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Journal Articles IEEE Transactions on Affective Computing Year : 2020

Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos

Stéphane Ayache

Abstract

Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. 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 computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.

Dates and versions

hal-01991652 , version 1 (23-01-2019)

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Cite

Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah, Sergio Escalera, Yagmur Gucluturk, et al.. Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos. IEEE Transactions on Affective Computing, 2020. ⟨hal-01991652⟩
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