Modeling, Recognizing, and Explaining Apparent Personality from Videos

Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah, Sergio Escalera, Yağmur Güç;lütürk, Umut Güçlü, Xavier Baro, Achmadnoer Sukma Wicaksana, Cynthia C.S. Liem, More Authors

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalIEEE Transactions on Affective Computing
DOIs
Publication statusE-pub ahead of print - 2020

Keywords

  • Explainable computer vision
  • First impressions
  • Personality analysis
  • Multimodal information
  • Algorithmic accountability

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  • Cite this

    Escalante, H. J., Kaya, H., Ali Salah, A., Escalera, S., Güç;lütürk, Y., Güçlü, U., Baro, X., Sukma Wicaksana, A., Liem, C. C. S., & More Authors (2020). Modeling, Recognizing, and Explaining Apparent Personality from Videos. IEEE Transactions on Affective Computing, 1-18. https://doi.org/10.1109/TAFFC.2020.2973984