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MAPTRAITS 2014 - The First Audio/Visual Mapping Personality Traits Challenge - An Introduction: Perceived Personality and Social Dimensions

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Published:12 November 2014Publication History

ABSTRACT

The Audio/Visual Mapping Personality Challenge and Workshop (MAPTRAITS) is a competition event that is organised to facilitate the development of signal processing and machine learning techniques for the automatic analysis of personality traits and social dimensions. MAPTRAITS includes two sub-challenges, the continuous space-time sub-challenge and the quantised space-time sub-challenge. The continuous sub-challenge evaluated how systems predict the variation of perceived personality traits and social dimensions in time, whereas the quantised challenge evaluated the ability of systems to predict the overall perceived traits and dimensions in shorter video clips. To analyse the effect of audio and visual modalities on personality perception, we compared systems under three different settings: visual-only, audio-only and audio-visual. With MAPTRAITS we aimed at improving the knowledge on the automatic analysis of personality traits and social dimensions by producing a benchmarking protocol and encouraging the participation of various research groups from different backgrounds.

References

  1. O. Celiktutan and H. Gunes. Continuous prediction of perceived traits and social dimension in space and time. In Proc. of IEEE Int. Conf. on Image Processing, Paris, France, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  2. F. Eyben, F. Weninger, F. Gross, and B. Schuller. Recent developments in opensmile, the munich open-source multimedia feature extractor. In Proceedings of the 21st ACM international conference on Multimedia, pages 835--838. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. G. McKeown, M. Valstar, R. Cowie, M. Pantic, and M. Schroder. The semaine database: Annotated multimodal records of emotionally colored conversations between a person and a limited agent. IEEE Trans. on Affective Computing, 3(1):5--17, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. E. Sariyanidi, H. Gunes, M. Gökmen, and A. Cavallaro. Local Zernike moment representations for facial affect recognition. In Proc. of British Machine Vision Conf., 2013.Google ScholarGoogle ScholarCross RefCross Ref
  5. X. Xiong and F. De la Torre. Supervised descent method and its application to face alignment. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. MAPTRAITS 2014 - The First Audio/Visual Mapping Personality Traits Challenge - An Introduction: Perceived Personality and Social Dimensions

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      • Published in

        cover image ACM Conferences
        ICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction
        November 2014
        558 pages
        ISBN:9781450328852
        DOI:10.1145/2663204

        Copyright © 2014 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 12 November 2014

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        • review-article

        Acceptance Rates

        ICMI '14 Paper Acceptance Rate51of127submissions,40%Overall Acceptance Rate453of1,080submissions,42%

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