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Tensor Factorization by Simultaneous Estimation of Mixing Factors for Robust Face Recognition and Synthesis

  • Sung Won Park
  • Marios Savvides
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)

Abstract

Facial images change appearance due to multiple factors such as poses, lighting variations, facial expressions, etc. Tensor approach, an extension of conventional matrix, is appropriate to analyze facial factors since we can construct multilinear models consisting of multiple factors using tensor framework. However, given a test image, tensor factorization, i.e., decomposition of mixing factors, is a difficult problem especially when the factor parameters are unknown or are not in the training set. In this paper, we propose a novel tensor factorization method to decompose the mixing factors of a test image. We set up a tensor factorization problem as a least squares problem with a quadratic equality constraint, and solve it using numerical optimization techniques. The novelty in our approach compared to previous work is that our tensor factorization method does not require any knowledge or assumption of test images. We have conducted several experiments to show the versatility of the method for both face recognition and face synthesis.

Keywords

Face Recognition Test Image Face Image Projection Method Training Image 
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

  • Sung Won Park
    • 1
  • Marios Savvides
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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