3D Face Recognition by Local Shape Difference Boosting

  • Yueming Wang
  • Xiaoou Tang
  • Jianzhuang Liu
  • Gang Pan
  • Rong Xiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)


A new approach, called Collective Shape Difference Classifier (CSDC), is proposed to improve the accuracy and computational efficiency of 3D face recognition. The CSDC learns the most discriminative local areas from the Pure Shape Difference Map (PSDM) and trains them as weak classifiers for assembling a collective strong classifier using the real-boosting approach. The PSDM is established between two 3D face models aligned by a posture normalization procedure based on facial features. The model alignment is self-dependent, which avoids registering the probe face against every different gallery face during the recognition, so that a high computational speed is obtained. The experiments, carried out on the FRGC v2 and BU-3DFE databases, yield rank-1 recognition rates better than 98%. Each recognition against a gallery with 1000 faces only needs about 3.05 seconds. These two experimental results together with the high performance recognition on partial faces demonstrate that our algorithm is not only effective but also efficient.


Face Recognition Depth Image Iterative Close Point Nose Bridge Gallery Face 
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.
    Medioni, G., Waupotitsch, R.: Face recognition and modeling in 3D. In: IEEE Int’l Workshop on AMFG (2003)Google Scholar
  2. 2.
    Chang, K., Bowyer, K., Flynn, P.: A Survey of Approaches and Challenges in 3D and Multi-Modal 2D+3D Face Recognition. Computer Vision and Image Understanding 101(1), 1–15 (2006)CrossRefGoogle Scholar
  3. 3.
    Chang, K.I., Bowyer, K., Flynn, P.J.: Adaptive Rigid Multi-Region Selection for Handling Expression Variation in 3D Face Recognition. In: IEEE Workshop on FRGC (2005)Google Scholar
  4. 4.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. on PAMI 14(2), 239–256 (1992)CrossRefGoogle Scholar
  5. 5.
    Russ, T.D., Koch, M.W., Little, C.Q.: A 2D range Hausdorff approach for 3D face recognition. In: IEEE Workshop on FRGC (2005)Google Scholar
  6. 6.
    Lu, X., Jain, A.K.: Deformation modeling for robust 3D face matching. In: IEEE Conf. on CVPR (2006)Google Scholar
  7. 7.
    Chua, C.S., Han, F., Ho, Y.K.: 3D Human Face Recognition Using Point Signature. In: Int’l Conf. on FG (2000)Google Scholar
  8. 8.
    Gordon, G.G.: Face recognition from depth maps and surface curvature. In: SPIE Conf. on Geometric Methods in Computer Vision (1991)Google Scholar
  9. 9.
    Wu, Y.J., Pan, G., Wu, Z.H.: Face Authentication based on Multiple Profiles Extracted from Range Data. In: Int’l Conf. on AVBPA (2003)Google Scholar
  10. 10.
    Wang, Y.M., Pan, G., Wu, Z.H.: 3D Face Recognition in the Presence of Expression: A Guidance-based Constraint Deformation Approach. In: IEEE Conf. on CVPR (2007)Google Scholar
  11. 11.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Three dimensional face recognition. Int’l Journal of Computer Vision 64(1), 5–30 (2005)CrossRefGoogle Scholar
  12. 12.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Annual Conf. on Computational Learning Theory (1998)Google Scholar
  13. 13.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: IEEE Conf. on CVPR (2005)Google Scholar
  14. 14.
    Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.: A 3D facial expression database for facial behavior research. In: Int’l Conf. on FG (2006)Google Scholar
  15. 15.
    Kakadiaris, I.A., Passalis, G., Toderici, G., et al.: Three-Dimensional Face recognition in the presence of facial expressions: An annotated deformable model approach. IEEE Trans. on PAMI 29(4), 640–649 (2007)CrossRefGoogle Scholar
  16. 16.
    Moreno, A.B., Sanchez, A., Velez, J.F., et al.: Face recognition using 3D surface-extracted descriptors. In: Irish Machine Vision and Image Processing Conference (2003)Google Scholar
  17. 17.
    Mian, A., Bennamoun, M., Owens, R.: An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face recognition. IEEE Trans. on PAMI 29(11), 1927–1943 (2007)CrossRefGoogle Scholar
  18. 18.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conf. on CVPR (2001)Google Scholar
  19. 19.
    Wang, X., Tang, X.: A unified framework for subspace face recognition. IEEE Trans. on PAMI 26(9), 1222–1228 (2004)CrossRefGoogle Scholar
  20. 20.
    Wang, X., Tang, X.: Unified subspace analysis for face recognition. In: IEEE International Conference on Computer Vision (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yueming Wang
    • 1
  • Xiaoou Tang
    • 1
  • Jianzhuang Liu
    • 1
  • Gang Pan
    • 2
  • Rong Xiao
    • 3
  1. 1.Dept. of Information EngineeringThe Chinese University of Hong KongHong Kong
  2. 2.College of Compute ScienceZhejiang UniversityChina
  3. 3.Microsoft Research AsiaChina

Personalised recommendations