First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Interpersonal Relationships Moderate the Effect of Faces on Person Judgments

Previous research suggests that people form impressions of others based on their facial appearance in a very fast and automatic manner, and this especially holds for trustworthiness. However, as yet, this process has been investigated mostly in a social vacuum without taking interpersonal factors into account. In the current research, we demonstrate that both the relationship context that is salient at the moment of an interaction and the performed behavior, are important moderators of the impact of facial cues on impression formation. It is shown that, when the behavior of a person we encounter is ambiguous in terms of trustworthiness, the relationship most salient at that moment is of crucial impact on whether and how we incorporate facial cues communicating (un)trustworthiness in our final evaluations. Ironically, this can result in less positive evaluations of interaction partners with a trustworthy face compared to interaction partners with an untrustworthy face. Implications for research on facial characteristics, trust, and relationship theories are discussed.trust;facial characteristics;person perception;relationship norms;word-of-mouth

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions

Four dimensions characterize comprehensive trait judgments of faces

People readily attribute many traits to faces: some look beautiful, some competent, some aggressive. These snap judgments have important consequences in real life, ranging from success in political elections to decisions in courtroom sentencing. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance, a highly influential framework that has been the basis for numerous studies in social and developmental psychology, social neuroscience, and in engineering applications. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments, and reconcile prior work on face perception with work in social cognition and personality psychology

Affective coherence: Affect as embodied evidence in attitude, advertising and art

For the first time, this volume brings together these varied developments under one umbrella and furnishes a comprehensive overview of this intellectual ..

A Tiger and a President: Imperceptible Celebrity Facial Cues Influence Trust and Preference.

This is the publisher's version. Copyright 2012 by Journal of Consumer Research.Neuroscientific research suggests that the brain has evolved specific capabilities enabling automatic social judgments of others to be made based on facial properties alone. However, little research in marketing has considered the consequences of how facial imagery is automatically processed. We explore automatic perceptions of familiarity by using morphing software to digitally combine unfamiliar faces with those of Tiger Woods and George Bush. Despite a complete lack of conscious recognition, trustworthiness ratings of the composite faces are clearly influenced by the celebrities in question. This appears to be due to implicit recognition being sufficient for individuals to automatically access their own summary valence judgments of either Woods or Bush. Alternative explanations based on a perceptual-fluency account, or implicit recognition sufficient to perceive specific trait ratings, are ruled out. These findings suggest that the marketing practice of digitally manipulating the attractiveness of facial imagery risks overlooking the important influence of familiarity. [ABSTRACT FROM AUTHOR

How Do Induced Affective States Bias Emotional Contagion to Faces? A Three-Dimensional Model

Affective states can propagate in a group of people and influence their ability to judge others’ affective states. In the present paper, we present a simple mathematical model to describe this process in a three-dimensional affective space. We obtained data from 67 participants randomly assigned to two experimental groups. Participants watched either an upsetting or uplifting video previously calibrated for this goal. Immediately, participants reported their baseline subjective affect in three dimensions: (1) positivity, (2) negativity, and (3) arousal. In a second phase, participants rated the affect they subjectively judged from 10 target angry faces and ten target happy faces in the same three-dimensional scales. These judgments were used as an index of participant’s affective state after observing the faces. Participants’ affective responses were subsequently mapped onto a simple three-dimensional model of emotional contagion, in which the shortest distance between the baseline self-reported affect and the target judgment was calculated. The results display a double dissociation: negatively induced participants show more emotional contagion to angry than happy faces, while positively induced participants show more emotional contagion to happy than angry faces. In sum, emotional contagion exerted by the videos selectively affected judgments of the affective state of others’ faces. We discuss the directionality of emotional contagion to faces, considering whether negative emotions are more easily propagated than positive ones. Additionally, we comment on the lack of significant correlations between our model and standardized tests of empathy and emotional contagion.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Infants\u27 & Toddlers\u27 Social Evaluations of Trustworthy and Untrustworthy Faces

Our understanding of the social world is highly influenced by the fast and automatic evaluations we make about others based on their facial appearance. The goal of the current studies is to explore the developmental origins of the particular face-trait evaluation of ‘trustworthiness.’ Experiment 1 tested whether 10-month-old infants differentiate between faces that adults rate as trustworthy and untrustworthy, and if they have a preference for one over the other in a crawling task. Experiment 2 tested whether 10-month-olds have implicit expectations about the social behavior of characters with trustworthy of untrustworthy faces in a looking-time task that presents infants with congruent or incongruent trait-action pairings. Finally, Experiment 3 explored the development of more explicit face-trait judgments by testing 2-year-old’ performance in a pointing task that required them to match a helpful or unhelpful action with characters that possess ‘trustworthy’ or ‘untrustworthy’ faces. Taken together, these studies provide preliminary evidence for a rudimentary form of face-based social evaluation in infancy

Differences in Appearance-Based Trait Inferences for Male and Female Political Candidates

Studies show that automatic trait inferences can predict outcomes of actual elections, but these studies generally include male candidates only. Substantial evidence also shows that female candidates are subject to gender-based stereotypes, which can lead to differences in how men and women candidates are evaluated. This article combines these two literatures to compare the effects of competence, threat, and attractiveness inferences in elections that include women. We use experimental data in which candidate pairs from state and local US elections were judged on these three traits and examine whether those ratings are predictive of election outcomes. We find that although competence matters most for elections involving only men, attractiveness predicts winners in women-only elections. In mixed-gender races, competence inferences predict success when the female candidate is perceived as more competent than the male candidate. Finally, unlike men, women benefit from being perceived as physically threatening in mixed-gender races