Linking the structure and perception of 3-D faces: Gender, ethnicity and expressive posture
Bibliographic record
Abstract
A statistical study of human face shape is reported whose overall goal was to identify and characterise salient components of facial structure for human perception and communicative behaviour. A large database of 3-D faces has been constructed and analysed for differences in ethnicity, sex, and posture. For each of more than 300 faces varying in race/ethnicity (Japanese versus Caucasian) and sex, nine postures (smiling, producing vowels, etc) were recorded. Principal components analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the data and to provide simple, yet reliable reconstruction of any face from components corresponding to the sex, ethnicity, and posture of the face. Thus, it appears that any face can be reconstructed from a small set of linear and intuitively salient components. Psychophysical tests confirmed that the shape is sufficient to estimate sex and ethnicity. Subjects were asked to judge the sex and ethnicity of (a) natural faces and (b) faces synthesised by randomly combining principal component coefficients within the database. Subjects successfully discriminated ethnicity and sex independently of posture, verifying that different combinations of components are required and in differing amounts. Finally, implications of these results for animation and face recognition are discussed, incorporating results of studies currently underway that examine the 'face print' residue of the sex - ethnicity factor analysis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".