Bibliographic record
Abstract
We represent face images by a set of triangular labeled graphs, each containing information on the appearance and geometry of a 3-tuple of face feature points. Our method automatically learns a model set and builds a graph space for each individual. A two-stage method for fast matching is developed, where in the first stage a maximum a posterior solution based on PCA factorization is used to efficiently prune the search space and select very few candidate model sets, and in the second stage a nearest neighborhood classifier is used to find the closest model graphs to the query image graphs. Finally, the probe image is assigned to the trained individual with the maximum number of references. Our proposed technique achieves perfect results on the ORL face set and an accuracy rate of 97.7% on the FERET face set, which shows the superiority of the proposed technique over all considered state-of-the-art methods (elastic bunch graph matching, LDA+PCA, Bayesian intra/extra-personal classifier, boosted Haar classifier)
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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 | 0.001 |
| 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".