Robust Face Recognition Using Color Information

  • Zhiming Liu
  • Chengjun Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


This paper presents a robust face recognition method using color information with the following three-fold contributions. First, a novel hybrid color space, the RC r Q color space, is constructed out of three different color spaces: the RGB, YC b C r , and YIQ color spaces. The RC r Q hybrid color space, whose component images possess complementary characteristics, enhances the discriminating power for face recognition. Second, three effective image encoding methods are proposed for the component images in the RC r Q hybrid color space: (i) a patch-based Gabor image representation for the R component image, (ii) a multi-resolution LBP feature fusion scheme for the C r component image, and (iii) a component-based DCT multiple face encoding for the Q component image. Finally, at the decision level, the similarity matrices generated using the three component images in the RC r Q hybrid color space are fused using a weighted sum rule. The most challenging Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 shows that the proposed method, which achieves the face verification rate of 92.43% at the false accept rate of 0.1%, performs better than the state-of-the-art face recognition methods.


Face Recognition Color Space Face Image Local Binary Pattern Component Image 
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.


  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 13, 71–86 (1991)Google Scholar
  2. 2.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 711–720 (1997)Google Scholar
  3. 3.
    Su, Y., Shan, S., Chen, X., Gao, W.: Hierarchical ensemble of global and local classifiers for face recognition. In: Proc. IEEE International Conference on Computer Vision (ICCV 2007) (2007)Google Scholar
  4. 4.
    Tan, X., Triggs, B.: Fusing gabor and lbp feature sets for kernel-based face recognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 235–249. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans. on Image Processing 11, 467–476 (2002)Google Scholar
  6. 6.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 28 (2006)Google Scholar
  7. 7.
    Kittler, J., Hatef, M., Robert, P., Matas, J.: On combining classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 20, 226–239 (1998)Google Scholar
  8. 8.
    Liu, C., Wechsler, H.: Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. on Image Processing 9, 132–137 (2000)Google Scholar
  9. 9.
    Gonzalez, R., Woods, R.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  10. 10.
    Shih, P., Liu, C.: Comparative assessment of content-based face image retrieval in different color spaces. International Journal of Pattern Recognition and Artificial Intelligence 19, 873–893 (2005)Google Scholar
  11. 11.
    Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional cortical filters. J. Optical Soc. Am. 2, 1160–1169 (1985)Google Scholar
  12. 12.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 971–987 (2002)Google Scholar
  13. 13.
    Kim, T., Kim, H., Hwang, W., Kittler, J.: Component-based lda face description for image retrieval and mpeg-7 standardisation. Image and Vision Computing 23, 631–642 (2005)Google Scholar
  14. 14.
    Heisele, B., Serre, T., Poggio, T.: A component-based framework for face recognition and identification. International Journal of Computer Vision 74, 167–181 (2007)Google Scholar
  15. 15.
    Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  16. 16.
    Kumar, V., Savvides, M., Xie, C.: Correlation pattern recognition for face recognition. Proceesings of the IEEE 94, 1963–1976 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zhiming Liu
    • 1
  • Chengjun Liu
    • 1
  1. 1.Department of Computer ScienceNew Jersey Institute of TechnologyNewark, New JerseyUSA

Personalised recommendations