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Factored principal components analysis, with applications to face recognition


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.

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Correspondence to Ian L. Dryden.

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Dryden, I.L., Bai, L., Brignell, C.J. et al. Factored principal components analysis, with applications to face recognition. Stat Comput 19, 229–238 (2009).

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