An Event Driven Fusion Approach for Enjoyment Recognition in Real-time

Florian Lingenfelser, Johannes Wagner, Elisabeth Andre, Gary McKeown, Will Curran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)
409 Downloads (Pure)


Social signals and interpretation of carried information is of high importance in Human Computer Interaction. Often used for affect recognition, the cues within these signals are displayed in various modalities. Fusion of multi-modal signals is a natural and interesting way to improve automatic classification of emotions transported in social signals. Throughout most present studies, uni-modal affect recognition as well as multi-modal fusion, decisions are forced for fixed annotation segments across all modalities. In this paper, we investigate the less prevalent approach of event driven fusion, which indirectly accumulates asynchronous events in all modalities for final predictions. We present a fusion approach, handling short-timed events in a vector space, which is of special interest for real-time applications. We compare results of segmentation based uni-modal classification and fusion schemes to the event driven fusion approach. The evaluation is carried out via detection of enjoyment-episodes within the audiovisual Belfast Story-Telling Corpus.
Original languageEnglish
Title of host publicationMM '14 Proceedings of the 22nd ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Print)9781450330633
Publication statusPublished - 03 Nov 2014
Event2014 ACM Conference on Multimedia, MM 2014 - Orlando, United States
Duration: 03 Nov 201407 Nov 2014


Conference2014 ACM Conference on Multimedia, MM 2014
Country/TerritoryUnited States


  • Affect recognition
  • Event-driven fusion
  • Multi-modal fusion
  • Social signal processing

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Software


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