All the Dark Triad and some of the Big Five traits are visible in the face

Alper, Sinan; Bayrak, Fatih; Yılmaz, Onurcan

doi:10.1016/j.paid.2020.110350

Cited by 23 publications

(28 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This works particularly well with the factors conscientiousness and extraversion (Little and Perrett, 2007). Another recent study (Alper et al, 2021) showed that agreeableness and conscientiousness are correctly inferred, while extraversion is correctly inferred in women. For our study, we chose prototypical faces for all Big Five factors (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness).…”

Section: The Present Studymentioning

confidence: 53%

See 1 more Smart Citation

In Your Face(t)—Personality Traits Interact With Prototypical Personality Faces in Economic Decision Making

2021

View full text Add to dashboard Cite

In everyday life, assumptions about our peers' as well as our own personality shape social interactions. We investigated whether self-rated personality and inferences drawn from partners' faces influence economic decisions. Participants (N = 285) played the trust game in the role of the trustor as well as the ultimatum game in the role of the proposer and interacted with trustees and receivers represented by prototypical personality faces. Participants also evaluated both their own traits and the personality of the faces. In the trust game, trustees represented by faces rated higher on agreeableness yielded higher transferred amounts. This effect was more pronounced for trustors low on dispositional trust, whereas trustors high on dispositional trust did not relate their decisions to the faces. Trustees represented by faces rated higher on conscientiousness yielded higher transferred amounts only for trustors high on dispositional anxiety. In the ultimatum game, receivers represented by faces rated higher on conscientiousness yielded lower offers only for proposers high on dispositional assertiveness. These results extend previous findings on the inferences drawn from facial features and the influence of personality on decision making. They highlight the importance of considering the personality of both interaction partner, as well as potential interactions of players' traits.

show abstract

Section: The Present Studymentioning

confidence: 53%

“…Trust in individuals with extreme levels of these traits can be particularly risky, as they are often selfish and manipulative. In studies with prototypical composite faces, it was shown that people can recognize the personality traits of the Dark Triad in people only by their faces (Holtzman, 2011;Alper et al, 2021).…”

Section: The Present Studymentioning

confidence: 99%

In Your Face(t)—Personality Traits Interact With Prototypical Personality Faces in Economic Decision Making

2021

View full text Add to dashboard Cite

show abstract

“…A growing number of studies have linked facial images to personality. It has been established that humans are able to perceive certain personality traits from each other's faces with some degree of accuracy [3] [4] [5][6] [7]. In addition to emotional expressions and other nonverbal behaviors that convey information about a person's psychological processes through the face, research has found that valid inferences about personality characteristics can even be made based on static images of faces with neutral expressions [8][9] [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Big Five Personality Traits Using Static Facial Images of College Students With Different Academic Backgrounds

Liu³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Appearance can affect social interaction, which in turn affects personality development.There is ample evidence that facial morphology and social cues provide information about human personality and behavior. In this study, we focused on the relationship between self-reported personality characteristics and facial features. We propose a new approach for predicting college students' personality characteristics (on the basis of the Big Five personality characteristics) with static facial images. First, we construct a dataset containing 13,347 data pairs composed of facial images and personality characteristics. Second, we train a deep neural network with 10,667 sample pairs from the dataset and use the remaining samples to test (1335 pairs) and validate (1335 pairs) self-reported Big Five personalities. We trained a series of deep neural networks on a large, labeled dataset to predict the self-reported Big Five personality trait scores. This novel work applies deep learning to this topic. We also verify the network's advanced nature on the publicly available database with obvious personality characteristics. The experimental results show that 1) personality traits can be reliably predicted from facial images with an accuracy that exceeds 70%. In five-character tag classification, the recognition accuracy of neuroticism and extroversion was the most accurate, and the prediction accuracy exceeded 90%. 2) Deep learning neural network features are better than traditional manual features in predicting personality characteristics. The results strongly support the application of neural networks trained on large-scale labeled datasets in multidimensional personality feature prediction from static facial images. 3) There are some differences in the personality traits of college students with different academic backgrounds. Future research can explore the relative contribution of other facial image features in predicting other personality characteristics.

show abstract

“…We can draw at least four valid inferences from other people's facial features [7]. Reference [8] examined the relationship between self-reported personality traits and first impressions. To investigate whether a computer can learn to assess human traits, the authors of [9] used a machine learning method to construct automatic feature predictors based on facial structure and appearance descriptors and found that all personality traits analyzed were accurate predictors.…”

Section: Introductionmentioning

confidence: 99%

2.5D Facial Personality Prediction Based on Deep Learning

Jia

Liu³

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

The assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college students. Although this method achieved a high accuracy, the reliance on positive images alone results in the loss of much personality-related information. Our new findings show that using real-life 2.5D static facial contour images, it is possible to make statistically significant predictions about a wider range of personality traits for both men and women. We address the objective of comprehensive understanding of a person’s personality traits by developing a multiperspective 2.5D hybrid personality-computing model to evaluate the potential correlation between static facial contour images and personality characteristics. Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.

show abstract

All the Dark Triad and some of the Big Five traits are visible in the face

Cited by 23 publications

References 88 publications

In Your Face(t)—Personality Traits Interact With Prototypical Personality Faces in Economic Decision Making

In Your Face(t)—Personality Traits Interact With Prototypical Personality Faces in Economic Decision Making

Prediction of the Big Five Personality Traits Using Static Facial Images of College Students With Different Academic Backgrounds

2.5D Facial Personality Prediction Based on Deep Learning

Contact Info

Product

Resources

About