Enhancement of low-quality fingerprint images by a three-stage filtering scheme
Why this work is in the frame
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Bibliographic record
Abstract
Fingerprint image quality heavily influences the recognition rate for fingerprint identification/verification systems. A low-quality fingerprint image may consists of broken ridges, scars, smears, falsely conglutinated ridges, poor ridge and valley contrast, etc. In this paper, we propose a novel and effective three-stage scheme to enhance low-quality fingerprint images. The first-stage consists of an oriented linear anisotropic diffusion filter with a local ridge orientation estimation that differs from traditional estimation method. The second and the third stages consist of an oriented local ridge compensation filter and a novel angular filter, respectively. Simulation results show that the proposed method can improve the quality of the fingerprint images with a lower computational cost.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it