Facial actions as visual cues for personality
Bibliographic record
Abstract
Abstract What visual cues do human viewers use to assign personality characteristics to animated characters? While most facial animation systems associate facial actions to limited emotional states or speech content, the present paper explores the above question by relating the perception of personality to a wide variety of facial actions (e.g., head tilting/turning, and eyebrow raising) and emotional expressions (e.g., smiles and frowns). Animated characters exhibiting these actions and expressions were presented to human viewers in brief videos. Human viewers rated the personalities of these characters using a well‐standardized adjective rating system borrowed from the psychological literature. These personality descriptors are organized in a multidimensional space that is based on the orthogonal dimensions of desire for affiliation and displays of social dominance. The main result of the personality rating data was that human viewers associated individual facial actions and emotional expressions with specific personality characteristics very reliably. In particular, dynamic facial actions such as head tilting and gaze aversion tended to spread ratings along the dominance dimension, whereas facial expressions of contempt and smiling tended to spread ratings along the affiliation dimension. Furthermore, increasing the frequency and intensity of the head actions increased the perceived social dominance of the characters. We interpret these results as pointing to a reliable link between animated facial actions/expressions and the personality attributions they evoke in human viewers. The paper shows how these findings are used in our facial animation system to create perceptually valid personality profiles based on dominance and affiliation as two parameters that control the facial actions of autonomous animated characters. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".