A Novel 2D Gabor Wavelets Window Method for Face Recognition

  • Lin Wang
  • Yongping Li
  • Hongzhou Zhang
  • Chengbo Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


This paper proposed a novel algorithm named 2D Gabor Wavelets Window (GWW) method. The GWW scans the image top left to bottom right to extract the local feature vectors (LFVs). A parametric feature vector is derived by downsampling and concatenating these LFVs for face representation and recognition. Compared with the Gabor Wavelets representation of the whole image, the total cost is reduced by maximum of 39% whilst the performance achieved better than the conventional PCA method when experimented on both the ORL and XM2VTSDB databases without any preprocessing.


Face Recognition Near Neighbor Gabor Wavelet Kernel Principal Component Analysis Gabor Feature 
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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  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Liu, C.J., Wechesler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans. On Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  3. 3.
    Liu, C.J.: Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition. IEEE Trans. on PAMI 26(5) (2004)Google Scholar
  4. 4.
    Qin, J., He, Z.S.: A SVM Face Recognition Method Based on Gabor-Featured Key Points. In: Proc. 4th IEEE Conf. on Machine Learning and Cybernetics, pp. 5144–5149 (2005)Google Scholar
  5. 5.
    Kalocsai, P., von der Malsburg, C., et al.: Face recognition by statistical analysis of feature detectors. Image and Vision Computing 14(4), 273–278 (2000)CrossRefGoogle Scholar
  6. 6.
    Hamamoto, Y., Uchimura, S., et al.: A Gabor Filter-Based Method for Recognizing Handwritten Numerals. Pattern Recognition 31(4), 395–400 (1998)CrossRefGoogle Scholar
  7. 7.
    Dailey, M., Cottell, G.: PCA=Gabor for Expression Recognition. UCSD Computer Science and Engineering Technical Report CS-629 (1999)Google Scholar
  8. 8.
    Alterson, R., Spetsakis, M.: Object recognition with adaptive Gabor features. Image and Vision Computing 22, 1007–1014 (2004)CrossRefGoogle Scholar
  9. 9.
    Zhu, J.K., Vai, M.I., et al.: Face Recognition Using 2D DCT with PCA. In: The 4th Chinese Conf. on Biometric Recognition (Sinobiometrics 2003), December 7-8 (2003)Google Scholar
  10. 10.
    Chien, J.T., Wu, C.C.: Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition. IEEE Trans. on PAMI 24(12) (2002)Google Scholar
  11. 11.
    Messer, K., Matas, J., et al.: XM2VTSDB: The extended M2VTS database. In: Proceeding of AVBPA 1999, pp. 72–77 (1999)Google Scholar
  12. 12.
    Jonsson, K., Kittler, J., et al.: Support Vector Machines for Face Authentication. In: Proceeding of BMVC 1999, pp. 543–553 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lin Wang
    • 1
  • Yongping Li
    • 1
  • Hongzhou Zhang
    • 1
  • Chengbo Wang
    • 1
  1. 1.Shanghai Institute of Applied PhysicsChinese Academy of SciencesShanghaiChina

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