• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

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

      Thumbnail
      View / Download
      2.8 Mb
      Author(s)
      Giritlioğlu, Dersu
      Mandira, Burak
      Yılmaz, Selim Fırat
      Ertenli, C. U.
      Akgür, Berhan Faruk
      Kınıklıoğlu, Merve
      Kurt, Aslı Gül
      Mutlu, E.
      Dibeklioğlu, Hamdi
      Date
      2020
      Source Title
      Journal on Multimodal User Interfaces
      Print ISSN
      1783-7677
      Electronic ISSN
      1783-8738
      Publisher
      Springer
      Language
      English
      Type
      Article
      Item Usage Stats
      198
      views
      629
      downloads
      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
      Permalink
      http://hdl.handle.net/11693/75892
      Published Version (Please cite this version)
      https://dx.doi.org/10.1007/s12193-020-00347-7
      Collections
      • Aysel Sabuncu Brain Research Center (BAM) 228
      • Department of Computer Engineering 1510
      • Department of Electrical and Electronics Engineering 3868
      • National Magnetic Resonance Research Center (UMRAM) 250
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCoursesThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCourses

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 2976
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy