A new unsupervised approach to face recognition is proposed in this paper. Shape and color entropy is presented to descript face features. Firstly, images are pre-processed including face normalization and image segmentation and so on. Secondly, by using the information entropy theory, the method defines the color and shape entropy of the face images, respectively. Finally, an integrated similarity measurement framework is presented by computing mutual information between images according to these entropies. Compared with other methods of feature description, experiments indicate that this approach is more effective and efficient.


mutual information face recognition image similarity feature description 


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  1. 1.
    Andrea, F.A., Michele, N., Daniel, R., Gabriele, S.: 2D and 3D face recognition: A survey. Pattern Recognition Letters 28, 1885–1906 (2007)CrossRefGoogle Scholar
  2. 2.
    Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. J. Cognit. Neurosci. 3(1), 71–96 (1991)CrossRefGoogle Scholar
  3. 3.
    Moghaddam, B.: Principal manifolds and probabilistic subspaces for visual recognition. IEEE Trans. Pattern Anal. Machine Intell. 24(6), 780–788 (2002)CrossRefGoogle Scholar
  4. 4.
    Belhumeur, P.N., Hespanha, J., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  5. 5.
    Liu, C., Wechsler, H.: A unified Bayesian framework for face recognition. In: Proc. Internat. Conf. on Image Processing (ICIP 1998), pp. 151–155 (1998)Google Scholar
  6. 6.
    Sung, K.K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Machine Intell. 20(1), 39–51 (1998)CrossRefGoogle Scholar
  7. 7.
    Tefas, A., Kotropoulos, C., Pitas, I.: Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans. Pattern Anal. Machine Intell. 23(7), 735–746 (2001)CrossRefGoogle Scholar
  8. 8.
    Johnny, K.C., Ng., Z.Y.Z., Yang, S.Q.: A comparative study of Minimax Probability Machine-based approaches for face recognition. Pattern Recognition Letters 28, 1995–2002 (2007)CrossRefGoogle Scholar
  9. 9.
    Lu, X.S., Zhang, S., Su, H., Chen, Y.Z.: Mutual information-based multimodal image registration using a novel joint histogram estimation. Computerized Medical Imaging and Graphics 32(3), 202–209 (2008)CrossRefGoogle Scholar
  10. 10.
    Suyash, P.A., Tolga, T., Norman, F., Ross, T.W.: Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification. Medical Image Analysis 10(5), 726–739 (2006)CrossRefGoogle Scholar
  11. 11.
    Viola, P., Wells, W.: Alignment by maximization of mutual information. In: Proceedings of the 5th International Conference on Computer Vision, Boston, MA, pp. 16–23 (1995)Google Scholar
  12. 12.
    Collignon, A., Maes, F., Vandermeulen, D., et al.: Automated multimodality image registration using information theory. In: Proceedings of the Information Processing in Medical Imaging Conference, Dordrecht, pp. 263–274 (1995)Google Scholar
  13. 13.
    Fan, Z.Z., Zhou, S.C.: Image Retrieval Based on Shape Entropy. Journal of Computer Application & Research 24(9), 309–311 (2007) (in Chinese)Google Scholar
  14. 14.
  15. 15.
  16. 16.
    Kwaka, K.C., Witold, P.: Face Recognition Using a Fuzzy Fisherface Classifier. Pattern Recognition 38, 1717–1732 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zizhu Fan
    • 1
  • Ergen Liu
    • 1
  1. 1.School of Natural ScienceEast China Jiaotong UniversityNanchangChina

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