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

15 Citations (Scopus)
104 Downloads (Pure)


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)894-911
Number of pages18
JournalIEEE Transactions on Affective Computing
Issue number2
Publication statusPublished - 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


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


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