Skeleton-based Personality Recognition using Laban Movement Analysis

Ziya Erkoç, Serkan Demirci, Sinan Sonlu, Uğur Güdükbay
Understanding Social Behavior in Dyadic and Small Group Interactions, PMLR 173:74-87, 2022.

Abstract

Personality is expressed through multiple behavioral elements, including body movement. Using a feature transformation based on Laban Movement Analysis, we present a model for estimating individuals’ Big Five personality traits. Our approach achieves higher performance than other methods without exposing image-level information to the network, which otherwise can leave the system susceptible to bias and result in ethical issues. With the ever-increasing role of computers in our daily lives, human-computer interaction and human understanding have become significant. Our system enables better human understanding for intelligent agents and personal assistants through personality estimation. We utilize Graph Convolutional Networks, commonly used for action recognition for this task.

Cite this Paper

BibTeX
@InProceedings{pmlr-v173-erkoc22a, title = {Skeleton-based Personality Recognition using Laban Movement Analysis}, author = {Erko\c{c}, Ziya and Demirci, Serkan and Sonlu, Sinan and G\"ud\"ukbay, U\u{g}ur}, booktitle = {Understanding Social Behavior in Dyadic and Small Group Interactions}, pages = {74--87}, year = {2022}, editor = {Palmero, Cristina and Jacques Junior, Julio C. S. and Clapés, Albert and Guyon, Isabelle and Tu, Wei-Wei and Moeslund, Thomas B. and Escalera, Sergio}, volume = {173}, series = {Proceedings of Machine Learning Research}, month = {16 Oct}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v173/erkoc22a/erkoc22a.pdf}, url = {https://proceedings.mlr.press/v173/erkoc22a.html}, abstract = {Personality is expressed through multiple behavioral elements, including body movement. Using a feature transformation based on Laban Movement Analysis, we present a model for estimating individuals’ Big Five personality traits. Our approach achieves higher performance than other methods without exposing image-level information to the network, which otherwise can leave the system susceptible to bias and result in ethical issues. With the ever-increasing role of computers in our daily lives, human-computer interaction and human understanding have become significant. Our system enables better human understanding for intelligent agents and personal assistants through personality estimation. We utilize Graph Convolutional Networks, commonly used for action recognition for this task.} }
Endnote
%0 Conference Paper %T Skeleton-based Personality Recognition using Laban Movement Analysis %A Ziya Erkoç %A Serkan Demirci %A Sinan Sonlu %A Uğur Güdükbay %B Understanding Social Behavior in Dyadic and Small Group Interactions %C Proceedings of Machine Learning Research %D 2022 %E Cristina Palmero %E Julio C. S. Jacques Junior %E Albert Clapés %E Isabelle Guyon %E Wei-Wei Tu %E Thomas B. Moeslund %E Sergio Escalera %F pmlr-v173-erkoc22a %I PMLR %P 74--87 %U https://proceedings.mlr.press/v173/erkoc22a.html %V 173 %X Personality is expressed through multiple behavioral elements, including body movement. Using a feature transformation based on Laban Movement Analysis, we present a model for estimating individuals’ Big Five personality traits. Our approach achieves higher performance than other methods without exposing image-level information to the network, which otherwise can leave the system susceptible to bias and result in ethical issues. With the ever-increasing role of computers in our daily lives, human-computer interaction and human understanding have become significant. Our system enables better human understanding for intelligent agents and personal assistants through personality estimation. We utilize Graph Convolutional Networks, commonly used for action recognition for this task.
APA
Erkoç, Z., Demirci, S., Sonlu, S. & Güdükbay, U.. (2022). Skeleton-based Personality Recognition using Laban Movement Analysis. Understanding Social Behavior in Dyadic and Small Group Interactions, in Proceedings of Machine Learning Research 173:74-87 Available from https://proceedings.mlr.press/v173/erkoc22a.html.

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