Browsing by Author "Durupinar, Funda"

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Open Access
Human movement personality detection parameters
(IEEE, 2024-06-23) Sonlu, Sinan; Dogan, Yalım; Ergüzen, Arçin Ülkü; Ünalan, Musa Ege; Demirci, Serkan; Durupinar, Funda; Güdükbay, Uğur
In this study, we develop a system that detects apparent personality traits from animation data containing human movements. Since the datasets that can be used for this purpose lack sufficient variance, we determined labels for the samples in two datasets containing human animations, in terms of the Five Factor Personality Theory, with the help of a user study. Using these labels, we identified movement parameters highly dependent on personality traits and based on Laban Movement Analysis categories. The artificial neural networks we trained for personality analysis from animation data show that models that take the motion parameters determined in the study as input have a higher accuracy rate than models that take raw animation data as input. Therefore, using the parameters determined in this study to evaluate human movements in terms of their personality traits will increase the systems' success.
Open Access
Towards understanding personality expression via body motion
(2024-05-29) Sonlu, Sinan; Dogan, Yalim; Erguzen, Arcin Ulku; Unalan, Musa Ege; Demirci, Serkan; Durupinar, Funda; Gudukbay, Ugur
This work addresses the challenge of data scarcity in personality-labeled datasets by introducing personality labels to clips from two open datasets, ZeroEGGS and Bandai, which provide diverse full-body animations. To this end, we present a user study to annotate short clips from both sets with labels based on the Five-Factor Model (FFM) of personality. We chose features informed by Laban Movement Analysis (LMA) to represent each animation. These features then guided us to select the samples of distinct motion styles to be included in the user study, obtaining high personality variance and keeping the study duration and cost viable. Using the labeled data, we then ran a correlation analysis to find features that indicate high correlation with each personality dimension. Our regression analysis results indicate that highly correlated features are promising in accurate personality estimation. We share our early findings, code, and data publicly.