Multimodal analysis of personality traits on videos of self-presentation and induced behavior

• Published in: Journal on multimodal user interfaces Vol. 15; no. 4; pp. 337 - 358
• Main Authors: Giritlioğlu, Dersu, Mandira, Burak, Yilmaz, Selim Firat, Ertenli, Can Ufuk, Akgür, Berhan Faruk, Kınıklıoğlu, Merve, Kurt, Aslı Gül, Mutlu, Emre, Gürel, Şeref Can, Dibeklioğlu, Hamdi
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2021; Springer Nature B.V
• Subjects: Behavior; Computer Science; Cues; Datasets; Image Processing and Computer Vision; Machine learning; Original Paper; Personality; Personality traits; Psychiatry; Psychology; Reliability analysis; Signal,Image and Speech Processing; Speech; User Interfaces and Human Computer Interaction; Video; Deep learning; Induced behavior; Estimation of personality traits; Self-presentation; Big five; Multimodal fusion
• ISSN: 1783-7677; 1783-8738
• DOI: 10.1007/s12193-020-00347-7

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.