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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Matthew, A.T., Alex, P.P.: Face Recognition Using Eigenfaces. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  2. 2.
    Peter, N.B., Joao, P.H., David, J.K.: Eigenface vs. Fisherface: Recognition Using Class Specific Linear Projection. IEEE Trans. on Pattern Anal. Machine Intell. 19, 711–720 (1997)CrossRefGoogle Scholar
  3. 3.
    Aleix, M.M., Avinash, C.K.: PCA versus LDA. IEEE Trans. on Pattern Anal. Machine Intell. 23, 228–233 (2001)CrossRefGoogle Scholar
  4. 4.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Trans. on Pattern Anal. Machine Intell. 22, 1090–1104 (2000)CrossRefGoogle Scholar
  5. 5.
    Bo, C., Shiguang, S., Xiaohua, Z., Wen, G.: Baseline Evaluations on the CAS-PEAL-R1 Face Database. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 370–378. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Tetsuo, F.: SOM of SOMs: Self-organizing Map Which Maps a Group of Self-organizing Maps. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 391–396. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Tetsuo, F.: SOM2 As “SOM of SOMs”. In: Proc. WSOM (2005)Google Scholar
  8. 8.
    Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: “Neural-Gas” Network for Vector Quantization and its Application to Time-Series Prediction. IEEE Trans. on Neural Networks 4, 558–569 (1993)CrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001)MATHGoogle Scholar
  10. 10.
    Phillips, P.J., Flynn, P.J., Scruggs, T., et al.: Overview of the Face Recognition Grand Challenge. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 947–954 (2005)Google Scholar

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

Personalised recommendations