Palmprint Classification with Multiple Filter Faces, Fourier Features and Voting Technique
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a novel method for palmprint classification. We extract the central region from the palmprint image, calculate eight filter faces (FF) from the region based on eight pairs of filters, compute the Fourier features from each FF, classify each of them to one known class, and then perform majority voting to determine the final class label of the unknown palmprint image. By examining the structures of the selected filters, we can see that our new method can supress random noise and at the same time it can extract directional features from the palmprint images. This is the main reason why FF-based methods are better than non-FF-based methods for palmprint classification. In addition, the majority winning policy (voting) based on eight FFs improves classification accuracies significantly. Experimental results demonstrate that our new method outperforms several existing methods for palmprint classification.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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