The Relationship between Personality Traits and Face Shapes in Chinese Traditional Physiognomy
Bibliographic record
Abstract
Objective: Physiognomy has over 3000 years of history in China, where the belief that personality can be discerned through physiognomy is widespread. However, it hasn’t been fully verified by scientific research. Through experiments, this paper explores the relationship between face shape and corresponding personality in physiognomy, and how face shape affects people’s judgment of personality. Method: According to the eight face shapes theory of physiognomy, 10 trained laboratory assistants have selected 64 typical faces through 3816 pieces of ID photos following a designated procedure, and tested the selected 64 persons’ scores of Cattell’s 16 Personality Factors Test. Eight more ID photos have been randomly selected, and each one has been modified by Image Processing Technology into eight face shapes, keeping other facial features same to ensure that the only variable is face shape, and ultimately obtained 64 artificial faces. 949 undergraduates, as participants, have visually judged these 128 faces in a laboratory by using E-prime 2.0 and 16PF Rating Scale. Results: Overall, there was no significant difference of tested sixteen personality traits among eight typical faces. Through a post-hoc test, some face shapes are perceived to have certain significant differences in some personality traits than a certain face shape. For example, on factor Q2 of 16PF, a heart-shaped face (M = 2.625*) is significantly lower than a diamond-shaped face (M = 4.375). In contrast, there are various differences among the eight face shapes on people’s visual judgmental of personality traits. For example, the heart-shaped face (M = 4.01**) is significantly lower than all other face shapes on factor A). By comprising the tested personality traits and perceived personality traits of each face shape, there are significant differences among some personality traits (e.g. diamond face on factor B, t = ?2.847**). Conclusions: Traditional physiognomy theory which explains personality by face shapes can’t be supported by the results. People are affected by the inherent stereotype (such as people with square face look like more right-minded), and tend to make a judgment about people’s personalities according to stereotypes of face shapes. Although their judgments are inconformity with the real personality traits, it indeed influences many people’s judgments on personality. According to this research, if people can tailor their face shape to someone’s preferences by using makeup, it will be easier for them to make a good impression with that person.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".