Multimodal analysis of personality traits on videos of self-presentation and induced behavior
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ORIGINAL PAPER
Multimodal analysis of personality traits on videos of self-presentation and induced behavior Dersu Giritlioglu ˘ 1 · Burak Mandira1 · Selim Firat Yilmaz2 · Can Ufuk Ertenli3 · Berhan Faruk Akgür4 · ¸ Can Gürel6,7 · Hamdi Dibeklioglu ˘ 1 Merve Kınıklıoglu ˘ 4 · Aslı Gül Kurt4 · Emre Mutlu5 · Seref Received: 26 May 2020 / Accepted: 29 September 2020 © Springer Nature Switzerland AG 2020
Abstract Personality analysis is an important area of research in several fields, including psychology, psychiatry, and neuroscience. With the recent dramatic improvements in machine learning, it has also become a popular research area in computer science. While the current computational methods are able to interpret behavioral cues (e.g., facial expressions, gesture, and voice) to estimate the level of (apparent) personality traits, accessible assessment tools are still substandard for practical use, not to mention the need for fast and accurate methods for such analyses. In this study, we present multimodal deep architectures to estimate the Big Five personality traits from (temporal) audio-visual cues and transcribed speech. Furthermore, for a detailed analysis of personality traits, we have collected a new audio-visual dataset, namely: Self-presentation and Induced Behavior Archive for Personality Analysis (SIAP). In contrast to the available datasets, SIAP introduces recordings of induced behavior in addition to self-presentation (speech) videos. With thorough experiments on SIAP and ChaLearn LAP First Impressions datasets, we systematically assess the reliability of different behavioral modalities and their combined use. Furthermore, we investigate the characteristics and discriminative power of induced behavior for personality analysis, showing that the induced behavior indeed includes signs of personality traits. Keywords Big five · Estimation of personality traits · Deep learning · Multimodal fusion · Self-presentation · Induced behavior
Seref ¸ Can Gürel [email protected]
Dersu Giritlio˘glu, Burak Mandira, Selim Firat Yilmaz, Can Ufuk Ertenli have equally contributed.
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Dersu Giritlio˘glu [email protected] Burak Mandira [email protected] Selim Firat Yilmaz [email protected] Can Ufuk Ertenli [email protected] Berhan Faruk Akgür [email protected] Merve Kınıklıo˘glu [email protected] Aslı Gül Kurt [email protected] Emre Mutlu [email protected]
Hamdi Dibeklio˘glu [email protected] 1
Department of Computer Engineering, Bilkent University, Ankara, Turkey
2
Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
3
Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
4
Department of Neuroscience, Bilkent University, Ankara, Turkey
5
Psychiatry Clinic, Etimesgut Sehit ¸ Sait Ertürk State Hospital, Ankara, Turkey
6
Department of Psychiatry, Hacettepe University, Ankara, Turkey
7
Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Neth
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