TR2002-12

Learning Gender with Support Faces

    •  Baback Moghaddam and Ming-Hsuan Yang, "Learning Gender with Support Faces", Tech. Rep. TR2002-12, Mitsubishi Electric Research Laboratories, Cambridge, MA, January 2002.
      BibTeX TR2002-12 PDF
      • @techreport{MERL_TR2002-12,
      • author = {Baback Moghaddam and Ming-Hsuan Yang},
      • title = {Learning Gender with Support Faces},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2002-12},
      • month = jan,
      • year = 2002,
      • url = {https://www.merl.com/publications/TR2002-12/}
      • }
  • Research Areas:

    Artificial Intelligence, Computer Vision

Abstract:

Nonlinear Support Vector Machines (SVMs) are investigated for appearance-based gender classification with low resolution "thumbnail" faces processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensemble-RBF networks. Furthermore, the difference in classification performance with low resolution "thumbnails" (21-by-12 pixels) and the corresponding higher resolution images (84-by-48 pixels) was found to be only 1%, thus demonstrating robustness and stability with respect to scale and degree of facial detail.