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Improving the Generalization of Fisherface by Training Class Selection Using SOM2

  • Jiayan Jiang
  • Liming Zhang
  • Tetsuo Furukawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

Fisherface is a popular subspace algorithm used in face recognition, and is commonly believed superior to another technique, Eigenface, due to its attempt to maximize the separability of training classes. However, the obtained discriminating subspace of the training set may not easily extend to unseen classes (thus poor generalization), as in the case of enrollment of new subjects. In this paper, we reduce the performance variance and improve the generalization of Fisherface by automatically selecting some representative classes for training, using a recently proposed neural network architecture SOM2. The experiments on ORL face database validate the proposed method.

Keywords

Face Recognition Face Image Face Database Neural Network Architecture Training Class 
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 2006

Authors and Affiliations

  • Jiayan Jiang
    • 1
  • Liming Zhang
    • 1
  • Tetsuo Furukawa
    • 2
  1. 1.E.E. Dept. Fudan UniversityShanghaiChina
  2. 2.Kyushu Institute of TechnologyKitakyushuJapan

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