Bayesian Face Revisited: A Joint Formulation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)


In this paper, we revisit the classical Bayesian face recognition method by Baback Moghaddam et al. and propose a new joint formulation. The classical Bayesian method models the appearance difference between two faces. We observe that this “difference” formulation may reduce the separability between classes. Instead, we model two faces jointly with an appropriate prior on the face representation. Our joint formulation leads to an EM-like model learning at the training time and an efficient, closed-formed computation at the test time. On extensive experimental evaluations, our method is superior to the classical Bayesian face and many other supervised approaches. Our method achieved 92.4% test accuracy on the challenging Labeled Face in Wild (LFW) dataset. Comparing with current best commercial system, we reduced the error rate by 10%.


Face Recognition Linear Discriminant Analysis Mahalanobis Distance Discriminative Information 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.


  1. 1.
    Moghaddam, B., Jebara, T., Pentland, A.: Bayesian face recognition. Pattern Recognition 33, 1771–1782 (2000)CrossRefGoogle Scholar
  2. 2.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. PAMI 22, 1090–1104 (2000)CrossRefGoogle Scholar
  3. 3.
    Wang, X., Tang, X.: A unified framework for subspace face recognition. PAMI 26, 1222–1228 (2004)CrossRefGoogle Scholar
  4. 4.
    Wang, X., Tang, X.: Subspace analysis using random mixture models. In: CVPR (2005)Google Scholar
  5. 5.
    Wang, X., Tang, X.: Bayesian face recognition using gabor features, pp. 70–73 (2003)Google Scholar
  6. 6.
    Li, Z., Tang, X.: Bayesian face recognition using support vector machine and face clustering. In: CVPR (2004)Google Scholar
  7. 7.
    Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification, vol. 10, pp. 207–244 (2005)Google Scholar
  8. 8.
    Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: ICML (2007)Google Scholar
  9. 9.
    Guillaumin, M., Verbeek, J.J., Schmid, C.: Is that you? metric learning approaches for face identification. In: ICCV (2009)Google Scholar
  10. 10.
    Ying, Y., Li, P.: Distance metric learning with eigenvalue optimization. Journal of Machine Learning Research 13, 1–26 (2012)Google Scholar
  11. 11.
    Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: ICCV (2009)Google Scholar
  12. 12.
    Taigman, Y., Wolf, L., Hassner, T.: Multiple one-shots for utilizing class label information. In: BMVC (2009)Google Scholar
  13. 13.
    Yin, Q., Tang, X., Sun, J.: An associate-predict model for face recognition. In: CVPR (2011)Google Scholar
  14. 14.
    Zhu, C., Wen, F., Sun, J.: A rank-order distance based clustering algorithm for face tagging. In: CVPR (2011)Google Scholar
  15. 15.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E., Hanson, A.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. In: ECCV (2008)Google Scholar
  16. 16.
    Taigman, Y., Wolf, L.: Leveraging billions of faces to overcome performance barriers in unconstrained face recognition. Arxiv preprint arXiv:1108.1122 (2011)Google Scholar
  17. 17.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. PAMI 19, 711–720 (1997)CrossRefGoogle Scholar
  18. 18.
    Ioffe, S.: Probabilistic Linear Discriminant Analysis. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part IV. LNCS, vol. 3954, pp. 531–542. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Prince, S., Li, P., Fu, Y., Mohammed, U., Elder, J.: Probabilistic models for inference about identity. PAMI 34, 144–157 (2012)CrossRefGoogle Scholar
  20. 20.
    Susskind, J., Memisevic, R., Hinton, G., Pollefeys, M.: Modeling the joint density of two images under a variety of transformations. In: CVPR (2011)Google Scholar
  21. 21.
    Ramanan, D., Baker, S.: Local distance functions: A taxonomy, new algorithms, and an evaluation. In: ICCV (2009)Google Scholar
  22. 22.
    Nguyen, H.V., Bai, L.: Cosine Similarity Metric Learning for Face Verification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part II. LNCS, vol. 6493, pp. 709–720. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Liang, L., Xiao, R., Wen, F., Sun, J.: Face Alignment Via Component-Based Discriminative Search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 72–85. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24, 971–987 (2002)CrossRefGoogle Scholar
  25. 25.
    Cao, Z., Yin, Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. In: CVPR (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.University of Science and Technology of ChinaChina
  2. 2.The Chinese University of Hong KongHong Kong
  3. 3.Microsoft Research AsiaBeijingChina

Personalised recommendations