Bibliographic record
Abstract
In this paper a novel technique for face recognition is proposed. Using the statistical Local Feature Analysis (LFA) method, a set of feature points is extracted for each face image at locations with highest deviations from the expectation. Each feature point is described by a sequence of local histograms captured from the Gabor responses at different frequencies and orientations around the feature point. Histogram intersection is used to compare the Gabor histogram sequences in order to find the matched feature points between two faces. Recognition is performed based on the average similarity between the best matched points, in the probe face and each of the gallery faces. Several experiments on the FERET set of faces show the superiority of the proposed technique over all considered state-of-the-art methods (Elastic Bunch Graph Matching, LDA+PCA, Bayesian Intra/extrapersonal Classifier, Boosted Haar Classifier), and validate the robustness of our method against facial expression variation and illumination variation.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".