Statistical Non-Uniform Sampling of Gabor Wavelet Coefficients for Face Recongnition
Bibliographic record
Abstract
A statistics based, non-uniform sampling of the Gabor wavelet decomposition coefficients for face recognition is presented in this paper. Gabor wavelets are popularly used to decompose face images into their spatial/frequency domains. The derived Gabor coefficients generate an augmented vector, e.g., 40 times larger than the original gray-scale vector. To reduce the dimensionality, uniform sampling of the Gabor coefficients is normally used. In this paper, we propose a non-uniform sampling method of the Gabor coefficients such that the coefficients corresponding to important face features are sampled much finer than those of the other parts of the image. The non-uniform sampling is based on the local statistics of the Gabor coefficients obtained from a set of training images. This adaptation is implemented in a hierarchical fashion; a coarse-to-fine strategy results in multi-level sampling rates. After the samples are obtained, the traditional principal component analysis (PCA) is used to code the samples for the final classification. The experimental results show that the proposed non-uniform sampling of Gabor coefficients outperforms the uniform one and the popular eigenfaces method.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".