Bibliographic record
Abstract
Using the palmprint in recognition systems has received a lot of interest during the last two decades. Some of these systems are based on first‐level features, such as the existing lines and creases in palmprint images, and others use second‐level features, such as minutiae, which are more reliable in comparison with the first group. Owing to a large number of minutiae in a palmprint, ∼1000 minutiae, the matching process is time consuming. In this study, a new minutia‐based matching strategy is proposed to make the matching process faster and more efficient. First, an orientation field estimation algorithm based on region‐growing is proposed, which emphasises selecting seed points with higher quality. Second, the estimated orientation field is used to align palmprint images to the same coordinate system, resulting in fewer computations during minutia matching. Finally, a new minutia descriptor based on the orientation field is designed to distinguish minutiae with different local orientation structures. This descriptor helps to find two mated minutiae much faster, speeding up the matching process. The proposed palmprint matching algorithm has been evaluated on the THUPALMLAB database, and the results show the superiority of the proposed algorithm over most of the state‐of‐the‐art algorithms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".