An Appearance Model Constructed on 3-D Surface for Robust Face Recognition Against Pose and Illumination Variations
Bibliographic record
Abstract
We propose a face recognition method that is robust against image variations due to arbitrary lighting and a large extent of pose variations, ranging from frontal to profile views. Existing appearance models defined on image planes are not applicable for such pose variations that cause occlusions and changes of silhouette. In contrast, our method constructs an appearance model of a three-dimensional (3-D) object on its surface. Our proposed model consists of a 3-D shape and geodesic illumination bases (GIBs). GIBs can describe the irradiances of an object's surface under any illumination and generate illumination subspace that can describe illumination variations of an image in an arbitrary pose. Our appearance model is automatically aligned to the target image by pose optimization based on a rough pose, and the residual error of this model fitting is used as the recognition score. We tested the recognition performance of our method with an extensive database that includes 14 000 images of 200 individuals with drastic illumination changes and pose variations up to 60/spl deg/ sideward and 45/spl deg/ upward. The method achieved a first-choice success ratio of 94.2% without knowing precise poses a priori.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".