Occlusion-Robust Face Recognition Using Iterative Stacked Denoising Autoencoder

  • Ying Zhang
  • Rui Liu
  • Saizheng Zhang
  • Ming Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)


This paper investigates how to recognize faces with partial occlusions using iterative stacked denoising autoencoder (ISDAE). We introduce a mapping-autoencoder (MAE) for occlusion detection, which requires no prior knowledge of occlusion. Inspired by stacked denoising autoencoder (SDAE)’s capability to learn patterns from noisy data, we propose a novel iterative structure of SDAE for occluded faces restoration. Deep neural network (DNN) is used for final recognition. Compared with the state-of-the-art approaches (e.g. sparse representation), ISDAE achieves competitive results under serious occlusion conditions.


face recognition occlusion stacked denoising autoencoder restricted boltzmann machine iterative deep neural network 


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  1. 1.
    Saito, Y., Kenmochi, Y., Kotani, K.: Estimation of eyeglassless facial images using principal component analysis. In: IEEE ICIP (1999)Google Scholar
  2. 2.
    Hwang, B.W., Lee, S.W.: Reconstruction of partially damaged face images based on a morphable model. IEEE TPAMI 25(3), 365–372 (2003)CrossRefGoogle Scholar
  3. 3.
    Li, S.Z., Hou, X.W., Zhang, H.J., Cheng, Q.S.: Learning spatially localized, part-based representation. In: IEEE CVPR (2001)Google Scholar
  4. 4.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE TPAMI 31(2), 210–227 (2008)CrossRefGoogle Scholar
  5. 5.
    Hinton, G.E.: A Practical Guide to Training Restricted Boltzmann Machines. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 599–619. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.: Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research 11, 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research 11, 625–660 (2010)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Bengio, Y.: Learning Deep Architectures for AI. Foundations and Trends in Machine Learning 2(1), 1–127 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: ICML (2011)Google Scholar
  10. 10.
    Luo, P., Wang, X.G., Tang, X.O.: Hierarchical Face Parsing via Deep Learning. In: IEEE CVPR (2012)Google Scholar
  11. 11.
    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Computation 14, 1771–1800 (2002)CrossRefzbMATHGoogle Scholar
  12. 12.
    Martinez, A., Benavente, R.: The AR face database. CVC Tech. Report 24 (1998)Google Scholar
  13. 13.
    Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ying Zhang
    • 1
  • Rui Liu
    • 2
  • Saizheng Zhang
    • 2
  • Ming Zhu
    • 1
  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Department of Electronic Engineering and Information ScienceUniversity of Science and Technology of ChinaHefeiChina

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