Facial Expression Decomposition
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Hongcheng Wang and
Narendra Ahuja
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1.
Hongcheng Wang and
Narendra Ahuja,
Facial Expression Decomposition, IEEE International Conference on
Computer Vision (ICCV), 2003
Abstract:
In this paper, we
propose a novel approach for facial expression decomposition -
Higher-Order Singular Value Decomposition (HOSVD), a natural
generalization of matrix SVD. We learn the expression subspace and
person subspace from a corpus of images showing seven basic facial
expressions, rather than resort to expert-coded facial expression
parameters. We propose a simultaneous face and facial
expression recognition algorithm, which can classify the given image
into one of the seven basic facial expression categories, and then
other facial expressions of the new person can be synthesized using
the learned expression subspace model. The contributions of this
work lie mainly in two aspects. First, we propose a new
multilinear model (HOSVD) based
approach to model the mapping between persons and expressions, used
for face transfer and facial expression synthesis for a new
person. Second, we
realize
simultaneous face and facial expression recognition as a result of
facial expression decomposition. Experimental results are presented
that illustrate the capability of the person subspace and expression
subspace in both synthesis and recognition tasks. As a quantitative
measure of the quality of synthesis, we propose using Gradient
Minimum Square Error (GMSE) which measures the gradient difference
between the original and synthesized images.
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 Feb 07, 2004
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