Automatic extraction of the face identity-subspace

N. P. Costen, T. F. Cootes, G. J. Edwards, C. J. Taylor

    Research output: Contribution to journalArticlepeer-review


    Facial variation divides into a number of functional subspaces, and variation unique to the ensemble. An improved method of measuring these is presented, within the space defined by an appearance model. Initial estimates of the subspaces (lighting, pose, identity and expression) are obtained by principal components analysis on appropriate groups of faces. A recoding algorithm is applied to image codings to maximise the probability of coding across these non-orthogonal subspaces. Ensemble-specific variation is then removed by measuring the spatial predictability of the eigenvectors excluding those, which are less predictable than the ensemble. These procedures significantly enhance identity recognition for a disjoint test set. © 2002 Elsevier Science B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)319-329
    Number of pages10
    JournalImage and Vision Computing
    Issue number5-6
    Publication statusPublished - 6 Mar 2002


    • Appearance models
    • Dimensionality estimation
    • Face recognition
    • Principal components analysis


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