Shape Matching Based on Fully Automatic Face Detection on Triangular Meshes

  • Wolfram von Funck
  • Holger Theisel
  • Hans-Peter Seidel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)


This paper tackles a particular shape matching problem: given a data base of shapes (described as triangular meshes), we search for all shapes which describe a human. We do so by applying a 3D face detection approach on the mesh which consists of three steps: first, a local symmetry value is computed for each vertex. Then, the symmetry values in a certain neighborhood of each vertex are analyzed for building sharp symmetry lines. Finally, the geometry around each vertex is analyzed to get further facial features like nose and forehead. We tested our approach with several shape data bases (e.g. the Princeton Shape Benchmark) and achieved high rates of correct face detection.


Face Recognition Face Detection Triangular Mesh Shape Match Symmetry Detection 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, S.: A search engine for 3D models. ACM Transactiond on Graphics 22(1), 83–105 (2003)CrossRefGoogle Scholar
  2. 2.
    Ankerst, M., Kastenmüller, G., Kriegel, H.P., Seidl, T.: Nearest neighbor classification in 3D protein data bases. In: Proc. 7th International Conference on Intelligent Systems for Molecular Biology, pp. 34–43 (1999)Google Scholar
  3. 3.
    Kang, S., Ikeuchi, K.: Determining 3-D object pose using the complex extended gaussian image. In: IEEE Conf. on Comp. Vision and Patt. Recog., pp. 580–585 (1991)Google Scholar
  4. 4.
    Saupe, D., Vranic, D.V.: 3D model retrieval with spherical harmonics and moments. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, p. 392. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Vranic, D.: An improvement of rotation invariant 3D shape descriptor based on functions on concentric spheres. In: Proc. IEEE International Conference on Image Processing (ICIP 2003), pp. 757–760 (2003)Google Scholar
  6. 6.
    Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Trans. Graph. 21(4), 807–832 (2002)CrossRefGoogle Scholar
  7. 7.
    Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. In: SGP 2003: Proceedings of the Eurographics/ACM SIGGRAPH symposium on Geometry processing, pp. 156–164 (2003)Google Scholar
  8. 8.
    Chen, D.-Y., Tian, X.-P., Shen, Y.-t., Ouhyoung, M.: On visual similarity based 3d model retrieval 23(3), 223–232 (2003)Google Scholar
  9. 9.
    Vranic, D., Saupe, D.: 3D shape descriptor based on 3D fourier transform. In: Proc. ECMCS 2001, pp. 271–274 (2001)Google Scholar
  10. 10.
    Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.: Topology matching for fully automatic similarity estimation of 3D shapes. In: Proc. SIGGRAPH, pp. 203–212 (2001)Google Scholar
  11. 11.
    Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Shape matching and anisotropy. In: Proc. SIGGRAPH (2004), pp. 623–629 (2004)Google Scholar
  12. 12.
    Funkhouser, T., Kazhdan, M., Shilane, P., Min, P., Kiefer, W., Tal, A., Rusinkiewicz, S., Dobkin, D.: Modeling by example. In: ACM Transactions on Graphics (SIGGRAPH 2004) (2004)Google Scholar
  13. 13.
    Yang, G., Huang, T.: Human face detection in a complex background. Pattern Recognition 27(1), 53–63 (1994)CrossRefGoogle Scholar
  14. 14.
    Kotropoulos, C., Pitas, I.: Rule-based face detection in frontal views. In: ICASSP 1997: Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1997), vol. 4, pp. 25–37. IEEE Computer Society, Los Alamitos (1997)CrossRefGoogle Scholar
  15. 15.
    Leung, T., Burl, M., Perona, P.: Finding faces in cluttered scenes using random labeled graph matching. In: Proc. Fifth International Conference on Computer Vision, pp. 637–644 (1995)Google Scholar
  16. 16.
    Yow, K., Cipolla, R.: Feature-based human face detection. Technical report, Department of Engineering, University of Cambridge, England (1996)Google Scholar
  17. 17.
    Sinha, P., Torralba, A.: Detecting faces in impoverished images. Journal of Vision 2(7) (2002) 601aCrossRefGoogle Scholar
  18. 18.
    Gordon, G.: Face recognition based on depth and curvature features. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 108–110 (1992)Google Scholar
  19. 19.
    Hallinan, P., et al.: Two- and Three-Dimensional Patterns on the Face. AK Peters, Natick (1999)Google Scholar
  20. 20.
    Huang, J., Heisele, B., Blanz, V.: Component-based face recognition with 3D morphable models. In: Proc. of the 4th Int. Conf. on Audio- and Video-Based Biometric Person Authenticitation, pp. 27–34 (2003)Google Scholar
  21. 21.
    Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)CrossRefGoogle Scholar
  22. 22.
    Zabrodsky, H., Peleg, S., Avnir, D.: Symmetry as a continuous feature. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(12), 1154–1166 (1995)CrossRefGoogle Scholar
  23. 23.
    Marola, G.: On the detection of the axes of symmetry of symmetric and almost symmetric planar images. IEEE Trans. Pattern Anal. Mach. Intell. 11(1), 104–108 (1989)zbMATHCrossRefGoogle Scholar
  24. 24.
    Shen, D., Ip, H., Cheung, K., Teoh, E.: Symmetry detection by generalized complex (gc) moments: A close-form solution. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 466–476 (1999)CrossRefGoogle Scholar
  25. 25.
    Reisfeld, D., Wolfson, H., Yeshurun, Y.: Detection of interest points using symmetry. In: ICCV 1990, pp. 62–65 (1990)Google Scholar
  26. 26.
    Reisfeld, D., Wolfson, H., Yeshurun, Y.: Robust facial feature detection using local symmetry. In: Proc. International Conference on Pattern Recognition, pp. 117–120 (1990)Google Scholar
  27. 27.
    Kovesi, P.: Symmetry and asymmetry from local phase. In: AI 1997, pp. 185–190 (1997)Google Scholar
  28. 28.
    Sun, C., Sherrah, J.: 3D symmetry detection using the extended gaussian image. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 164–168 (1997)CrossRefGoogle Scholar
  29. 29.
    Kazhdan, M., Chazelle, B., Dobkin, D., Finkelstein, A., Funkhouser, T.: A Reflective Symmetry Descriptor. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 642–656. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  30. 30.
    Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The princeton shape benchmark. In: Proc. Shape Modeling International, pp. 167–178 (2004)Google Scholar
  31. 31.
    Tangelder, J., Veltkamp, R.: Polyhedral model retrieval using weighted point sets (2003)Google Scholar
  32. 32.
    aim@shape (2004),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wolfram von Funck
    • 1
  • Holger Theisel
    • 1
  • Hans-Peter Seidel
    • 1
  1. 1.MPI InformatikSaarbrueckenGermany

Personalised recommendations