Advertisement

Face Recognition by Discriminant Analysis with Gabor Tensor Representation

  • Zhen Lei
  • Rufeng Chu
  • Ran He
  • Shengcai Liao
  • Stan Z. Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

This paper proposes a novel face recognition method based on discriminant analysis with Gabor tensor representation. Although the Gabor face representation has achieved great success in face recognition, its huge number of features often brings about the problem of curse of dimensionality. In this paper, we propose a 3rd-order Gabor tensor representation derived from a complete response set of Gabor filters across pixel locations and filter types. 2D discriminant analysis is then applied to unfolded tensors to extract three discriminative subspaces. The dimension reduction is done in such a way that most useful information is retained. The subspaces are finally integrated for classification. Experimental results on FERET database show promising results of the proposed method.

Keywords

discriminant analysis Gabor tensor representation face recognition 

References

  1. 1.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. PAMI 19(7), 711–720 (1997)Google Scholar
  2. 2.
    Chen, L., Liao, H., Ko, M., Lin, J., Yu, G.: A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognition (2000)Google Scholar
  3. 3.
    Ki-Chung, C., Cheol, K.S., Ryong, K.S.: Face recognition using principal component analysis of gabor filter responses. In: Proceedings of International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 53–57 (1999)Google Scholar
  4. 4.
    Kong, H., Li, X., Wang, L., Teoh, E.K., Wang, J.G., Venkateswarlu, R.: Generalized 2d principal component analysis. In: Proc. International Joint Conference on Neural Networks (2005)Google Scholar
  5. 5.
    Lades, M., Vorbruggen, J., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers 42, 300–311 (1993)CrossRefGoogle Scholar
  6. 6.
    Li, M., Yuan, B.: 2d-lda: A novel statistical linear discriminant analysis for image matrix. Pattern Recognition Letters 26(5), 527–532 (2005)CrossRefGoogle Scholar
  7. 7.
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition. Springer, New York (2005)zbMATHGoogle Scholar
  8. 8.
    Liu, C.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 572–581 (2004)CrossRefGoogle Scholar
  9. 9.
    Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  10. 10.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  11. 11.
    Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1999)CrossRefGoogle Scholar
  12. 12.
    Shen, L., Bai, L.: Gabor wavelets and kernel direct disciminant analysis for face recognition. In: ICPR 2004. Int’l Conf on Pattern Recognition, pp. 284–287 (2004)Google Scholar
  13. 13.
    Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. on PAMI 16(8), 831–836 (1996)Google Scholar
  14. 14.
    Wiskott, L., Fellous, J., Kruger, N., malsburg, C.V.: Face recognition by elastic bunch graph matching. IEEE Trans. PAMI 19(7), 775–779 (1997)Google Scholar
  15. 15.
    Xu, D., Yan, S.C., Zhang, L., Zhang, H.J., Liu, Z.K., Shum, H.Y.: Concurrent subspaces analysis. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 203–208. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  16. 16.
    Yan, S.C., Xu, D., Yang, Q., Zhang, L., Zhang, H.J.: Discriminant analysis with tensor representation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 526–532. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  17. 17.
    Yang, M.H.: Kernel eigenface vs. kernel fisherface: Face recognition using kernel methods. In: Proc. IEEE Int. Conf on Automatic Face and Gesture Recognition, IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  18. 18.
    Ye, J., Janardan, R., Li, Q.: Two-dimensional linear discriminant analysis. In: Proceedings of Neural Information Processing Systems (2004)Google Scholar
  19. 19.
    Yu, H., Yang, J.: A direct lda algorithm for high-dimensional data with application to face recognition. Pattern Recognition (2001)Google Scholar
  20. 20.
    Zhang, D., Chen, S., Zhou, Z.: Recognizing face or object from a single image: Linear vs. kernel methods on 2d patterns. In: S+SSPR 2006, in conjunction with ICPR 2006, HongKong, China (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zhen Lei
    • 1
  • Rufeng Chu
    • 1
  • Ran He
    • 1
  • Shengcai Liao
    • 1
  • Stan Z. Li
    • 1
  1. 1.Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun Donglu, Beijing 100080China

Personalised recommendations