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Robust Face Recognition Based on Part-Based Localized Basis Images

  • Jongsun Kim
  • Juneho Yi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

In order for a subspace projection based method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method using ICA architecture I basis images that is robust to local distortion and partial occlusion. The proposed representation only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of “recognition by parts.” We have contrasted our representation with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that our representation performs better than PCA, ICA architecture I, ICA architecture II, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.

Keywords

Face Recognition Independent Component Analysis Basis Image Partial Occlusion Local Distortion 
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 2005

Authors and Affiliations

  • Jongsun Kim
    • 1
  • Juneho Yi
    • 1
  1. 1.School of Information & Communication EngineeringSungkyunkwan University, Korea, Biometrics Engineering Research Center 

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