Contourlet based distance measurement to improve fingerprint identification accuracy
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, Kullback-Leibler Distance (KLD) is employed to measure the dissimilarity between marginal statistical features of contourlet transform to fingerprint identification. Conourlet transform is a non separable two dimensional transform which can well capture the geometry of edges in the images which convey important information for the human visual system (HVS). Here, marginal statistics of each transform subband are modeled by a Generalized Gaussian Density (GGD) model and the GGD parameters-α and β- are granted as the extracted features from the corresponding subbands and the fingerprint recognition is done based on k-NN classifier employing Kullback-Leibler Distance (KLD) measure. The fingerprint recognition results confirm the high efficiency of the proposed system comparing with the state of the art methods.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 it