Factored principal components analysis, with applications to face recognition

Ian L. Dryden, Li Bai, Christopher J. Brignell, Linlin Shen

Research output: Journal PublicationArticlepeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)229-238
Number of pages10
JournalStatistics and Computing
Volume19
Issue number3
DOIs
Publication statusPublished - Sep 2009
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'Factored principal components analysis, with applications to face recognition'. Together they form a unique fingerprint.

Cite this