Using a Tensor Framework for the Analysis of Facial Dynamics

Lisa N Gralewski, N Campbell, Ian S Penton-Voak

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

21 Citations (Scopus)


Research has shown that the dynamics of facial motion are important in the perception of gender, identity, and emotion. In this paper we show that it is possible to use a multilinear tensor framework to extract facial motion signatures and to cluster these signatures by gender or by emotion. Here we consider only the dynamics of internal features of the face (e.g. eyebrows, eyelids and mouth) so as to remove structural and shape cues to identity and gender. Such structural gender biases include jaw width and forehead shape and their removal ensures dynamic cues alone are being used. Additionally, we demonstrate the generative capabilities of using a tensor framework, by consistently synthesising new motion signatures.
Translated title of the contributionUsing a Tensor Framework for the Analysis of Facial Dynamics
Original languageEnglish
Title of host publication7th International Conference on Automatic Face and Gesture Recognition (FGR06)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)0-7695-2503-2
Publication statusPublished - 24 Apr 2006

Bibliographical note

Conference Proceedings/Title of Journal: 7th International Conference of Automatic Face and Gesture Recognition, FG2006

Structured keywords

  • Cognitive Science
  • Social Cognition


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