TY - JOUR
T1 - First Impressions
T2 - A Survey on Vision-Based Apparent Personality Trait Analysis
AU - Jacques Junior, Julio C.S.
AU - Gucluturk, Yagmur
AU - Perez, Marc
AU - Guclu, Umut
AU - Andujar, Carlos
AU - Baro, Xavier
AU - Escalante, Hugo Jair
AU - Guyon, Isabelle
AU - Van Gerven, Marcel A.J.
AU - Van Lier, Rob
AU - Escalera, Sergio
N1 - Funding Information:
This project has been partially supported by granted Spanish Ministry projects TIN2016-74946-P, TIN2015-66951-C2-2-R and TIN2017-88515-C2-1-R. This work is partially supported by ICREA under the ICREA Academia programme. We thank ChaLearn Looking at People sponsors for their support, including Microsoft Research, Google, NVIDIA Corporation, Amazon, Facebook and Disney Research.
Publisher Copyright:
© 2010-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - big-five
KW - computer vision
KW - facial expression
KW - firs impressions
KW - gesture
KW - machine learning
KW - multi-modal recognition
KW - nonverbal signals
KW - person perception
KW - Personality computing
KW - speech analysis
KW - subjective bias
UR - http://www.scopus.com/inward/record.url?scp=85126127916&partnerID=8YFLogxK
U2 - 10.1109/TAFFC.2019.2930058
DO - 10.1109/TAFFC.2019.2930058
M3 - Journal article
AN - SCOPUS:85126127916
SN - 2371-9850
VL - 13
SP - 75
EP - 95
JO - IEEE Transactions on Affective Computing
JF - IEEE Transactions on Affective Computing
IS - 1
ER -