Face Recognition with Local Gabor Textons

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

Abstract

This paper proposes a novel face representation and recognition method based on local Gabor textons. Textons, defined as a vocabulary of local characteristic features, are a good description of the perceptually distinguishable micro-structures on objects. In this paper, we incorporate the advantages of Gabor feature and textons strategy together to form Gabor textons. And for the specificity of face images, we propose local Gabor textons (LGT) to portray faces more precisely and efficiently. The local Gabor textons histogram sequence is then utilized for face representation and a weighted histogram sequence matching mechanism is introduced for face recognition. Preliminary experiments on FERET database show promising results of the proposed method.

Keywords

local textons Gabor filters histogram sequence face recognition 

References

  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Proceedings of the European Conference on Computer Vision, Prague, Czech, pp. 469–481 (2004)Google Scholar
  2. 2.
    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
  3. 3.
    Comon, P.: Independent component analysis - a new concept? Signal Processing 36, 287–314 (1994)MATHCrossRefGoogle Scholar
  4. 4.
    Cula, O., Dana, K.: Compact representation of bidirectional texture functions. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1041–1047. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  5. 5.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2000)Google Scholar
  6. 6.
    Julesz, B.: Texton, the elements of texture perception, and their interactions 290(5802), 91–97 (March 1981)Google Scholar
  7. 7.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)MATHCrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Moghaddam, B., Jebara, T., Pentland, A.: Bayesian face recognition. Pattern Recognition 33(11), 1771–1782 (2000)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.
    Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  12. 12.
    Varma, M., Zisserman, A.: Classifying images of materials: achieving viewpoint and illumination independence. In: Proceedings of the European Conference on Computer Vision, pp. 255–271 (2002)Google Scholar
  13. 13.
    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
  14. 14.
    Zhang, W.C., Shan, S.G., Gao, W., Zhang, H.M.: Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: Proceedings of IEEE International Conference on Computer Vision, pp. 786–791. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zhen Lei
    • 1
  • Stan Z. Li
    • 1
  • Rufeng Chu
    • 1
  • Xiangxin Zhu
    • 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

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