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Gender Classification of Human Faces

  • Arnulf B.A. Graf
  • Felix A. Wichmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)

Abstract

This paper addresses the issue of combining pre-processing methods—dimensionality reduction using Principal Component Analysis (PCA) and Locally Linear Embedding (LLE)—with Support Vector Machine (SVM) classification for a behaviorally important task in humans: gender classification. A processed version of the MPI head database is used as stimulus set. First, summary statistics of the head database are studied. Subsequently the optimal parameters for LLE and the SVM are sought heuristically. These values are then used to compare the original face database with its processed counterpart and to assess the behavior of a SVM with respect to changes in illumination and perspective of the face images. Overall, PCA was superior in classification performance and allowed linear separability.

Keywords

Dimensionality reduction PCA LLE gender classification SVM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Arnulf B.A. Graf
    • 1
  • Felix A. Wichmann
    • 1
  1. 1.Max Planck Institute for Biological CyberneticsTübingenGermany

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