Title:	First Results on Using Transformer for Extroversion Personality Trait Recognition
Authors:	Goncalves, Alan Carvalho, Marco A.G. Ramos, Josue J.G. Paiva, Pedro V.V.
Citation:	Journal of WSCG. 2024, vol. 32, no. 1-2, p. 111-118.
Issue Date:	2024
Publisher:	Václav Skala - UNION Agency
Document type:	článek article
URI:	http://hdl.handle.net/11025/57350
ISSN:	1213 – 6972 1213 – 6980 (CD-ROM) 1213 – 6964 (on-line)
Keywords:	rozpoznání osobnostních rysů;model OCEAN;ovlivňování výpočetní techniky;zpracování videa
Keywords in different language:	personality trait recognition;OCEAN model;affect computing;video processing
Abstract in different language:	Personality traits are characteristics that can describe a person’s behavior, also reflecting their thoughts and feel ings. There are those who support the idea that traits can be strong predictors of leadership, implying emotional stability of the individual. Knowing the importance of the subject, areas such as psychology and neuropsychol ogy have been studying and analyzing personality, aiming to better understand such patterns that guide behavior. A model widely accepted to categorize personality traits is known as Big Five and uses the acronym OCEAN: Openness, Conscientiousness, Extroversion, Agreeableness and Neuroticism. On the other hand, new approaches that emerged from the field of computer vision allow to analyzing personality from visual data, making this new area of research quite attractive for researchers. This work presents an initial study of the use of the Transformer architecture to analyze personality traits, with a specific focus on extroversion, using digital videos of human faces. A literature review was carried out focusing on the application of computational techniques in this issue involving deep learning and Transformers. We also accomplished an experiment analysing Extroversion personality trait, as a starting point for our studies, using the ChaLearn dataset. An AUC (Area under the ROC Curve) value of 71.04% was obtained, with fine adjustment of parameters in the transformer, demonstrating the robustness of the proposed architecture
Rights:	© Václav Skala - UNION Agency © Václav Skala - UNION Agency
Appears in Collections:	Volume 32, number 1-2 (2024)

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