Discriminative SIFT features for face recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it