Robust Fingerprint Enhancement by Directional Filtering in Fourier Domain
Why this work is in the frame
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Bibliographic record
Abstract
Reliabe extraction of true minutiae in fingerprint image is critical to the performance of an automated fingerprint identification system (AFIS). In order to utilize AFIZ in law enforcement agencies first step is to digitalize an archyve of fingerprints obtained by ink method. For improving the quality of automatically extracted minutiae (both in number and type) enhancement is previously performed, but, nevertheless, quite a number of spuirious minutiae (especially in blurry or regions containing scars and creases) are extracted. It is crucial for AFIS performance that number of extracted (and consequently saved in fingerprint template) spurious minutia is minimized to maximal extent. In order to do so, we propose directional Log-Gabor filtering in frequency domain. Results proved to be preferable for a wide range of input digitized fingerprint images.
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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 | 0.001 | 0.002 |
| 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