High-order local normal derivative pattern (LNDP) for 3D face recognition
Bibliographic record
Abstract
This paper proposes a novel descriptor based on the local derivative pattern (LDP) for 3D face recognition. Compared to the local binary pattern (LBP), LDP can capture more detailed information by encoding directional pattern features. It is based on the local derivative variations that extract high-order local information. We propose a novel discriminative facial shape descriptor, local normal derivative pattern (LNDP) that extracts LDP from the surface normal. Using surface normal, the orientation of a surface at each point is determined as a first-order surface differential. Three normal component images are extracted by estimating three components of normal vectors in x, y, and z channels. Each normal component is divided into several patches and encoded using LDP. The final descriptor is created by concatenating histograms of the LNDP on each patch. Experimental results on two famous 3D face databases, FRGC v2.0 and Bosphorus illustrate the effectiveness of the proposed descriptor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".