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Facial Gender Classification Using Shape from Shading and Weighted Principal Geodesic Analysis

  • Jing Wu
  • W. A. P. Smith
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

In this paper, we investigate gender classification based on 2.5D facial surface normals (facial needle-maps) which can be recovered from 2D intensity images using a non-lambertian Shape-from-shading (SFS) method. We also describe a weighted principal geodesic analysis (WPGA) method to extract features from facial surface normals. By incorporating the weight matrix into principal geodesic analysis (PGA), we control the obtained principal variance axes to be in the direction of the variance on gender information. For classification, an a posteriori probability based method is adopted. Experimental results confirms that using WPGA increases the gender discriminating power in the leading eigenvectors, and also demonstrates the feasibility of gender classification based on facial shape information.

Keywords

Weight Matrix Projection Matrix Gender Information Diagonal Weight Matrix Facial Gender 
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-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jing Wu
    • 1
  • W. A. P. Smith
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceThe University of YorkYorkUK

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