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

TL;DR

This survey reviews recent vision-based methods for analyzing apparent personality traits from visual data, highlighting key approaches, challenges, datasets, and future research directions in the field.

Contribution

It provides a comprehensive overview of current vision-based personality analysis techniques, comparing their features and limitations, and discusses future research opportunities.

Findings

Recent methods accurately analyze facial and body cues for personality traits.

Subjectivity in data labeling remains a significant challenge.

Several datasets and challenges are available to advance research in this area.

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.