Automatic Facial Pose Determination of 3D Range Data for Face Model and Expression Identification

  • Xiaozhou Wei
  • Peter Longo
  • Lijun Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


Many of the contemporary 3D facial recognition and facial expression recognition algorithms depend on locating primary facial features, such as the eyes, nose, or lips. Others are dependent on determining the pose of the face. We propose a novel method for limiting the search space needed to find these “interesting features.” We then show that our algorithm can be used in conjunction with surface labeling to robustly determine the pose of a face. Our approach does not require any type of training. It is pose-invariant and can be applied to both manually cropped models and raw range data, which can include the neck, ears, shoulders, and other noise. We applied the proposed algorithm to our created 3D range model database, the experiments show the promising results to classify individual faces and individual facial expressions.


Surface Normal Difference Facial Pose Detection 3D range model 


  1. 1.
    Dorai, C., Jain, A.: COSMO-A representation scheme for 3D free-form objects. IEEE Trans. on PAMI 19(10), 1115–1130 (1997)Google Scholar
  2. 2.
    Hattori, K., Sato, Y.: Estimating pose of human face based on symmetry plane using range and intensity images. In: ICPR 1998 (1998)Google Scholar
  3. 3.
    Rajwade, A., Levine, M.D.: Facial Pose from 3D Data. Journal of Image and Vision Computing 2007 (to appear)Google Scholar
  4. 4.
    Razdan, A., Bae, M.: Curvature estimation scheme for triangle meshes using biquadratic Bezier patches. Computer-Aided Design 37(14) (2000)Google Scholar
  5. 5.
    Srinivasan, S., Boyer, K.L.: Head pose estimation using view based eigenspaces. In: ICPR 2002 (2002)Google Scholar
  6. 6.
    Tanaka, H.T., Ikeda, M.: Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition. In: ICPR 1996, pp. 25–29 (1996)Google Scholar
  7. 7.
    Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.: A 3D facial expression database for facial behavior research. In: IEEE FGR 2006, Southampton, UK, pp. 211–216 (2006)Google Scholar
  8. 8.
    Bowyer, K., Chang, K., Flynn, P.: A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. CVIU 101(1), 1–15 (2006)Google Scholar
  9. 9.
    Bronstein, A., Bronstein, M., Kimmel, R.: Three dimensional face recognition. IJCV 5(30) (2005)Google Scholar
  10. 10.
    Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Trans. on PAMI 25(9) (2003)Google Scholar
  11. 11.
    Chang, Y., Vieira, M., Turk, M., Velho, L.: Automatic 3D facial expression analysis in videos. In: IEEE ICCV 2005 Workshop on Analysis and Modeling of Faces and Gestures, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  12. 12.
    Cohen, I., Sebe, N., Garg, A., Chen, L., Huang, T.: Facial expression recognition from video sequences: temporal and static modeling. CVIU 91(1) (2003)Google Scholar
  13. 13.
    Gross, R., et al.: Quo vadis face recognition? In: Workshop on empirical evaluation methods in computer vision (2001)Google Scholar
  14. 14.
    Li, S., Jain, A.: Handbook of face recognition. Springer, New York (2004)Google Scholar
  15. 15.
    Pantic, M., et al.: Automatic analysis of facial expressions: the state of the art. IEEE Trans. PAMI 22(12) (2000)Google Scholar
  16. 16.
    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: IEEE CVPR, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  17. 17.
    Sun, Y., Yin, L.: 3d face recognition using two views face modeling and labeling. In: CVPR 2005 Workshop on A3DISS (2005)Google Scholar
  18. 18.
    Tang, X., Li, Z.: Video based face recognition using multiple classifiers. In: IEEE FGR 2004, IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  19. 19.
    Lu, X., Jain, A., et al.: Matching 2.5D face scans to 3D models. IEEE Trans. PAMI 28(1), 31–43 (2006)Google Scholar
  20. 20.
    Yin, L., Wei, X., Longo, P., Bhuvanesh, A.: Analyzing facial expressions using intensity-variant 3d data for human computer interaction. In: ICPR 2006, Hong Kong (2006)Google Scholar
  21. 21.
    Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4) (December 2003)Google Scholar
  22. 22.
    Kittler, J., Hilton, A., et al.: 3D Assisted Face Recognition: A Survey of 3D Imaging, Modelling and Recognition Approaches. In: CVPR 2005 Workshops on A3DISS (2005)Google Scholar
  23. 23.
    Wang, Y., Samaras, D., Metaxas, D., Elgammal, A., et al.: High resolution acquisition, learning, transfer of dynamic 3D face expression. In: Eurographics (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xiaozhou Wei
    • 1
  • Peter Longo
    • 1
  • Lijun Yin
    • 1
  1. 1.Department of Computer Science, State University of New York at Binghamton, Binghamton, NY 

Personalised recommendations