A 3-D Anthropometric-Muscle-Based Active Appearance Model
Bibliographic record
Abstract
This paper describes a novel method for modeling the shape and appearance of human faces in three dimensions using a constrained three-dimensional (3-D) active appearance model (AAM). Our algorithm is an extension of the classical two-dimensional (2-D) AAM. The method uses a generic 3-D wireframe model of the face, based on two sets of controls: anatomically motivated muscle actuators to model facial expressions and statistically based anthropometrical controls to model different facial-types. The 3-D anthropometric-muscle-based model (AMBM) of the face allows representing a facial image in terms of a controlled model-parameter set, hence, providing a natural and constrained basis for face segmentation and analysis. The generated face models are consequently simpler and less memory intensive compared to the classical appearance-based models. The proposed method allows for accurate fitting results by constraining solutions to be valid instances of a face model. Extensive image-segmentation experiments have demonstrated the accuracy of the proposed algorithm against the classical AAM.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".