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Merging Subspace Models for Face Recognition

  • Władysław Skarbek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2756)

Abstract

The merging problem for principal subspace (PS) models is considered in the form: given two principal subspace models \(\mathcal M_i\) for independent training data sequences, assuming that the original data is not available, find the subspace model for the union of the original data sets. The principal subspace merging (PSM) algorithm and its approximated version (APSM) are proposed to solve the problem. The accuracy and the complexity of the approach has been mathematically analyzed and verified on face image models. If data vectors are modeled by projections into a linear subspace of dimension r in N dimensional feature space then the algorithm has O(r(4N 2+13r 2)) time complexity.

Keywords

Face Recognition Singular Value Decomposition Projection Error Singular Subspace Descriptor Size 
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 2003

Authors and Affiliations

  • Władysław Skarbek
    • 1
  1. 1.Faculty of Electronics and Information TechnologyWarsaw University of Technology 

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