skip to main content
10.1145/2797143.2797165acmotherconferencesArticle/Chapter ViewAbstractPublication PageseannConference Proceedingsconference-collections
research-article

Exploiting the Use of Ensemble Classifiers to Enhance the Precision of User's Emotion Classification

Authors Info & Claims
Published:25 September 2015Publication History

ABSTRACT

There is an increasing number of studies in the area of Human-Computer Interaction (HCI) that bears witness to the importance of taking account of emotional factors in interactions with computer systems. By getting to know the emotions of the users, it is possible for artificial agents to have an influence on human feelings with a view to stimulating them in a particular or everyday activities. Thus, one of the great challenges of the HCI area is to enable computer systems to recognize and interpret the feelings of the users. This article sets out a functional Ensemble model for the classification of emotions based on the motor facial expressions of the users. The results described in this article show that the Ensemble Classification that is put forward, can achieve greater rates of accuracy in classifying feelings than what can be obtained by using a single classifier.

References

  1. J. N. Bailenson, E. D. Pontikakis, I. B. Mauss, et al. Real-time classification of evoked emotions using facial feature tracking and physiological responses. Intl. journal of human-computer studies, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. R. R. Bouckaert, E. Frank, M. Hall, R. Kirkby, et al. Weka manual for version 3-7-8, 2013.Google ScholarGoogle Scholar
  3. G. Chanel, J. J. Kierkels, M. Soleymani, et al. Short-term emotion assessment in a recall paradigm. Intl. Journal of Human-Computer Studies, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. R. O. Duda, P. E. Hart, et al. Pattern classification. John Wiley & Sons, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. Ekman. Darwin and facial expression: A century of research in review. Ishk, 2006.Google ScholarGoogle Scholar
  6. G. P. R. Filho, J. Ueyama, L. A. Villas, A. R. Pinto, and G. Pessin. Nodepm: A remote monitoring alert system for energy consumption using probabilistic techniques. Sensors, 2014.Google ScholarGoogle Scholar
  7. J. Klein, Y. Moon, et al. This computer responds to user frustration:: Theory, design, and results. Interacting with computers, 2002.Google ScholarGoogle Scholar
  8. O. Langner, R. Dotsch, G. Bijlstra, et al. Presentation and validation of the radboud faces database. Cognition and Emotion, 2010.Google ScholarGoogle Scholar
  9. G. Libralon and R. Romero. Mapping of facial elements for emotion analysis. In Proceedings of the Brazilian Conf. on Intelligent Systems, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. A. Lichtenstein et al. Comparing two emotion models for deriving affective states from physiological data. In Affect and Emotion in HCI. Springer, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. R. LiKamWa, Y. Liu, et al. Moodscope: Building a mood sensor from smartphone usage patterns. In Proceeding of the 11th annual intl. conf. on Mobile systems, applications, and services, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. P. Lucey, J. F. Cohn, et al. The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In Computer Vision and Pattern Recognition Workshops, IEEE Computer Society Conf. on, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  13. S. Mahlke and M. Minge. Consideration of multiple components of emotions in human-technology interaction. In Affect and emotion in HCI. Springer, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. Øhrn et al. Rough sets: a knowledge discovery technique for multifactorial medical outcomes. American journal of physical medicine & rehabilitation, 2000.Google ScholarGoogle Scholar
  15. C. Peter and B. Urban. Emotion in human-computer interaction. In Expanding the Frontiers of Visual Analytics and Visualization. Springer, 2012.Google ScholarGoogle Scholar
  16. S. Ramakrishnan and I. M. El Emary. Speech emotion recognition approaches in human computer interaction. Telecommunication Systems, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. M. Saragih, S. Lucey, et al. Deformable model fitting by regularized landmark mean-shift. Intl. Journal of Computer Vision, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. K. R. Scherer. What are emotions? and how can they be measured? Social science information, 2005.Google ScholarGoogle Scholar
  19. B. Schuller, S. Reiter, R. Muller, et al. Speaker independent speech emotion recognition by ensemble classification. In Multimedia and Expo, 2005. ICME 2005. IEEE Intel. Conf. on, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  20. F. Zhou, X. Qu, M. G. Helander, et al. Affect prediction from physiological measures via visual stimuli. Intl. Journal of Human-Computer Studies, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Exploiting the Use of Ensemble Classifiers to Enhance the Precision of User's Emotion Classification

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        EANN '15: Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS)
        September 2015
        266 pages
        ISBN:9781450335805
        DOI:10.1145/2797143

        Copyright © 2015 ACM

        © 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 September 2015

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        EANN '15 Paper Acceptance Rate36of60submissions,60%Overall Acceptance Rate36of60submissions,60%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader