Supplemental Material for Beyond Personality Traits: Which Facial Expressions Imply Dominance in Two-Person Interaction Scenes?

Emotion ◽  
2017 ◽  
Keyword(s):  
Personality Traits ◽  
Facial Expressions

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
Keyword(s):  
Personality Traits ◽  
Facial Expressions

Can Unhappy Pictures Enhance the Effect of Personas? A User Experiment

2022 ◽  
Vol 29 (2) ◽  
pp. 1-59
Author(s):  
Joni Salminen ◽  
Sercan Şengün ◽  
João M. Santos ◽  
Soon-Gyo Jung ◽  
Bernard Jansen
Keyword(s):  
Mixed Methods ◽  
Facial Expression ◽  
Personality Traits ◽  
Facial Expressions ◽  
User Perceptions ◽  
Design Ideas ◽  
User Experiment ◽  
Pain Points

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.

The effect of personality and social context on willingness to share emotion information on social media

2018 ◽  
Author(s):  
Damien Dupré ◽  
Nicole Andelic ◽  
Anna Zajac ◽  
Gawain Morrison ◽  
Gary John McKeown
Keyword(s):  
Social Media ◽  
Social Context ◽  
Facial Expression ◽  
Personality Traits ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Personal Information ◽  
Facial Emotion ◽  
Emotional Information ◽  
New Sensors

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.

scholarly journals Expression Analysis System

2021 ◽  
Vol 10 (3) ◽  
pp. 13-15
Author(s):  
Riya KalburgI ◽  
Punit Solanki ◽  
Rounak Suthar ◽  
Saurabh Suman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Personality Traits ◽  
Facial Expressions ◽  
Network Algorithms ◽  
Periodic Monitoring ◽  
Derivative System ◽  
Analysis System ◽  
The Individual ◽  
Backward Propagation

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.

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