Enhanced Palmprint Recognition via Curvi-Linear Anisotropic Gaussian Filter-Based Combined Differential Concavity and Infirmity Codes
Why this work is in the frame
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Bibliographic record
Abstract
The inherent curvature in palm lines can pose challenges for palmprint recognition, particularly at lower resolutions where wrinkles become indistinguishable, leading to performance degradation.To address these issues, this study introduces a novel methodology employing curvi-linear anisotropic Gaussian filter-based Combined Differential Concavity and Infirmity (CDCI) codes.The use of curved filters has been proposed to represent curved palm lines more accurately, while anisotropic filtering is expected to enhance the extraction of blurred palm lines.The new representation, grounded in curvi-linear anisotropic Gaussian filtering, is posited to improve the recognition system's performance by effectively addressing these challenges.The proposed approach's effectiveness has been tested using the touchless IITD database and the contact-based PolyU 2D database.The experimental results suggest that the proposed methodology surpasses the performance of state-of-the-art coding-based procedures in palmprint recognition with the improvement of 3.82% and 36.36% in recognition rate and equal error rate.
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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 | 0.001 |
| 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