Published on Sat Apr 21 2018

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

Julio C. S. Jacques Junior, Yağmur Güçlütürk, Marc Pérez, Umut Güçlü, Carlos Andujar, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon, Marcel A. J. van Gerven, Rob van Lier, Sergio Escalera

Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields. From the past few years, it has also become an attractive research area in visual computing. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors.

0
0
0
Abstract

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.

Thu Aug 03 2017
Computer Vision
What your Facebook Profile Picture Reveals about your Personality
Social psychologists have shown that people manage these impressions differently depending upon their personality. Extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits. Neuroticism ones have a prevalence of pictures of indoor places.
0
0
0
Thu Sep 12 2019
Computer Vision
On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals
Personality perception is implicitly biased due to many subjective factors, such as cultural, social, contextual, gender and appearance. Approaches for automatic personality perception are not expected to predict the real personality of the target, but the personality external observers attribute to it.
0
0
0
Tue Apr 26 2016
Computer Vision
Modern Physiognomy: An Investigation on Predicting Personality Traits and Intelligence from the Human Face
Human behavior of evaluating other individuals with respect to their personality traits and intelligence by evaluating their faces plays a crucial role in human relations. These trait judgments might influence important social outcomes in our lives such as elections and court sentences. Previous studies have reported that human can make valid inferences for at least four personality traits.
0
0
0
Wed Aug 07 2019
Artificial Intelligence
Recent Trends in Deep Learning Based Personality Detection
Personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection.
0
0
0
Wed Jan 28 2015
Computer Vision
The Beauty of Capturing Faces: Rating the Quality of Digital Portraits
The beauty of a digital portrait is linked to its artistic value, and independent from age, race and gender of the subject. A classifier trained to separate beautiful portraits from non-beautiful portraits outperforms generic aesthetic classifiers.
0
0
0
Sun May 29 2016
Computer Vision
Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information
We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color, as well as subjective traits. We propose a novel approach that incorporates facial landmark information for input images as an additional channel.
0
0
0