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Statistics and Computing

, Volume 19, Issue 3, pp 229–238 | Cite as

Factored principal components analysis, with applications to face recognition

  • Ian L. DrydenEmail author
  • Li Bai
  • Christopher J. Brignell
  • Linlin Shen
Article

Abstract

A dimension reduction technique is proposed for matrix data, with applications to face recognition from images. In particular, we propose a factored covariance model for the data under study, estimate the parameters using maximum likelihood, and then carry out eigendecompositions of the estimated covariance matrix. We call the resulting method factored principal components analysis. We also develop a method for classification using a likelihood ratio criterion, which has previously been used for evaluating the strength of forensic evidence. The methodology is illustrated with applications in face recognition.

Keywords

Face recognition Forensic identification Gabor wavelets Kernel density estimator Likelihood ratio Multivariate normal Principal components analysis 

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Ian L. Dryden
    • 1
    Email author
  • Li Bai
    • 2
  • Christopher J. Brignell
    • 1
  • Linlin Shen
    • 3
  1. 1.School of Mathematical SciencesUniversity of NottinghamNottinghamUK
  2. 2.School of Computer ScienceUniversity of NottinghamNottinghamUK
  3. 3.School of Information and EngineeringShen Zhen UniversityShen ZhenChina

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