Paper Info
Title
Modeling, Recognizing, and Explaining Apparent Personality From Videos
Abstract
Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.
Year
DOI
Venue
2022
10.1109/TAFFC.2020.2973984
IEEE Transactions on Affective Computing
Keywords
DocType
Volume
Explainable computer vision,first impressions,personality analysis,multimodal information,algorithmic accountability
Journal
13
Issue
ISSN
Citations 
2
1949-3045
2
PageRank 
References 
Authors
0.36
26
17
Name
Order
Citations
PageRank
Hugo Jair Escalante193973.89
Heysem Kaya219519.84
Albert Ali Salah380970.11
Sergio Escalera41415113.31
Yağmur Güçlütürk5141.67
Umut Güçlü68810.86
Xavier Baró747433.99
Isabelle Guyon8110331544.34
Julio C. S. Jacques Junior9225.50
Meysam Madadi10879.28
Stéphane Ayache1120717.19
Evelyne Viegas12243.85
Furkan Gürpınar1320.36
Achmadnoer Sukma Wicaksana1461.09
Cynthia C. S. Liem1510918.24
Marcel Van Gerven1632139.35
R.J. van Lier17192.39