mlsanityData to 14 Oct 2026

DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition

arXiv 2403.08318 ↗First posted 13 Mar 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 47 references that are in the map. First posted on arXiv 13 Mar 2024.

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References · 14

  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  2. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  3. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
  4. 2020Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learningcited by 220
  5. 2019Disentangled Representation Learning for 3D Face Shapecited by 48
  6. 2021When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Frameworkcited by 29
  7. 2019Decorrelated Adversarial Learning for Age-Invariant Face Recognitioncited by 21
  8. 2021Learning Facial Representations from the Cycle-consistency of Facecited by 20
  9. 2022Disentangling Identity and Pose for Facial Expression Recognitioncited by 11
  10. 2021FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networkscited by 9
  11. 2018Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognitioncited by 7
  12. 2021Unsupervised Disentanglement of Linear-Encoded Facial Semanticscited by 7
2 more
  1. 20202D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learningcited by 5
  2. 2022AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognitioncited by 3

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