FACIAL METAMORPHOSIS USING GEOMETRICAL METHODS FOR BIOMETRIC APPLICATIONS
Bibliographic record
Abstract
Facial expression modeling has been a popular topic in biometrics for many years. One of the emerging recent trends is capturing subtle details such as wrinkles, creases and minor imperfections that are highly important for biometric modeling as well as matching. In this paper, we suggest a novel approach to the problem of expression modeling and morphing based on a geometry-based paradigm. In 2D image space, a distance-based morphing system is utilized to create a line drawing style facial animation from two input images representing frontal and profile views of the face. Aging wrinkles and expression lines are extracted and mapped back to the synthesized facial NPR (nonphotorealistic) sketches. In 3D object space, we present a metamorphosis system that combines the traditional free-form deformation (FFD) model with data interpolation techniques based on the proximity preserving Voronoi diagram. With feature points selected from two images of the target face, the proposed system generates the 3D target facial model by transforming a generic model. Experimental results demonstrate that morphing sequences generated by our systems are of convincing quality.
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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 | 0.001 |
| 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) | 0.004 | 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".