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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)

Abstract

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.

Keywords

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

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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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