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Noise Face Image Hallucination via Data-Driven Local Eigentransformation

  • Xiaohui Dong
  • Ruimin Hu
  • Junjun Jiang
  • Zhen Han
  • Liang Chen
  • Ge Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8879)

Abstract

Face hallucination refers to inferring an High-Resolution (HR) face image from the input Low-Resolution (LR) one. It plays a vital role in LR face recognition by both manual and computer. The eigentransformation method based on Principal Component Analysis (PCA), which represents face image as a linear combination of the eigenfaces, has attracted considerable interests because of its simplicity and effectiveness. However, the face image observed is in a high-dimensional non-linear space, whose statistical properties cannot be captured by the PCA based linear modeling method. To this end, in this paper we advance a Data-driven Local Eigentransformation (DLE) method for face hallucination by exploiting the local geometry structure of data manifold and learning a specified eigentransformation model for each observed image. Experimental results show the effectiveness of the proposed approach for hallucinating face images especially with noise.

Keywords

Face hallucination super-resolution local eigentransformation noise face image video surveillance 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaohui Dong
    • 1
  • Ruimin Hu
    • 1
    • 2
  • Junjun Jiang
    • 1
  • Zhen Han
    • 1
    • 2
  • Liang Chen
    • 1
  • Ge Gao
    • 1
    • 2
  1. 1.National Engineering Research Center for Multimedia SoftwareComputer School of Wuhan UniversityChina
  2. 2.Research Institute of Wuhan University in ShenzhenChina

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