Curvature-based face surface recognition using spherical correlation. Principal directions for curved object recognition
Why this work is in the frame
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Bibliographic record
Abstract
Surface curvatures such as Gaussian, mean and principal curvatures are intrinsic surface properties and have played important roles in curved surface analysis. In this paper, we present a correlation-based face recognition approach based on the analysis of maximum and minimum principal curvatures and their directions. We treat face recognition problem as a 3D shape recognition problem of free-form curved surfaces. Our approach is based on a 3D vector sets correlation method which does not require either face feature extraction or surface segmentation. Each face in both input images and the model database, is represented as an Extended Gaussian Image (EGI), constructed by mapping principal curvatures and their directions at each surface points, onto two unit spheres, each of which represents ridge and valley lines respectively. Individual face is then recognized by evaluating the similarities among others by using Fisher's spherical correlation on EGI's effaces. The method is tested for its simplicity and robustness and successively implemented for each of face range images from NRCC (National Research Council Canada) 3D image data files. Results show that shape information from surface curvatures provides vital cues in distinguishing and identifying such fine surface structure as human faces.
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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 | 0.000 |
| 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 it