Learning Effective Intrinsic Features to Boost 3D-Based Face Recognition

  • Chenghua Xu
  • Tieniu Tan
  • Stan Li
  • Yunhong Wang
  • Cheng Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)


3D image data provide several advantages than 2D data for face recognition and overcome many problems with 2D intensity images based methods. In this paper, we propose a novel approach to 3D-based face recognition. First, a novel representation, called intrinsic features, is presented to encode local 3D shapes. It describes complementary non-relational features to provide an intrinsic representation of faces. This representation is extracted after alignment, and is invariant to translation, rotation and scale. Without reduction, tens of thousands of intrinsic features can be produced for a face, but not all of them are useful and equally important. Therefore, in the second part of the work, we introduce a learning method for learning most effective local features and combining them into a strong classifier using an AdaBoost learning procedure. Experimental results are performed on a large 3D face database obtained with complex illumination, pose and expression variations. The results demonstrate that the proposed approach produces consistently better results than existing methods.


Face Recognition Intrinsic Feature Range Image Iterative Close Point Mesh Node 
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.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys archive 35(4), 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Lee, J.C., Milios, E.: Matching Range Images of Human Faces. In: Proc. ICCV 1990, pp. 722–726 (1990)Google Scholar
  3. 3.
    Gordon, G.G.: Face Recognition Based on Depth and Curvature Features. In: Proc. CVPR 1992, pp. 108–110 (1992)Google Scholar
  4. 4.
    Yacoob, Y., Davis, L.S.: Labeling of Human Face Components from Range Data. CVGIP: Image Understanding 60(2), 168–178 (1994)CrossRefGoogle Scholar
  5. 5.
    Chua, C.S., Han, F., Ho, Y.K.: 3D Human Face Recognition Using Point Signiture. In: Proc. FG 2000, pp. 233–239 (2000)Google Scholar
  6. 6.
    Beumier, C., Acheroy, M.: Automatic 3D Face Authentication. Image and Vision Computing 18(4), 315–321 (2000)CrossRefzbMATHGoogle Scholar
  7. 7.
    Tanaka, H.T., Ikeda, M., Chiaki, H.: Curvature-based Face Surface Recognition Using Spherical Correlation. In: Proc. FG 1998, pp. 372–377 (1998)Google Scholar
  8. 8.
    Hesher, C., Srivastava, A., Erlebacher, G.: A Novel Technique for Face Recognition Using Range Imaging. In: Inter. Multiconference in Computer Science (2002)Google Scholar
  9. 9.
    Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Trans. on PAMI 25(9), 1063–1074 (2003)CrossRefGoogle Scholar
  10. 10.
    Dorai, C., Jain, A.K.: COSMOS-A Representation Scheme for 3-D Free-Form Objects. IEEE Trans. on PAMI 19(10), 1115–1130 (1997)CrossRefGoogle Scholar
  11. 11.
    Lu, X., Colbry, D., Jain, A.K.: Three-dimensional Model Based Face Recognition. In: Proc. ICPR 2004, pp. 362–365 (2004)Google Scholar
  12. 12.
    Lee, M.W., Ranganath, S.: Pose-invariant Face Recognition Using a 3D Deformable Model. Pattern Recognition 36, 1835–1846 (2003)CrossRefGoogle Scholar
  13. 13.
    Wang, Y., Chua, C., Ho, Y.: Facial Feature Detection and Face Recognition from 2D and 3D Images. Pattern Recognition Letters 23, 1191–1202 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Chang, K.I., Bowyer, K.W., Flynn, P.J.: An Evaluation of Multi-model 2D+3D Biometrics. IEEE Trans. on PAMI 27(4), 619–624 (2005)CrossRefGoogle Scholar
  15. 15.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Expression-Invariant 3D Face Recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 62–70. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision Templates for Multiple Classifier Fusion: an Experimental Comparon. Patter Recognition 34, 299–314 (2001)CrossRefzbMATHGoogle Scholar
  17. 17.
    Xu, C., Wang, Y., Tan, T., Quan, L.: Automatic 3D Face Recognition Combining Global Geometric Features with Local Shape Variation Information. In: Proc. FG 2004, pp. 308–313 (2004)Google Scholar
  18. 18.
    Xu, C., Wang, Y., Tan, T., Quan, L.: Robust Nose Detection in 3D Facial Data Using Local Characteristics. In: Proc. ICIP 2004, pp. 1995–1998 (2004)Google Scholar
  19. 19.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The Feret Evaluation Methodology for Face-Recognition Algorithm. IEEE Trans. on PAMI 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  20. 20.
    Shen, J., Shen, W., Shen, D.: On Geometric and Orthogonal Moments. Inter. Journal of Pattern Recognition and Artificial Intelligence 14(7), 875–894 (2000)CrossRefzbMATHGoogle Scholar
  21. 21.
    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
  22. 22.
    Viola, P., Jones, M.: Robust Real-time Object Detection. In: Proc. 2nd Inter. Workshop on Statistical Computional Theories of Vision (2001)Google Scholar
  23. 23.
    Moghaddam, B., Pentland, A.: Beyond Euclidean Eigenspaces: Bayesian Matching for Vision recognition. Face Recognition: From Theories to Applications, 921 (1998), ISBN 3-540-64410-5Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chenghua Xu
    • 1
  • Tieniu Tan
    • 1
  • Stan Li
    • 1
  • Yunhong Wang
    • 2
  • Cheng Zhong
    • 1
  1. 1.Center for Biometrics and Security Research (CBSR), & National Laboratory of Pattern Recognition (NLPR), Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.School of Computer Science and EngineeringBeihang UniversityBeijingChina

Personalised recommendations