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Depth Estimation of Face Images Based on the Constrained ICA Model

  • Zhan-Li Sun
  • Kin-Man Lam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

In this paper, we propose a novel and efficient algorithm to reconstruct the 3D structure of a human face from one or a number of its 2D images with different poses. In our proposed algorithm, the rotation and translation process from a frontal-view face image to a non-frontal-view face image is at first formulated as a constrained independent component analysis (cICA) model. Then, the overcomplete ICA problem is converted into a normal ICA problem. The CANDIDE model is also employed to design a reference signal in our algorithm. Moreover, a model-integration method is proposed to improve the depth-estimation accuracy when multiple non-frontal-view face images are available. Experimental results on a real 3D face image database demonstrate the feasibility and efficiency of the proposed method.

Keywords

Independent Component Analysis Face Image Independent Component Analysis Blind Source Separation Depth Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zhan-Li Sun
    • 1
    • 2
  • Kin-Man Lam
    • 1
  1. 1.Centre for Signal Processing, Department of Electronic and Information EngineeringThe Hong Kong Polytechnic University 
  2. 2.Hefei Institute of Intelligent MachinesChinese Academy of Sciences 

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