Face Recognition Using Probabilistic Two-Dimensional Principal Component Analysis and Its Mixture Model

  • Haixian Wang
  • Zilan Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


In this paper, by supposing a parametric Gaussian distribution over the image space (spanned by the row vectors of 2D image matrices) and a spherical Gaussian noise model for the image, we endow the two-dimensional principal component analysis (2DPCA) with a probabilistic framework called probabilistic 2DPCA (P2DPCA), which is robust to noise. Further, by using the probabilistic perspective of P2DPCA, we extend P2DPCA to a mixture of local P2DPCA models (MP2DPCA). MP2DPCA offers us a method of being able to model faces in unconstrained (complex) environment with possibly large variation. The model parameters could be fitted on the basis of maximum likelihood (ML) estimation via the expectation maximization (EM) algorithm. The experimental recognition results on UMIST face database confirm the effectivity of the proposed methods.


Face Recognition Recognition Rate Expectation Maximization Algorithm Probabilistic Principal Component Analysis Probabilistic Perspective 
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

  • Haixian Wang
    • 1
  • Zilan Hu
    • 2
  1. 1.Research Center for Learning ScienceSoutheast UniversityNanjingP.R. China
  2. 2.School of Mathematics and PhysicsAnhui University of TechnologyMaanshanP.R. China

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