Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Proceedings of the 8th international conference on Multimodal interfaces
A new implementation of common matrix approach using third-order tensors for face recognition
Expert Systems with Applications: An International Journal
Recognition of 3D facial expression dynamics
Image and Vision Computing
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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.