Faceprint: Fusion of Local Features for 3D Face Recognition

  • Guangpeng Zhang
  • Yunhong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


3D face recognition is a very active biometric research field. Due to the 3D data’s insensitivity to illumination and pose variations, 3D face recognition has the potential to perform better than 2D face recognition. In this paper, we focus on local feature based 3D face recognition, and propose a novel Faceprint method. SIFT features are extracted from texture and range images and matched, the matching number of key points together with geodesic distance ratios between models are used as three kinds of matching scores, likelihood ratio based score level fusion is conducted to calculate the final matching score. Thanks to the robustness of SIFT, shape index, and geodesic distance against various changes of geometric transformation, illumination, pose and expression, the Faceprint method is inherently insensitive to these variations. Experimental results indicate that Faceprint method achieves consistently high performance comparing with commonly used SIFT on texture images.


3D face recognition local feature fusion 


  1. 1.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. ACM Computing Surveys, 399–458 (2003)Google Scholar
  2. 2.
    Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Computer Vision and Image Understanding 101(1), 1–15 (2006)Google Scholar
  3. 3.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)Google Scholar
  4. 4.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on pattern analysis and machine intelligence 19(7), 711 (1997)Google Scholar
  5. 5.
    Kanade, T.: Computer Recognition of Human Faces. Interdisciplinary Systems Research 47 (1977)Google Scholar
  6. 6.
    Wiskott, L., Fellous, J., Kruger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)Google Scholar
  7. 7.
    Husken, M., Brauckmann, M., Gehlen, S., von der Malsburg, C.: Strategies and benefits of fusion of 2D and 3D face recognition. In: IEEE Workshop on Face Recognition Grand Challenge Experiments (2005)Google Scholar
  8. 8.
    Chua, C., Han, F., Ho, Y.K.: 3D human face recognition using point signature. In: Proc. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 233–238 (2000)Google Scholar
  9. 9.
    Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of SIFT features for face authentication. In: Proc. IEEE International Conference on Computer Vision and Pattern Recognition Workshop, pp. 35–41 (2006)Google Scholar
  10. 10.
    Pentland, A., Moghaddam, B., Starner, T.: View-Based and modular eigenspaces for face recognition. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 84–91 (1994)Google Scholar
  11. 11.
    Huang, J., Heisele, B.: Blanz. V.: Component-based Face Recognition with 3D Morphable Models. In: Proc. of the 4th International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 27–34 (2003)Google Scholar
  12. 12.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. of the International Conference on Computer Vision 1999, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Mian, A.S., Bennamoun, M., Owens, R.A.: An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition. IEEE Transactions on pattern analysis and machine intelligence 29(11), 1927–1943 (2007)Google Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(4), 91–110 (2004)Google Scholar
  15. 15.
    Lindeberg, T.: Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics 21(2), 224–270 (1994)Google Scholar
  16. 16.
    Kisku, D.R., Rattani, A., Grosso, E., Tistarelli, M.: Face Identification by SIFT-based Complete Graph Topology. In: 5th IEEE Workshop on Automatic Identification Advanced Technologies, Alghero, Italy (2007)Google Scholar
  17. 17.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Three-dimensional face recognition. International Journal of Computer Vision 64(1), 5–30 (2005)Google Scholar
  18. 18.
    Kimmel, R., Sethian, J.A.: Computing geodesic on manifolds. Proc. US National Academy of Science 95, 8431–8435 (1998)Google Scholar
  19. 19.
    Sethian, J.A.: A review of the theory, algorithms, and applications of level set method for propagating surfaces. Acta numerica (1996)Google Scholar
  20. 20.
    Nandakumar, K., Chen, Y., Dass, S.C., Jain, A.K.: Likelihood Ratio Based Biometric Score Fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 342–347 (2008)Google Scholar
  21. 21.
    Ulery, B., Hicklin, A.R., Watson, C., Fellner, W., Hallinan, P.: Studies of Biometric Fusion. NIST, Tech. Rep. IR 7346. (2006)Google Scholar
  22. 22.
    Lu, X., Jain, A.K., Colbry, D.: Matching 2.5D Face Scans to 3D Models. IEEE Transactions on pattern analysis and machine intelligence 28(1), 31–43 (2006)Google Scholar
  23. 23.
    Kakadiaris, I.A., Passalis, G., Toderici, G., Murtuza, N., Lu, Y., Karampatziakis, N., Theoharis, T.: Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach. IEEE Transactions on pattern analysis and machine intelligence 29(4), 640–649 (2007)Google Scholar
  24. 24.
    Phillips, P.J., et al.: Overview of the Face Recognition Grand Challenge. In: Proc. Of IEEE Conf. on Computer Vision and Pattern Recognition, pp. I:947–954 (2005)Google Scholar
  25. 25.
    Dorai, C., Jain, A.K.: COSMOS - A Representation Scheme for 3D Free- Form Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(10), 1115–1130 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Guangpeng Zhang
    • 1
  • Yunhong Wang
    • 1
  1. 1.School of Computer Science and EngineeringBeihang UniversityChina

Personalised recommendations