Singular-minutiae points relationship-based approach to fingerprint matching
Bibliographic record
Abstract
Key issues with several of the existing fingerprint matching algorithms include problem of alignment of two minutiae feature vectors, failure with noisy image and nonlinear distortions. In this study, singular-minutiae points relationship-based algorithm is proposed for addressing the problem of alignment of two minutiae feature vectors in fingerprint matching. The algorithm uses the minutiae in the neighborhood of the core or delta point for promoting accuracy and reduction in computation by taking advantage of the fact that same source images of equal dimensions maintain same distance for every minutia point and the core/delta point irrespective of orientation. Results of experiments on local fingerprints and FVC2006 standard fingerprint databases were classified into correct, false positive and false negative. With correct results, the reference fingerprint is correctly matched to one or more fingerprints from the same person while in the case of false positive; the reference fingerprint is matched to one or more fingerprints of another person. False negative results were recorded for cases where the reference image refused to match with any of the fingerprints in the database. Based on FVC2006 fingerprints database, the Receiver Operating Characteristics (ROC) curves were also generated for the proposed algorithm and some recently formulated ones. Analysis of obtained results in all cases shows very good performance of the new algorithm.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".