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Face Recognition Using Principal Geodesic Analysis and Manifold Learning

  • Matthew P. Dickens
  • William A. P. Smith
  • Jing Wu
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4477)

Abstract

This paper describes how face recognition can be effected using 3D shape information extracted from single 2D image views. We characterise the shape of the field of facial normals using a statistical model based on principal geodesic analysis. The model can be fitted to 2D brightness images of faces to recover a vector of shape parameters. Since it captures variations in a field of surface normals, the dimensionality of the shape vector is twice the number of image pixels. We investigate how to perform face recognition using the output of PGA by applying a number of dimensionality reduction techniques including principal components analysis, locally linear embedding, locality preserving projection and Isomap.

Keywords

Face Recognition Recognition Performance Rand Index Locally Linear Embedding Locality Preserve Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Matthew P. Dickens
    • 1
  • William A. P. Smith
    • 1
  • Jing Wu
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer Science, The University of York 

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