Multimodal 2D–3D face recognition using local descriptors: pyramidal shape map and structural context
Bibliographic record
Abstract
In this study, the authors propose a local descriptor based multimodal approach to improve face recognition performance. Pre‐processing is done to smooth, resample, and register data. The resampled three‐dimensional (3D) face data are applied to extract novel descriptors including pyramidal shape index, pyramidal curvedness, pyramidal mean, and Gaussian curvatures. Proposed pyramidal shape maps are extracted at each level of the Gaussian pyramid on each point of the 3D data to have 2D matrices as representatives of 3D geometry information. A local descriptor structural context histogram, which represents the structure of the image using scale invariant feature transform, is calculated on pyramidal shape map descriptors and texture image to find matched keypoints in 3D and 2D modality, respectively. Score‐level fusion by means of sum rule is employed to get a final matching score. Experimental results on the Face Recognition Grand Challenge (FRGC v2) database illustrate verification rates of 99 and 98.65% at 0.1% false acceptance rate for all versus all and ROC III experiments, respectively. On Bosphorus database, verification rate of 95.8% for neutral versus all experiment has been achieved.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".