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4th Neural Computation and Psychology Workshop, London, 9–11 April 1997 pp 84–97Cite as

Testing Principal Component Representations for Faces

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Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

A variety of experimental results indicate that the human visual system processes faces at least to some extent holistically, rather than by analysing individual features such as nose and eyes. Principal Components Analysis (PCA) of face images, which is widely used in engineering approaches to face identification, produces an inherently global representation. We investigate the psychological plausibility of this representation, looking at correlations with human perceptions of memorability and similarity. We show that transformation of faces to an average shape prior to PCA improves correlations with human ratings

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Author information

Authors and Affiliations

  1. Department of Psychology, University of Stirling, UK

    Peter J. B. Hancock & Vicki Bruce

  2. Department of Psychology, University of Glasgow, UK

    A. Mike Burton

Authors
  1. Peter J. B. Hancock
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  2. Vicki Bruce
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  3. A. Mike Burton
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Editor information

Editors and Affiliations

  1. Centre for Speech and Language, Department of Psychology, Birkbeck College, University of London, Malet Street, WC1E 7HX, London, UK

    John A. Bullinaria BSc, MSc, PhD

  2. Department of Psychology, University College London, Gower Street, WC1E 6BT, London, UK

    David W. Glasspool BSc, Msc  & George Houghton BA, MSc, PhD  & 

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© 1998 Springer-Verlag London Limited

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Cite this paper

Hancock, P.J.B., Bruce, V., Burton, A.M. (1998). Testing Principal Component Representations for Faces. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_8

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  • DOI: https://doi.org/10.1007/978-1-4471-1546-5_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76208-9

  • Online ISBN: 978-1-4471-1546-5

  • eBook Packages: Springer Book Archive

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