@Article{JulioC.S.JacquesJunior2022,
author="Julio C. S. Jacques Junior
and Yagmur Gucluturk
and Marc Perez
and Umut Gu{\c{c}}lu
and Carlos Andujar
and Xavier Baro
and Hugo Jair Escalante
and Isabelle Guyon
and Marcel A. J. van Gerven
and Rob van Lier
and Sergio Escalera",
title="First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis",
journal="IEEE Transactions on Affective Computing",
year="2022",
volume="13",
number="1",
pages="75--95",
optkeywords="Personality computing",
optkeywords="first impressions",
optkeywords="person perception",
optkeywords="big-five",
optkeywords="subjective bias",
optkeywords="computer vision",
optkeywords="machine learning",
optkeywords="nonverbal signals",
optkeywords="facial expression",
optkeywords="gesture",
optkeywords="speech analysis",
optkeywords="multi-modal recognition",
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.",
optnote="HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3724), last updated on Tue, 25 Apr 2023 15:26:51 +0200",
doi="10.1109/TAFFC.2019.2930058"
}