Sonlu, SinanDogan, YalımErgüzen, Arçin ÜlküÜnalan, Musa EgeDemirci, SerkanDurupinar, FundaGüdükbay, Uğur2025-02-222025-02-222024-06-232165-0608https://hdl.handle.net/11693/116640Conference Name:32nd IEEE Signal Processing and Communications Applications Conference (SIU)Date of Conference:MAY 15-18, 2024In 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.TurkishAnimationPersonalityLaban movement analysisFive factor personalityComputer graphicsFive Factor Personality TheoryConference Paper10.1109/SIU61531.2024.10601008979-8-3503-8896-1