Advertisement

The Spiral Facets: A Compact 3D Facial Mesh Surface Representation and Its Applications

  • Naoufel Werghi
  • Harish Bhaskar
  • Mohamed Khamis Naqbi
  • Youssef Meguebli
  • Haykel Boukadida
Part of the Communications in Computer and Information Science book series (CCIS, volume 274)

Abstract

In this paper we introduce the spiral facets framework as a novel mechanism of encoding 3D facial triangular mesh surface that incorporates crucial 3D face shape information by exploiting specific arrangement patterns of facets in the mesh model. The spiral facets framework is characterized by its simplicity and in the ability of adapting it for several useful applications within the domain of 3D face recognition. This paper will introduce the framework and study in detail the properties of the spiral facets that make it suitable for applications including frontal face extraction, estimation of face pose, facial feature extraction and face alignment. We validate the stability and the robustness of our framework against different system level parameters through experimentation with raw 3D mesh surfaces obtained from both globally available datasets and in-house 3D scanned data. Our results demonstrate that the spiral facets framework is a simple and compact representation that encompasses rich shape information that can be usefully deployed both locally and globally across the 3D face in comparison to other standard representations.

Keywords

3D face descriptors Spiral facets Cubic spline Feature localization Geodesic curves 3D face orientation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Moreno, A., Sanchez, A., Martinez, E.: Robust representation of 3d faces for recognition. Int. Journal of Pattern Recognition and Artificial Intelligence 20, 1159–1186 (2006)CrossRefGoogle Scholar
  2. 2.
    Chua, C., Han, F., Ho, Y.: 3d human face recognition using point signature. In: Conf. on Automatic Face and Gesture Recognition, pp. 233–238 (2000)Google Scholar
  3. 3.
    Wu, Y., Pan, G., Wu, Z.: Face Authentication Based on Multiple Profiles Extracted From Range Data. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 515–522. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Xu, D., Hu, P., Cao, W., Li, H.: 3d face recognition using moment invariants. In: IEEE Int. Conference on Shape Modeling and Applications, pp. 261–262 (2008)Google Scholar
  5. 5.
    Irfanoglu, M., Gokberk, B., Akarun, L.: 3d shape-based face recognition using automatically registered facial surfaces. In: Conf. Pattern Recognition, vol. 4, pp. 183–186 (2004)Google Scholar
  6. 6.
    Lu, X., Jain, A.: Deformation analysis for 3d face matching. In: IEEE Workshops on Application of Computer Vision, pp. 99–104 (2005)Google Scholar
  7. 7.
    Lee, Y., Park, K., Shim, J., Yi, T.: 3d face recognition using statistical multiple features for the local depth information. In: Conf. Vision Interface, pp. 102–108 (2003)Google Scholar
  8. 8.
    Xu, C., Wang, Y., Tan, T., Quan, L.: A new attempt to face recognition using eigenfaces. In: Asian Conference on Computer Vision, vol. 2, pp. 884–889 (2004)Google Scholar
  9. 9.
    Bronstein, A., Bronstein, M., Kimmel, R.: Expression invariant 3d face recognition. Audio- and Video-Based Person Authentication, 62–70 (2003)Google Scholar
  10. 10.
    Berretti, S., Bimbo, A., Pala, P.: Description and retrieval of 3d face models using iso-geodesic stripes. In: Conf. Multimedia Information Retrieval, pp. 13–22 (2006)Google Scholar
  11. 11.
    Samir, C., Srivastava, A., Daoudi, M., Klassen, E.: An intrinsic framework for analysis of facial surfaces. International Journal of Computer Vision 82, 80–85 (2009)Google Scholar
  12. 12.
    Kakadiaris, I., et al.: Three-dimensional face recognition in the presence of facial expressions: An annotated deformable model approach. IEEE Transaction on Pattern Analysis and Machine Intelligence 29, 1–10 (2007)Google Scholar
  13. 13.
    Pan, G., Wu, Y., Wu, Z., Liu, W.: 3d face recognition by profile and surface matching. In: IEEE/INNS Conf. on Neural Networks, vol. 3, pp. 2169–2174 (2003)Google Scholar
  14. 14.
    Xu, C., Wang, Y., Tan, T., Quan, L.: Automatic 3d face recognition combining global geometric features with local shape variation information. In: IEEE Conf. on Automatic Face and Gesture Recognition, pp. 302–307 (2004)Google Scholar
  15. 15.
    Mian, A., Bennamoun, M., Owens, R.: An efficient multimodal 2d-3d hybrid approach to automatic face recognition. IEEE Transactions in Pattern Analysis and Machine Intelligence 29, 1927–1943 (2007)CrossRefGoogle Scholar
  16. 16.
    Cormen, T.H., Leiserson, C., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, McGraw-Hill (2001)Google Scholar
  17. 17.
    Sethian, J., Kimmel, R.: Computing geodesic paths on manifolds. Proc. National Academy of Sciences 95, 8431–8435 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Colbry, D., Stockman, G., Jain, A.: Detection of anchor points for 3d face verification. In: Proc. Computer Vision and Pattern Recognition (2005)Google Scholar
  19. 19.
    Heseltine, T., Pears, N., Austin, J.: Three-dimensional face recognition using combinations of surface feature map subspace components. Image and Vision Computing 26, 382–396 (2008)CrossRefGoogle Scholar
  20. 20.
    Segundo, M., Queirolo, C., Bellon, O., Silva, L.: Automatic 3d facial segmentation and landmark detection. In: Proc. 14th Int. Conf. on Image Analysis and Processing, pp. 431–436 (2007)Google Scholar
  21. 21.
    Wang, Y., Tang, X., Liu, J., Pan, G., Xiao, R.: 3D Face Recognition by Local Shape Difference Boosting. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 603–616. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    Ruiz, M., Illingworth, J.: Automatic landmarking of faces in 3d-alf. In: 5th International Conference on Visual Information Engineering (VIE 2008), pp. 41–46 (2008)Google Scholar
  23. 23.
    Romero, M., Pears, N.: Landmark localisation in 3d face data. In: IEEE Conf. on Advanced Video and Signal Based Surveillance, pp. 73–78 (2009)Google Scholar
  24. 24.
    Xu, C., Tan, T., Wang, Y., Quan, L.: Combining local features for robust nose location in 3d facial data. Pattern Recognition Letters 27, 1487–1494 (2006)CrossRefGoogle Scholar
  25. 25.
    Frey, P., Borouchaki, H.: Surface mesh quality evaluation. International Journal for Numerical Methods in Engineering 45, 101–118 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Ashbrook, A.P., Fisher, R.B., Robertson, C., Werghi, N.: Finding Surface Correspondence for Object Recognition and Registration Using Pairwise Geometric Histograms. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 674–686. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  27. 27.
    Al-Osaimi, F.R., Bennamoun, M., Mian, A.: Integration of local and global geometrical cues for 3d face recognition. Pattern Recognition 41, 1030–1040 (2008)zbMATHCrossRefGoogle Scholar
  28. 28.
    Nair, P., Cavallaro, A.: 3-d face detection, landmark localization, and registration using a point distribution model. IEEE Trans. Multimedia 1, 611–623 (2009)CrossRefGoogle Scholar
  29. 29.
    Niese, R., Al-Hamadi, A., Michaelsi, B.: A novel method for 3d face detection and normalization. Journal of Multimedia 2, 1–12 (2007)CrossRefGoogle Scholar
  30. 30.
    Piegl, L., Tiller, W.: The NURBS Book. Springer, Heidelberg (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Naoufel Werghi
    • 1
  • Harish Bhaskar
    • 1
  • Mohamed Khamis Naqbi
    • 1
  • Youssef Meguebli
    • 2
  • Haykel Boukadida
    • 2
  1. 1.Dept. of Computer EngineeringKhalifa UniversitySharjahU.A.E.
  2. 2.School of Science and TechniquesUniversity of TunisTunisTunisia

Personalised recommendations