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Matthias Wimmer, Christoph Mayer, and Bernd Radig. Recognizing Facial Expressions Using Model-based Image Interpretation. In Verbal and Nonverbal Communication Behaviours, COST Action 2102 International Workshop, , Vietri sul Mare, Italy, April 2008. Invited Paper

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Abstract

Even if electronic devices widely occupy our daily lives, human-machine interaction still lacks intuition. Therefore, researchers intend to resolve these shortcomings by augmenting traditional systems with aspects of human-human interaction and consider human emotion, behavior, and intention. This publication focusses on one aspect of this challenge: recognizing facial expressions. Our approach achieves real-time performance and provides robustness for real-world applicability. This computer vision task comprises of various phases for which it exploits model-based techniques that accurately localize facial features, seamlessly track them through image sequences, and finally infer facial expressions visible. We specifically adapt state-of-the-art techniques to each of these challenging phases. Our system has been successfully presented to industrial, political, and scientific audience in various events.

BibTeX Entry

@inproceedings{ wimmer08recognizing,
  author    		= {Matthias Wimmer and Christoph Mayer and Bernd Radig},
  title     		= {Recognizing Facial Expressions Using Model-based Image Interpretation},
  booktitle 		= {Verbal and Nonverbal Communication Behaviours, COST Action 2102 International Workshop},
  year      		= {2008},
  month			= {April},
  address   		= {Vietri sul Mare, Italy},
  publisher		= {},
  xxxvolume		= {},
  xxxpages		= {},
  bib2html_pubtype	= {Refereed Workshop Paper},
  bib2html_rescat	= {Image Understanding},
  bib2html_groups   	= {IU},
  abstract          	= 
  			{Even if electronic devices widely occupy our daily lives, human-machine interaction still lacks intuition. Therefore, researchers intend to resolve these shortcomings by augmenting traditional systems with aspects of human-human interaction and consider human emotion, behavior, and intention.
			This publication focusses on one aspect of this challenge: recognizing facial expressions. Our approach achieves real-time performance and provides robustness for real-world applicability. This computer vision task comprises of various phases for which it exploits model-based techniques that accurately localize facial features, seamlessly track them through image sequences, and finally infer facial expressions visible. We specifically adapt state-of-the-art techniques to each of these challenging phases. Our system has been successfully presented to industrial, political, and scientific audience in various events.},
  note			= {Invited Paper},
}

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