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Merging Subspace Models for Face Recognition

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Computer Analysis of Images and Patterns (CAIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2756))

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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.

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© 2003 Springer-Verlag Berlin Heidelberg

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Skarbek, W. (2003). Merging Subspace Models for Face Recognition. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_74

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  • DOI: https://doi.org/10.1007/978-3-540-45179-2_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40730-0

  • Online ISBN: 978-3-540-45179-2

  • eBook Packages: Springer Book Archive

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