Probabilistic two-dimensional principal component analysis and its mixture model for face recognition

  • Authors:

  • Haixian Wang;Sibao Chen;Zilan Hu;Bin Luo

  • Affiliations:

  • Southeast University, Key Laboratory of Child Development and Learning Science of Ministry of Education, 210096, Nanjing, Jiangsu, China;University of Science and Technology of China, Department of Electronic Engineering and Information Science, 230027, Hefei, Anhui, China;Anhui University of Technology, School of Mathematics and Physics, 243002, Maanshan, Anhui, China;Anhui University, Key Lab of ICSP of Ministry of Education, 230039, Hefei, Anhui, China

  • Venue:
  • Neural Computing and Applications
  • Year:
  • 2008

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Abstract

Recently, two-dimensional principal component analysis (2DPCA) as a novel eigenvector-based method has proved to be an efficient technique for image feature extraction and representation. 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 2DPCA with a probabilistic framework called probabilistic 2DPCA (P2DPCA), which is robust to noise. Further, by using the probabilistic perspective of P2DPCA, we extend the P2DPCA to a mixture of local P2DPCA models (MP2DPCA). The MP2DPCA offers us a method of being able to model faces in unconstrained (complex) environment. 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, AR face database, and the face recognition (FR) data collected at University of Essex confirm the effectivity of the proposed methods.