3D Face Matching Using the Surface Interpenetration Measure

  • Olga R. P. Bellon
  • Luciano Silva
  • Chauã C. Queirolo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


3D face recognition has gained growing attention in the last years, mainly because both the limitations of 2D images and the advances in 3D imaging sensors. This paper proposes a novel approach to perform 3D face matching by using a new metric, called the Surface Interpenetration Measure (SIM). The experimental results include a comparison with a state-of-art work presented in the literature and show that the SIM is very discriminatory as confronted with other metrics. The experiments were performed using two different databases and the obtained results were quite similar, showing the robustness of our approach.


Root Mean Square Error Facial Expression Face Recognition Range Image Face Match 
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 35, 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Achermann, B., Jiang, X., Bunke, H.: Face recognition using range images. In: Intl. Conf. on Virtual Systems and Multimedia, pp. 129–136 (1997)Google Scholar
  3. 3.
    Lee, J.C., Milios, E.: Matching range images of human faces. In: Intl. Conf. on Computer Vision, pp. 722–726 (1990)Google Scholar
  4. 4.
    Negamine, T., Uemura, T., Masuda, I.: 3d facial image analysis for human identification. In: Prof. of ICPR, pp. 324–327 (1992)Google Scholar
  5. 5.
    Brunelli, R., Falavigna, D.: Person identification using multiple cues. IEEE PAMI 17, 955–966 (1995)Google Scholar
  6. 6.
    Bowyer, K.W., Chang, K.I., Flynn, P.J.: A survey of approaches to three-dimensional face recognition. In: Proc. of ICPR, pp. 358–361 (2004)Google Scholar
  7. 7.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE PAMI 14, 239–256 (1992)Google Scholar
  8. 8.
    Horn, B.K.P.: Extended gaussian images. In: DARP, vol. 84, pp. 72–89 (1984)Google Scholar
  9. 9.
    Martinez, A.M., Kak, A.C.: Pca versus lda. IEEE PAMI 23, 228–223 (2001)Google Scholar
  10. 10.
    Lu, X., Colbry, D., Jain, A.K.: Three-dimensional model based face recognition. In: Proc. of ICPR, pp. 362–366 (2004)Google Scholar
  11. 11.
    Cook, J., Chandran, V., Sridharan, S., Fookes, C.: Face recognition from 3d data using iterative closest point algorithm and gaussian mixture models. In: 3D Data Processing, Visualization, and Transmission, pp. 502–509 (2004)Google Scholar
  12. 12.
    Tanaka, H.T., Ikeda, M.: Curvature-based face surface recognition using spherical correlation - principal directions for curved object recognition. In: ICPR, vol. 3, pp. 638–642 (1996)Google Scholar
  13. 13.
    Chang, K.I., Bowyer, K.W., Flynn, P.J.: An evaluation of multimodal 2d+3d face biometrics. IEEE PAMI 27, 619–624 (2005) (to appear)Google Scholar
  14. 14.
    Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multi-modal 2d and 3d biometrics for face recognition. In: Intl. Workshop on Analysis and Modeling of Faces and Gestures, pp. 187–194 (2003)Google Scholar
  15. 15.
    Silva, L., Bellon, O.R.P., Boyer, K.L.: Robust Range Image Registration Using Genetic Algorithms and the Surface Interpenetration Measure. Machine Perception and Artificial Intelligence, vol. 60. World Scientific Publishing, Singapore (2005)zbMATHGoogle Scholar
  16. 16.
    Silva, L., Bellon, O.R.P., Boyer, K.L.: Robust range image registration using the surface interpenetration measure and enhanced genetic algorithms. IEEE PAMI 27, 762–776 (2005)Google Scholar
  17. 17.
    Silva, L., Bellon, O.R.P., Gotardo, P.F.U., Boyer, K.L.: Range image registration using enhanced genetic algorithms. In: IEEE International Conference on Image Processing, vol. 2, pp. 711–714 (2003)Google Scholar
  18. 18.
    Silva, L., Bellon, O.R.P., Boyer, K.L.: Robust multiview range image registration. In: Proc. of 17th Brazilian Symposium on Computer Graphics and Image Processing, pp. 80–88 (2003)Google Scholar
  19. 19.
    Man, K.F., Tang, K.S., Kwong, S.: Genetic algorithms: concepts and applications. IEEE Trans. on Industrial Eletronics 43, 519–534 (1996)CrossRefGoogle Scholar
  20. 20.
    Dorai, C., Jain, A.K.: COSMOS - a representation scheme for 3d free-form objects. IEEE PAMI 19, 1115–1130 (1997)Google Scholar
  21. 21.
    Colbry, D., Lu, X., Jain, A.K., Stockman, G.: 3d face feature extraction for recognition. Technical Report 4-39, MSU-CSE (2004)Google Scholar
  22. 22.
    Lu, X., Jain, A.K.: Integrating range and texture information for 3d face recognition. In: IEEE Workshop on Applications of Computer Vision, pp. 156–163 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Olga R. P. Bellon
    • 1
  • Luciano Silva
    • 1
  • Chauã C. Queirolo
    • 1
  1. 1.Departamento de Informática, IMAGO Research GroupUniversidade Federal do ParanáCuritibaBrasil

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