Bibliographic record
Abstract
SIFT (Scale Invariant Feature Transform) features are widely used in object recognition. These features are invariant to changes in scale, 2D translation and rotation transformations. To a limited extent they are also robust to 3D projection transformations. SIFT Features however, are of very high dimension and large number of SIFT features are generated from an image. The large computational effort associated with matching all the SIFT features for recognition tasks, limits its application to face recognition problems. In this work we propose a discriminative ranking of SIFT features that can be used to prune the number of SIFT features for face recognition. Our method checks the number of irrelevant features to be matched thereby reducing the computational complexity. In the process it also increases the recognition accuracy. We show that the reduction in the number of computations is more than 4 times and increase in the recognition accuracy is 1% on average. Experimental results confirm that our proposed recognition method is robust to changes in head pose, illumination, facial expression and partial occlusion.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".