Kernel Principal Component Analysis of Gabor Features for Palmprint Recognition

  • Murat Aykut
  • Murat Ekinci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


This paper presents Gabor-based kernel Principal Component Analysis (KPCA) method by integrating the Gabor wavelet and the KPCA methods for palmprint recognition. The intensity values of the palmprint images extracted by using an image preprocessing method are first normalized. Then Gabor wavelets are applied to derive desirable palmprint features. The transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. The KPCA method nonlinearly maps the Gabor wavelet image into a high-dimensional feature space and the matching is realized by weighted Euclidean distance. The proposed algorithm has been successfully tested on the PolyU palmprint database which the samples were collected in two different sessions. Experimental results show that this method achieves 97.22% accuracy for PolyU dataset using 3850 images from 385 different palms captured in the first session as train set and the second session im0061ges as test set.


Palmprint recognition Biometrics Gabor-wavelet Kernel PCA 


  1. 1.
    Zhang, D., Jing, X., Yang, J.: Biometric Image Discrimination Technologies. Computational Intelligence and Its Application Series. Idea Group Publishing (2006) Google Scholar
  2. 2.
    Zhang, D., Shu, W.: Two novel characteristics in palmprint verification: Datum point invariance and line feature matching. Pattern Recognition 32(4), 691–702 (1999) Google Scholar
  3. 3.
    Zhang, D., Kongi, W., You, J., Wong, M.: Online Palmprint Identification. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(9), 1041–1049 (2003) Google Scholar
  4. 4.
    Jing, X.Y., Zhang, D.: A Face and Palmprint Recognition Approach Based on Discriminant DCT Feature Extraction. IEEE Trans. on Systems, Man, and Cybernetics 34(6) (2004) Google Scholar
  5. 5.
    Kumar, A., Zhang, D.: Personal Recognition Using Hand Shape and Texture. IEEE Transactions on Image Processing 5(8), 2454–2460 (2006) Google Scholar
  6. 6.
    Wu, X., Zhang, D., Wang, K.: Fisherpalms Based Palmprint Recognition. Pattern Recognition Letters 24(15), 2829–2838 (2003) Google Scholar
  7. 7.
    Lu, G., Zhang, D., Wang, K.: Palmprint Recognition Using Eigenpalms Features. Pattern Recognition Letters 24(9-10), 1463–1467 (2003) Google Scholar
  8. 8.
    Liu, C.: Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition. IEEE Transactions on PAMI (5), 572–581 (2004) Google Scholar
  9. 9.
    Scholkopf, B., Smola, A.: Learning with Kernels: Support Vector Machine, Regularization, Optimization and Beyond. MIT Press, Cambridge (2002) Google Scholar
  10. 10.
    Ekinci, M., Aykut, M.: Gabor-Based Kernel PCA for Palmprint Recognition. IET Electronics Letters 43(20), 1077–1079 (2007) Google Scholar
  11. 11.
    Daugman, J.G.: Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile. Vision Research 20, 847–856 (1980) Google Scholar
  12. 12.
    Liu, D., Wechsler, H.: Independent Component Analysis of Gabor Features for Face Recognition. IEEE Transactions on Neural Networks 14(4), 919–928 (2003) Google Scholar
  13. 13.
    Weldon, T.P., Higgins, W.E., Dunn, D.F.: Efficient Gabor Filter Design for Texture Segmentation. Pattern Recognition 29(12), 2005–2015 (1996) Google Scholar
  14. 14.
    Manjunath, B.S., Ma, W.Y.: Texture Feature for Browing and Retrieval of Image Data. IEEE Transaction on Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996) Google Scholar
  15. 15.
    Lee, C.J., Wang, S.D.: Fingerprint Feature Extraction Using Gabor Filters. Electronic Letters 35(4), 288–290 (1999) Google Scholar
  16. 16.
    Zhu, Y., Tan, T., Wang, Y.: Biometric Personal Identification Based on Handwriting. In: IEEE Int. Conference on Pattern Recognition, vol. 2, pp. 797–800 (2000) Google Scholar
  17. 17.
    Daubechies, I.: Ten Lecture on Wavelets. Capital City Press, Philadelphia (1992) Google Scholar
  18. 18.
    Zhang, L., Zhang, D.: Characterization of Palmprints by Wavelet Signatures via Directional Context Modeling. IEEE Trans. on Systems, Man, and Cybernetics 34, 1335–1347 (2004) Google Scholar
  19. 19.
    Wang, Y., Ruan, Q.: Kernel Fisher Discriminant Analysis for Palmprint Recognition. In: The 18th International Conference on Pattern Recognition (ICPR 2006) (2006) Google Scholar
  20. 20.
    Microsoft Windows Platform SDK Windows XP SP2, Memory Management, © (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Murat Aykut
    • 1
  • Murat Ekinci
    • 1
  1. 1.Computer Vision & Pattern Recognition Lab. Department of Computer EngineeringKaradeniz Technical UniversityTrabzonTurkey

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