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.

创新的 DrFER 方法在 3D 人脸领域引入了概念上的解缠表示学习方法，通过使用双分支框架，有效地将表情信息与身份信息解耦，并通过重新配置损失函数和网络结构适应于点云数据，从而提高了框架在识别面部表情方面的能力，即使在涉及不同头部姿势的情况下，经过对 BU-3DFE 和 Bosphorus 数据集进行的广泛评估表明，DrFER 超过了其他 3D FER 方法的性能。