Improved Face Representation by Nonuniform Multilevel Selection of Gabor Convolution Features
Bibliographic record
Abstract
Gabor wavelets are widely employed in face representation to decompose face images into their spatial-frequency domains. The Gabor wavelet transform, however, introduces very high dimensional data. To reduce this dimensionality, uniform sampling of Gabor features has traditionally been used. Since uniform sampling equally treats all the features, it can lead to a loss of important features while retaining trivial ones. In this paper, we propose a new face representation method that employs nonuniform multilevel selection of Gabor features. The proposed method is based on the local statistics of the Gabor features and is implemented using a coarse-to-fine hierarchical strategy. Gabor features that correspond to important face regions are automatically selected and sampled finer than other features. The nonuniformly extracted Gabor features are then classified using principal component analysis and/or linear discriminant analysis for the purpose of face recognition. To verify the effectiveness of the proposed method, experiments have been conducted on benchmark face image databases where the images vary in illumination, expression, pose, and scale. Compared with the methods that use the original gray-scale image with 4096-dimensional data and uniform sampling with 2560-dimensional data, the proposed method results in a significantly higher recognition rate, with a substantial lower dimension of around 700. The experimental results also show that the proposed method works well not only when multiple sample images are available for training but also when only one sample image is available for each person. The proposed face representation method has the advantages of low complexity, low dimensionality, and high discriminance.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".