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DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition (2403.08318v1)

Published 13 Mar 2024 in cs.CV

Abstract: 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.

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References (48)
  1. Learning facial representations from the cycle-consistency of face. In ICCV, 2021.
  2. Manifold based analysis of facial expression. IVC, 2006.
  3. Fast and light manifold cnn based 3d facial expression recognition across pose variations. In ACM MM, 2018.
  4. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In CVPR, 2020.
  5. P. Ekman and W. V. Friesen. Constants across cultures in the face and emotion. J Pers Soc Psychol, 1971.
  6. A point set generation network for 3d object reconstruction from a single image. In CVPR, 2017.
  7. Automatic facial expression recognition on a single 3d face by exploring shape deformation. In ACM MM, 2009.
  8. Apathy is the root of all expressions. In FG, 2017.
  9. Multilinear modelling of faces and expressions. TPAMI, 2020.
  10. Learning identity-invariant motion representations for cross-id face reenactment. In CVPR, 2020.
  11. When age-invariant face recognition meets face age synthesis: A multi-task learning framework. In CVPR, 2021.
  12. Local directional pattern (ldp) for face recognition. In ICCE, 2010.
  13. Accurate facial parts localization and deep learning for 3d facial expression recognition. In FG, 2018.
  14. J. Jiang and W. Deng. Disentangling identity and pose for facial expression recognition. TAC, 2022.
  15. Disentangled representation learning for 3d face shape. In CVPR, 2019.
  16. 2d+ 3d facial expression recognition via discriminative dynamic range enhancement and multi-scale learning. arXiv:2011.08333, 2020.
  17. Facial attention based convolutional neural network for 2d+ 3d facial expression recognition. In VCIP, 2019.
  18. I. Kotsia and I. Pitas. Facial expression recognition in image sequences using geometric deformation features and support vector machines. TIP, 2006.
  19. 3d facial expression recognition via multiple kernel learning of multi-scale local normal patterns. In ICPR, 2012.
  20. An efficient multimodal 2d+ 3d feature-based approach to automatic facial expression recognition. CVIU, 2015.
  21. Multimodal 2d+ 3d facial expression recognition with deep fusion convolutional neural network. TMM, 2017.
  22. Orthogonalization-guided feature fusion network for multimodal 2d+ 3d facial expression recognition. TMM, 2020.
  23. Hard negative generation for identity-disentangled facial expression recognition. PR, 2019.
  24. Bilinear models for 3-d face and facial expression recognition. TIFS, 2008.
  25. Facial expression recognition through cross-modality attention fusion. TCDS, 2022.
  26. Facetunegan: Face autoencoder for convolutional expression transfer using neural generative adversarial networks. C&G, 2023.
  27. Facial expression recognition via joint deep learning of rgb-depth map latent representations. In ICCV workshops, 2017.
  28. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. NeurIPS, 2017.
  29. Bosphorus database for 3d face analysis. In BIOID, 2008.
  30. Facenet: A unified embedding for face recognition and clustering. In CVPR, 2015.
  31. Robust facial expression recognition using local binary patterns. In ICIP, 2005.
  32. Afnet-m: Adaptive fusion network with masks for 2d+ 3d facial expression recognition. In ICIP, 2023.
  33. Ffnet-m: Feature fusion network with masks for multimodal facial expression recognition. In ICME, 2021.
  34. H. Tang and T. S. Huang. 3d facial expression recognition based on properties of line segments connecting facial feature points. In FG, 2008.
  35. 3d facial expression recognition using deep feature fusion cnn. In ISSC, 2019.
  36. Disentangled representation learning gan for pose-invariant face recognition. In CVPR, 2017.
  37. L. Van der Maaten and G. Hinton. Visualizing data using t-sne. JMLR, 2008.
  38. Decorrelated adversarial learning for age-invariant face recognition. In CVPR, 2019.
  39. 3d facial expression recognition based on primitive surface feature distribution. In CVPR, 2006.
  40. Unsupervised domain adaptation with regularized optimal transport for multimodal 2d+ 3d facial expression recognition. In FG, 2018.
  41. Automatic 3d facial expression recognition using geometric scattering representation. In FG, 2015.
  42. A 3d facial expression database for facial behavior research. In FG, 2006.
  43. G. Zhao and M. Pietikainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. TPAMI, 2007.
  44. Muscular movement model-based automatic 3d/4d facial expression recognition. TMM, 2016.
  45. Unsupervised disentanglement of linear-encoded facial semantics. In CVPR, 2021.
  46. Discriminative attention-based convolutional neural network for 3d facial expression recognition. In FG, 2019.
  47. Intensity enhancement via gan for multimodal facial expression recognition. In ICIP, 2020.
  48. Cmanet: Curvature-aware soft mask guided attention fusion network for 2d+ 3d facial expression recognition. In ICME, 2022.
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Authors (3)
  1. Hebeizi Li (1 paper)
  2. Hongyu Yang (43 papers)
  3. Di Huang (203 papers)

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