Bibliographic record
Abstract
To solve the small sample problem of biometric identification, this paper investigates the limiting case of the problem, i.e., the recognition of a single training sample, and proposes a single sample discriminant analysis method based on Gabor wavelet and KPCA-RBF (KPRC) classifier (kernel principal component analysis-radial basis function). The proposed method performs pixel-level fusion of face and palmprint images. Firstly, a face image and a palmprint image were subject to two-dimensional (2D) Gabor wavelet transform. The resulting Gabor face image and Gabor palmprint image were fused on the pixel level into a new fused image. Next, a new classifier called KPCA-RBF was designed to extract nonlinear discriminative features by KPCA, and classify objects with RBF. Based on AR database, FERET database, and palmprint database, the single sample discriminant analysis method was realized based on Gabor transform and KPCA-RBF classifier. Experimental results show that multimodal recognition methods clearly outshine single-modal recognition methods, and the GABOR-KPRC with pixel-level fusion achieves better recognition effect than other fusion methods. It was also demonstrated that Gabor transform and KPRC classifier can effectively improve the fusion effect, whether for pixel-level fusion or decision-level fusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".