Does Face Recognition Rely on Encoding of 3-D Surface? Examining the Role of Shape-from-Shading and Shape-from-Stereo
Bibliographic record
Abstract
It is now well known that processing of shading information in face recognition is susceptible to bottom lighting and contrast reversal, an effect that may be due to a disruption of 3-D shape processing. The question then is whether the disruption can be rectified by other sources of 3-D information, such as shape-from-stereo. We examined this issue by comparing identification performance either with or without stereo information using top-lit and bottom-lit face stimuli in both photographic positive and negative conditions. The results show that none of the shading effects was reduced by the presence of stereo information. This finding supports the notion that shape-from-shading overrides shape-from-stereo in face perception. Although shape-from-stereo did produce some signs of facilitation for face identification, this effect was negligible. Together, our results support the view that 3-D shape processing plays only a minor role in face recognition. Our data are best accounted for by a weighted function of 2-D processing of shading pattern and 3-D processing of shapes, with a much greater weight assigned to 2-D pattern processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".