual classifiers e/sub k/(x) (or signature representations R(/spl gamma/)) in an attempt to overcome in some way the need for feature selection. In this paper, the authors present a first systematical evaluation of a multi-classifier-based approach for off-line signature verification. Two types of integrated classifiers based on kNN or minimum distance classifiers and 15 types of representation related to the ESC used as a shape factor have been evaluated using a signature database of 800 images (20 writers/spl times/40 signatures per writer) in the context of random forgeries.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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