Abstract
A facial expression is a combination of an expressive
component and a neutral component of a person. In this
paper, we propose to recognize facial expressions by extracting information of the expressive component through
a de-expression learning procedure, called De-expression
Residue Learning (DeRL). First, a generative model is
trained by cGAN. This model generates the corresponding
neutral face image for any input face image . We call this
procedure de-expression because the expressive information is filtered out by the generative model; however, the
expressive information is still recorded in the intermediate layers. Given the neutral face image, unlike previous
works using pixel-level or feature-level difference for facial
expression classification, our new method learns the deposition (or residue) that remains in the intermediate layers
of the generative model. Such a residue is essential as it
contains the expressive component deposited in the generative model from any input facial expression images. Seven
public facial expression databases are employed in our
experiments. With two databases (BU-4DFE and BP4Dspontaneous) for pre-training, the DeRL method has been
evaluated on five databases, CK+, Oulu-CASIA, MMI, BU-
3DFE, and BP4D+. The experimental results demonstrate
the superior performance of the proposed method