DrFER：学习用于三维面部表情识别的解耦表示
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
AI总结：
DrFER提出双分支解耦框架，将表情与身份信息分离，适应点云数据，在BU-3DFE和Bosphorus数据集上超越现有三维FER方法。
摘要：
面部表情识别（FER）一直是面部分析领域的一个焦点。在现有的三维FER或二维+三维FER方法中，表情特征的提取常常与身份信息纠缠在一起，损害了这些特征的独特性。为了应对这一挑战，我们引入了创新的DrFER方法，将解耦表示学习的概念引入到三维FER领域。DrFER采用双分支框架，有效地将表情信息与身份信息解耦。与以往在三维面部领域中的解耦尝试不同，我们仔细重新配置了损失函数和网络结构，使整个框架适应点云数据。这种适应性增强了框架识别面部表情的能力，即使在涉及不同头部姿态的情况下也是如此。在BU-3DFE和Bosphorus数据集上进行的大量评估证实，DrFER的性能优于其他三维FER方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
