Beyond personality traits: Which facial expressions imply dominance in two-person interaction scenes?

Emotion ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 872-885 ◽  
Author(s):  
Yoshiyuki Ueda ◽  
Sakiko Yoshikawa
2022 ◽  
Vol 29 (2) ◽  
pp. 1-59
Author(s):  
Joni Salminen ◽  
Sercan Şengün ◽  
João M. Santos ◽  
Soon-Gyo Jung ◽  
Bernard Jansen

There has been little research into whether a persona's picture should portray a happy or unhappy individual. We report a user experiment with 235 participants, testing the effects of happy and unhappy image styles on user perceptions, engagement, and personality traits attributed to personas using a mixed-methods analysis. Results indicate that the participant's perceptions of the persona's realism and pain point severity increase with the use of unhappy pictures. In contrast, personas with happy pictures are perceived as more extroverted, agreeable, open, conscientious, and emotionally stable. The participants’ proposed design ideas for the personas scored more lexical empathy scores for happy personas. There were also significant perception changes along with the gender and ethnic lines regarding both empathy and perceptions of pain points. Implications are the facial expression in the persona profile can affect the perceptions of those employing the personas. Therefore, persona designers should align facial expressions with the task for which the personas will be employed. Generally, unhappy images emphasize realism and pain point severity, and happy images invoke positive perceptions.


2018 ◽  
Author(s):  
Damien Dupré ◽  
Nicole Andelic ◽  
Anna Zajac ◽  
Gawain Morrison ◽  
Gary John McKeown

Sharing personal information is an important way of communicating on social media. Among the information possibly shared, new sensors and tools allow people to share emotion information via facial emotion recognition. This paper questions whether people are prepared to share personal information such as their own emotion on social media. In the current study we examined how factors such as felt emotion, motivation for sharing on social media as well as personality affected participants’ willingness to share self-reported emotion or facial expression online. By carrying out a GLMM analysis, this study found that participants’ willingness to share self-reported emotion and facial expressions was influenced by their personality traits and the motivation for sharing their emotion information that they were given. From our results we can conclude that the estimated level of privacy for certain emotional information, such as facial expression, is influenced by the motivation for sharing the information online.


Author(s):  
Riya KalburgI ◽  
Punit Solanki ◽  
Rounak Suthar ◽  
Saurabh Suman

Expression is the most basic personality trait of an individual. Expressions, ubiquitous to humans from all cultures, can be pivotal in analyzing the personality which is not confined to boundaries. Analyzing the changes in the expression of the individual can bolster the process of deriving his/her personality traits underscoring the paramount reactions like anger, happiness, sadness and so on. This paper aims to exercise Neural Network algorithms to predict the personality traits of an individual from his/her facial expressions. In this paper, a methodology to analyze the personality traits of the individual by periodic monitoring of the changes in facial expressions is presented. The proposed system is intended to analyze the expressions by exploiting Neural Networks strategies to first analyze the facial expressions of the individual by constantly monitoring an individual under observation. This monitoring is done with the help of OpenCV which captures the facial expression at an interval of 15 secs. Thousands of images per expression are used to train the model to aptly distinguish between expression using prominent Neural Network Methodologies of Forward and Backward Propagation. The identified expression is then be fed to a derivative system which plots a graph highlighting the changes in the expression. The graph acts as the crux of the proposed system. The project is important from the perspective of serving as an alternative to manual monitoring which are prone to errors and subjective in nature.


2021 ◽  
Vol 12 ◽  
Author(s):  
Angélique Lebert ◽  
Laurence Chaby ◽  
Amandine Guillin ◽  
Samuel Chekroun ◽  
Dorine Vergilino-Perez

In everyday life, interactions between humans are generally modulated by the value attributed to the situation, which partly relies on the partner's behavior. A pleasant or cooperating partner may trigger an approach behavior in the observer, while an unpleasant or threatening partner may trigger an avoidance behavior. In this context, the correct interpretation of other's intentions is crucial to achieve satisfying social interactions. Social cues such as gaze direction and facial expression are both fundamental and interrelated. Typically, whenever gaze direction and facial expression of others communicate the same intention, it enhances both the interlocutor's gaze direction and the perception of facial expressions (i.e., shared signal hypothesis). For instance, an angry face with a direct gaze is perceived as more intense since it represents a threat to the observer. In this study, we propose to examine how the combination of others' gaze direction (direct or deviated) and emotional facial expressions (i.e., happiness, fear, anger, sadness, disgust, and neutrality) influence the observer's gaze perception and postural control. Gaze perception was indexed by the cone of direct gaze (CoDG) referring to the width over which an observer feels someone's gaze is directed at them. A wider CoDG indicates that the observer perceived the face as looking at them over a wider range of gaze directions. Conversely, a narrower CoDG indicates a decrease in the range of gaze directions perceived as direct. Postural control was examined through the center of pressure displacements reflecting postural stability and approach-avoidance tendencies. We also investigated how both gaze perception and postural control may vary according to participants' personality traits and emotional states (e.g., openness, anxiety, etc.). Our results confirmed that gaze perception is influenced by emotional faces: a wider CoDGs was observed with angry and disgusted faces while a narrower CoDG was observed for fearful faces. Furthermore, facial expressions combined with gaze direction influence participants' postural stability but not approach-avoidance behaviors. Results are discussed in the light of the approach-avoidance model, by considering how some personality traits modulate the relation between emotion and posture.


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