mlsanityData to 7 Oct 2026

PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

arXiv 2107.11721 ↗First posted 25 Jul 2021cs.CVcited by 7

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Abstract (arXiv)

Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e.g., in cases of surveillance and photo-tagging). To address it, current methods either deploy pose-specific models or frontalize faces by additional modules. Still, they ignore the fact that identity information should be consistent across poses and are not realizing the data imbalance between frontal and profile face images during training. In this paper, we propose an efficient PoseFace framework which utilizes the facial landmarks to disentangle the pose-invariant features and exploits a pose-adaptive loss to handle the imbalance issue adaptively. Extensive experimental results on the benchmarks of Multi-PIE, CFP, CPLFW and IJB have demonstrated the superiority of our method over the state-of-the-arts.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 32 of its 55 references that are in the map. First posted on arXiv 25 Jul 2021.

Builds on

32 of 55 references.

References · 32

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20 more
  1. 2017Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesiscited by 75
  2. 2019Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Networkcited by 51
  3. 2017Towards Large-Pose Face Frontalization in the Wildcited by 50
  4. 2017Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognitioncited by 37
  5. 2020Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Imagescited by 37
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  7. 2018Pose-Robust Face Recognition via Deep Residual Equivariant Mappingcited by 28
  8. 2018Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognitioncited by 27
  9. 2017Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognitioncited by 24
  10. 2018Orthogonal Deep Features Decomposition for Age-Invariant Face Recognitioncited by 20
  11. 2017Reconstruction-Based Disentanglement for Pose-invariant Face Recognitioncited by 19
  12. 2016Triplet Probabilistic Embedding for Face Verification and Clusteringcited by 14
  13. 2018Multicolumn Networks for Face Recognitioncited by 13
  14. 2018Comparator Networkscited by 13
  15. 2018Disentangled Variational Representation for Heterogeneous Face Recognitioncited by 13
  16. 2021Searching for Alignment in Face Recognitioncited by 12
  17. 2018Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalizationcited by 11
  18. 2018Support Vector Guided Softmax Loss for Face Recognitioncited by 10
  19. 2014Shared Representation Learning for Heterogeneous Face Recognitioncited by 9
  20. 2017An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representationscited by 8

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 0; 2025: 1.

7 papers.

  1. 2021Learning Compatible Embeddingscited by 37
  2. 2022Improving Federated Learning Face Recognition via Privacy-Agnostic Clusterscited by 18
  3. 2022Information Maximization for Extreme Pose Face Recognitioncited by 3
  4. 2022Towards Privacy-Preserving, Real-Time and Lossless Feature Matchingcited by 2
  5. 2022Basket-based Softmaxcited by 1
  6. 2025Test-Time Augmentation for Pose-invariant Face Recognitioncited by 1
  7. 2023Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstructioncited by 0