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Automatic recognition of lower facial action units

Published: 24 August 2010 Publication History

Abstract

The face is an important source of information in multimodal communication. Facial expressions are generated by contractions of facial muscles, which lead to subtle changes in the area of the eyelids, eye brows, nose, lips and skin texture, often revealed by wrinkles and bulges. To measure these subtle changes, Ekman et al.[5] developed the Facial Action Coding System (FACS). FACS is a human-observer-based system designed to detect subtle changes in facial features, and describes facial expressions by action units (AUs). We present a technique to automatically recognize lower facial Action Units, independently from one another. Even though we do not explicitly take into account AU combinations, thereby making the classification process harder, an average F1 score of 94.83% is achieved.

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Cited By

View all
  • (2012)GPSO versus neural network in facial emotion detection2012 IEEE Symposium on Industrial Electronics and Applications10.1109/ISIEA.2012.6496648(299-304)Online publication date: Sep-2012
  • (2011)Context-independent facial action unit recognition using shape and gabor phase informationProceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I10.5555/2062780.2062841(548-557)Online publication date: 9-Oct-2011
  • (2011)Automatic real-time FACS-coder to anonymise drivers in eye tracker videos2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)10.1109/ICCVW.2011.6130492(1986-1993)Online publication date: Nov-2011
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Published In

cover image ACM Other conferences
MB '10: Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research
August 2010
183 pages
ISBN:9781605589268
DOI:10.1145/1931344
  • Editors:
  • Emilia Barakova,
  • Boris de Ruyter,
  • Andrew Spink
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2010

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Author Tags

  1. AdaBoost
  2. OVL
  3. SVM
  4. facial action units

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Cited By

View all
  • (2012)GPSO versus neural network in facial emotion detection2012 IEEE Symposium on Industrial Electronics and Applications10.1109/ISIEA.2012.6496648(299-304)Online publication date: Sep-2012
  • (2011)Context-independent facial action unit recognition using shape and gabor phase informationProceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I10.5555/2062780.2062841(548-557)Online publication date: 9-Oct-2011
  • (2011)Automatic real-time FACS-coder to anonymise drivers in eye tracker videos2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)10.1109/ICCVW.2011.6130492(1986-1993)Online publication date: Nov-2011
  • (2011)Context-Independent Facial Action Unit Recognition Using Shape and Gabor Phase InformationAffective Computing and Intelligent Interaction10.1007/978-3-642-24600-5_58(548-557)Online publication date: 2011

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