Effect of parameter values on fingerprint filtering
Bibliographic record
Abstract
Fingerprint is presently the most significant biometric for human verification and identification. The reason being its highest degree of uniqueness, availability, durability and consistency when compared with other biometrics such as face, nose, iris, ear, palm print and signature. The use of fingerprint in human identity management spans through stages of enrolment, enhancement, feature extraction and pattern matching. The enhancement stage involves ridge segmentation, normalization, orientation estimation, frequency estimation, filtering, binarization and thinning. Filtering is the stage at which all forms of noise and contaminations introduced into the image during enrolment are removed. The removal of noise and contaminations is necessary for accurate feature extraction and pattern matching. In some of the existing fingerprint image filtering algorithms, accurate and appropriate parameter selections are essential for obtaining optimal and satisfactory results. In this research, the existing Gabor filter was modified and the values of some standard parameters were varied. Experimental study on the adequacy of the modified algorithm and its parameter values on fingerprint filtering were investigated on the standard FVC2002 fingerprint database. Comparative analysis of the obtained results with what were obtained from some existing algorithms shows satisfactory and acceptable performances of the modified 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".