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Detecting personality and emotion traits in crowds from video sequences

Authors:

Rodolfo Migon Favaretto,

Soraia Raupp Musse,

Felipe Vilanova,

Ângelo Brandelli CostaAuthors Info & Claims

Machine Vision and Applications, Volume 30, Issue 5

Pages 999 - 1012

https://doi.org/10.1007/s00138-018-0979-y

Published: 01 July 2019 Publication History

Abstract

This paper presents a methodology to detect personality and basic emotion characteristics of crowds in video sequences. Firstly, individuals are detected and tracked, and then groups are recognized and characterized. Such information is then mapped to OCEAN dimensions, used to find out personality and emotion in videos, based on OCC emotion models. Although it is a clear challenge to validate our results with real life experiments, we evaluate our method with the available literature information regarding OCEAN values of different Countries and also emergent Personal distance among people. Hence, such analysis refer to cultural differences of each country too. Our results indicate that this model generates coherent information when compared to data provided in available literature, as shown in qualitative and quantitative results.

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Cited By

Leekha MKhan SSrinivas HShah RShukla J(2024)VyaktitvaNirdharan: Multimodal Assessment of Personality and Trait Emotional IntelligenceIEEE Transactions on Affective Computing10.1109/TAFFC.2024.340424315:4(2139-2153)Online publication date: 22-May-2024
https://dl.acm.org/doi/10.1109/TAFFC.2024.3404243
Beyan CVinciarelli ABue A(2023)Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A SurveyACM Computing Surveys10.1145/362651656:5(1-41)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3626516
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https://dl.acm.org/doi/10.1007/s00138-023-01438-7
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Published In

cover image Machine Vision and Applications

Machine Vision and Applications Volume 30, Issue 5

July 2019

201 pages

ISSN:0932-8092

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer-Verlag GmbH Germany, part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 July 2019

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Cited By

Leekha MKhan SSrinivas HShah RShukla J(2024)VyaktitvaNirdharan: Multimodal Assessment of Personality and Trait Emotional IntelligenceIEEE Transactions on Affective Computing10.1109/TAFFC.2024.340424315:4(2139-2153)Online publication date: 22-May-2024
https://dl.acm.org/doi/10.1109/TAFFC.2024.3404243
Beyan CVinciarelli ABue A(2023)Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A SurveyACM Computing Surveys10.1145/362651656:5(1-41)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3626516
Nguyen TNguyen TBouchara F(2023)Representing dynamic textures based on polarized gradient featuresMachine Vision and Applications10.1007/s00138-023-01438-734:5Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/s00138-023-01438-7
Krishnani DShivakumara PLu TPal ULopresti DKumar G(2021)A new context-based feature for classification of emotions in photographsMultimedia Tools and Applications10.1007/s11042-020-10404-880:10(15589-15618)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11042-020-10404-8
Migon Favaretto Rde Andrade Araujo VVilanova FBrandelli Costa ARaupp Musse S(2021)Cultural behaviors analysis in video sequencesMachine Vision and Applications10.1007/s00138-021-01225-232:4Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s00138-021-01225-2
Araujo VDalmoro BFavaretto RVilanova FCosta AMusse S(2021)How Much Do We Perceive Geometric Features, Personalities and Emotions in Avatars?Advances in Computer Graphics10.1007/978-3-030-89029-2_42(548-567)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-89029-2_42

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