The perception of lip aesthetics in the context of facial proportions—An eye‐tracking‐based analysis
Bibliographic record
Abstract
BACKGROUND: Minimally invasive lip volumizing and contouring soft tissue filler procedures are frequently requested by both female and male aesthetic patients. Guidance on how to achieve the most beautiful outcome is inconsistent. OBJECTIVE: To investigate what the most beautiful proportions are in relation to vermillion thickness, the distance of the upper and lower lip in relation to nose and chin, and relation to the bigonial distance. METHODS: This study included a total of n = 101 volunteers (52 females, 49 males, 100% Caucasian) who inspected frontal images of modified facial proportions and answered a related questionnaire showcasing the same images. Image presentation followed a randomized sequence both for the eye tracking and for the survey component of this study but was equal for all observers. RESULTS: The most attractive vertical position of lips was the 1:2 ratio in which the distance between lips and chin is double the length as the distance between lips and nose. For the ratio between the width of the lips and the bigonial distance, it was the 1:2.5 ratio, whereas for the thickness of the upper lip vermilion in relation to the upper lip ergotrid it was the 1:3/1:2 (male/female) ratio and for the lower lip vermilion and lower lip ergotrid it was the 1:4 ratio for both genders. CONCLUSION: The results of this eye tracking and survey-based investigation revealed that instead of one single universal ratio, multiple facial proportions exist that are perceived as most attractive/masculine/feminine. Regarding the perception of facial aesthetics, it appears there is a distinction between attractiveness and masculinity/femininity: the most attractive male/female face is not necessarily the most masculine or feminine.
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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 | 0.001 | 0.002 |
| 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".