  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.
