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Gender Classification of Human Faces


Conference Paper


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.

Author(s): Graf, ABA. and Wichmann, FA.
Book Title: Biologically Motivated Computer Vision
Journal: Biologically Motivated Computer Vision 2002
Pages: 1-18
Year: 2002
Month: November
Day: 0
Editors: B{\"u}lthoff, H. H., S.W. Lee, T. A. Poggio and C. Wallraven
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/3-540-36181-2_49
Event Name: Second International Workshop on Biologically Motivated Computer Vision (BMCV 2002)
Event Place: Tübingen, Germany

Address: Berlin, Germany
Digital: 0
ISBN: 3-540-36181-2
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Gender Classification of Human Faces},
  author = {Graf, ABA. and Wichmann, FA.},
  journal = {Biologically Motivated Computer Vision 2002},
  booktitle = {Biologically Motivated Computer Vision},
  pages = {1-18},
  editors = {B{\"u}lthoff, H. H., S.W. Lee, T. A. Poggio and C. Wallraven},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = nov,
  year = {2002},
  month_numeric = {11}