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; null More Authors

doi:10.1109/TAFFC.2020.2973984

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

Multimedia Computing

Research output: Contribution to journal › Article › Scientific › peer-review

7 Citations (Scopus)

39 Downloads (Pure)

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 language	English
Pages (from-to)	1-18
Number of pages	18
Journal	IEEE Transactions on Affective Computing
DOIs	https://doi.org/10.1109/TAFFC.2020.2973984
Publication status	Published - 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
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.

Keywords

Explainable computer vision
First impressions
Personality analysis
Multimodal information
Algorithmic accountability

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10.1109/TAFFC.2020.2973984

Modeling_Recognizing_and_Explaining_Apparent_Personality_from_VideosFinal published version, 5.39 MB

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Cite this

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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.",

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doi = "10.1109/TAFFC.2020.2973984",

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Modeling, Recognizing, and Explaining Apparent Personality from Videos. / Escalante, Hugo Jair; Kaya, Heysem; Ali Salah, Albert; Escalera, Sergio; Güç;lütürk, Yağmur; Güçlü, Umut ; Baro, Xavier; Sukma Wicaksana, Achmadnoer ; Liem, Cynthia C.S.; More Authors.

In: IEEE Transactions on Affective Computing, 2020, p. 1-18.

Research output: Contribution to journal › Article › Scientific › peer-review

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T1 - Modeling, Recognizing, and Explaining Apparent Personality from Videos

AU - Escalante, Hugo Jair

AU - Kaya, Heysem

AU - Ali Salah, Albert

AU - Escalera, Sergio

AU - Güç;lütürk, Yağmur

AU - Güçlü, Umut

AU - Baro, Xavier

AU - Sukma Wicaksana, Achmadnoer

AU - Liem, Cynthia C.S.

AU - More Authors, null

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AB - 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.

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KW - Algorithmic accountability

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Modeling, Recognizing, and Explaining Apparent Personality from Videos

Abstract

Bibliographical note

Keywords

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