Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

@article{Zhao2016RobustLF,
  title={Robust LSTM-Autoencoders for Face De-Occlusion in the Wild},
  author={Fang Zhao and Jiashi Feng and Jian Zhao and Wenhan Yang and Shuicheng Yan},
  journal={IEEE Transactions on Image Processing},
  year={2016},
  volume={27},
  pages={778-790}
}
Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still challenging for existing face recognizers, which is heavily desired in real-world applications concerning surveillance and security. Although much research effort has been devoted to developing face de-occlusion methods, most of them can only work well under constrained conditions, such as all of faces are from a pre-defined closed set of subjects. In this… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 22 CITATIONS

High-Fidelity Monocular Face Reconstruction based on an Unsupervised Model-based Face Autoencoder.

  • IEEE transactions on pattern analysis and machine intelligence
  • 2018
VIEW 3 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Unsupervised Facial Image De-occlusion with Optimized Deep Generative Models

  • 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)
  • 2018
VIEW 4 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction

  • 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
  • 2017
VIEW 3 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

References

Publications referenced by this paper.
SHOWING 1-10 OF 27 REFERENCES

Robust Face Recognition via Sparse Representation

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2009
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Generative Adversarial Nets

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Context Encoders: Feature Learning by Inpainting

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
VIEW 1 EXCERPT

FaceNet: A unified embedding for face recognition and clustering

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2015
VIEW 1 EXCERPT

Longterm recurrent convolutional networks for visual recognition and description

J. Donahue, L. Hendricksa, S. Guadarrama, M. Rohrbach
  • Proc. IEEE Conf. Comp. Vis. Pattern Recogn. (CVPR), 2015, pp. 2625–2634.
  • 2015
VIEW 1 EXCERPT