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Viewpoint-Invariant Face Recognition Based on View-Based Representation

  • Jinyun Chung
  • Juho Lee
  • Hyun Jin Park
  • Hyun Seung Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

In this paper, we suggest a viewpoint-invariant face recognition model based on view-based representation. The suggested model has four stages: view-based representation, viewpoint classification, frontal face estimation and face recognition. For view-based representation, we obtained the feature space by using independent subspace analysis, the bases of which are grouped like the neurons in the brain’s visual area. The viewpoint of a facial image can be easily classified by a single-layer perceptron due to view-dependent activation characteristic of the feature space. To estimate the independent subspace analysis representation of frontal face, a radial basis neural network learns to generalize the relation of the bases between two viewpoints. Face recognition relies on a normalized correlation for selecting the most similar frontal faces in a gallery. Through our face recognition experiment on XM2VTS [9], we obtained a face recognition rate of 89.33%.

Keywords

Face Recognition Facial Image Radial Basis Function Neural Network Frontal Face Radial Basis Neural Network 
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 2006

Authors and Affiliations

  • Jinyun Chung
    • 1
  • Juho Lee
    • 1
  • Hyun Jin Park
    • 1
  • Hyun Seung Yang
    • 1
  1. 1.Korea Advanced Institute of Science and Technology, 373-1, Guseong-dong, Yuseong-gu, Daejeon, 305-701Korea

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