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ECCV
2010
Springer

Coupled Gaussian Process Regression for Pose-Invariant Facial Expression Recognition

8 years 5 months ago
Coupled Gaussian Process Regression for Pose-Invariant Facial Expression Recognition
We present a novel framework for the recognition of facial expressions at arbitrary poses that is based on 2D geometric features. We address the problem by first mapping the 2D locations of landmark points of facial expressions in non-frontal poses to the corresponding locations in the frontal pose. Then, recognition of the expressions is performed by using any state-of-the-art facial expression recognition method (in our case, multi-class SVM). To learn the mappings that achieve pose normalization, we use a novel Gaussian Process Regression (GPR) model which we name Coupled Gaussian Process Regression (CGPR) model. Instead of learning single GPR model for all target pairs of poses at once, or learning one GPR model per target pair of poses independently of other pairs of poses, we propose CGPR model, which also models the couplings between the GPR models learned independently per target pairs of poses. To the best of our knowledge, the proposed method is the first one satisfying all: ...
Ognjen Rudovic, Ioannis Patras, Maja Pantic
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ECCV
Authors Ognjen Rudovic, Ioannis Patras, Maja Pantic
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