  Facial Expression Recognition (FER) has consistently been a focal point in
the field of facial analysis. In the context of existing methodologies for 3D
FER or 2D+3D FER, the extraction of expression features often gets entangled
with identity information, compromising the distinctiveness of these features.
To tackle this challenge, we introduce the innovative DrFER method, which
brings the concept of disentangled representation learning to the field of 3D
FER. DrFER employs a dual-branch framework to effectively disentangle
expression information from identity information. Diverging from prior
disentanglement endeavors in the 3D facial domain, we have carefully
reconfigured both the loss functions and network structure to make the overall
framework adaptable to point cloud data. This adaptation enhances the
capability of the framework in recognizing facial expressions, even in cases
involving varying head poses. Extensive evaluations conducted on the BU-3DFE
and Bosphorus datasets substantiate that DrFER surpasses the performance of
other 3D FER methods.
