Modeling, Recognizing, and Explaining Apparent Personality from Videos
Publication date
2022-04
Authors
Escalante, Hugo Jair
Kaya, Heysem
Salah, A.A.
Escalera, Sergio
Gucluturk, Yagmur
Guclu, Umut
Baro, Xavier
Guyon, Isabelle
Jacques, Julio C.S.
Madadi, Meysam
Editors
Advisors
Supervisors
Document Type
Article
Metadata
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taverne
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.
Keywords
Algorithmic accountability, Computational modeling, Computer vision, Explainable computer vision, Face, First impressions, Interviews, Multimodal information, Personality analysis, Predictive models, Videos, Visualization, Taverne, Software, Human-Computer Interaction
Citation
Escalante, H J, Kaya, H, Salah, A A, Escalera, S, Gucluturk, Y, Guclu, U, Baro, X, Guyon, I, Jacques, J C S, Madadi, M, Ayache, S, Viegas, E, Gurpinar, F, Wicaksana, A S, Liem, C, Van Gerven, M A J & Van Lier, R 2022, 'Modeling, Recognizing, and Explaining Apparent Personality from Videos', IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 894-911. https://doi.org/10.1109/TAFFC.2020.2973984