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

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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.

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Acknowledgments

The authors would like to express their gratitude to the anonymous referees for help recommendations. The authors also gratefully acknowledge the financial support of National Natural Science Foundation of China (Grant Nos. 10571001, 60603083, and 60375010).

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Authors and Affiliations

  1. Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing, Jiangsu, 210096, China

    Haixian Wang

  2. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui, 230027, China

    Sibao Chen

  3. School of Mathematics and Physics, Anhui University of Technology, Maanshan, Anhui, 243002, China

    Zilan Hu

  4. Key Lab of ICSP of Ministry of Education, Anhui University, Hefei, Anhui, 230039, China

    Bin Luo

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  1. Haixian Wang
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  2. Sibao Chen
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  3. Zilan Hu
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  4. Bin Luo
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Correspondence to Haixian Wang.

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Wang, H., Chen, S., Hu, Z. et al. Probabilistic two-dimensional principal component analysis and its mixture model for face recognition. Neural Comput & Applic 17, 541–547 (2008). https://doi.org/10.1007/s00521-007-0155-1

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  • DOI: https://doi.org/10.1007/s00521-007-0155-1

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