Skip to main content

Asymmetry Patterns Shape Contexts to Describe the 3D Geometry of Craniofacial Landmarks

  • Conference paper
  • First Online:
Computer Vision, Imaging and Computer Graphics -- Theory and Applications (VISIGRAPP 2013)

Abstract

We present a new family of 3D geometry descriptors based on asymmetry patterns from the popular 3D Shape Contexts (3DSC). Our approach resolves the azimuth ambiguity of 3DSC, thus providing rotational invariance, at the expense of a marginal increase in computational load, outperforming previous algorithms dealing with azimuth ambiguity. We build on a recently presented measure of approximate rotational symmetry in 2D, defined as the overlapping area between a shape and rotated versions of itself, to extract asymmetry patterns from a 3DSC in a variety of ways, depending on the spatial relationships that need to be highlighted or disabled. Thus, we define Asymmetry Patterns Shape Contexts (APSC) from a subset of the possible spatial relations present in the spherical grid of 3DSC; hence they can be thought of as a family of descriptors that depend on the subset that is selected. The possibility to define APSC descriptors by selecting diverse spatial patterns from a 3DSC has two important advantages: (1) choosing the appropriate spatial patterns can considerably reduce the errors obtained with 3DSC when targeting specific types of points; (2) Once one APSC descriptor is built, additional ones can be built with only incremental cost. Therefore, it is possible to use a pool of APSC descriptors to maximize accuracy without a large increase in computational cost.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Polhemus FastSCAN\(^{TM}\), Colchester, VT, USA. An example is available at http://www.cipa.dcu.ie/videos/face3d/Scanning_DCU_RCSI.avi [Accessed on 20.05.2013].

  2. 2.

    The complete results for all patterns listed in Table 1 are available at http://www.cipa.dcu.ie/pubs_full.html [Accessed on 15.07.2013].

  3. 3.

    http://clementcreusot.com/phd/ [Accessed on 08.07.2013].

  4. 4.

    http://www.cipa.dcu.ie/pubs_full.html [Accessed on 15.07.2013].

References

  1. Aynechi, N., Larson, B.E., Leon-Salazar, V., Beiraghi, S.: Accuracy and precision of a 3D anthropometric facial analysis with and without landmark labeling before image acquisition. Angle Orthod. 81(2), 245–252 (2011)

    Article  Google Scholar 

  2. Bariya, P., Novatnack, J., Schwartz, G., Nishino, K.: 3D geometric scale variability in range images: features and descriptors. Int. J. Comput. Vis. 99(2), 232–255 (2012)

    Article  MathSciNet  Google Scholar 

  3. Bronstein, A.M., Bronstein, M.M., Castellani, U., Dubrovina, A., Guibas, L.J., Horaud, R.P., Kimmel, R., Knossow, D., von Lavante, E., Mateus, D., Ovsjanikov, M., Sharma, A.: SHREC 2010: robust correspondence benchmark. In: Proceedings of the Eurographics Workshop on 3D Object Retrieval (2010)

    Google Scholar 

  4. Chen, H., Bhanu, B.: 3D free-form object recognition in range images using local surface patches. Pattern Recogn. Lett. 28(10), 1252–1262 (2007)

    Article  Google Scholar 

  5. Creusot, C., Pears, N., Austin, J.: Automatic keypoint detection on 3D faces using a dictionary of local shapes. In: Proceedings of the 1st Joint Conference on 3D Imaging, Modeling, Processing, Visualization, and Transmission, pp. 204–211 (2011)

    Google Scholar 

  6. Creusot, C., Pears, N., Austin, J.: A machine-learning approach to keypoint detection and landmarking on 3D meshes. Int. J. Comput. Vis. 102, 146–179 (2013)

    Article  Google Scholar 

  7. Dagum, L., Menon, R.: OpenMP: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)

    Article  Google Scholar 

  8. Farkas, L.G.: Anthropometry of the Head and Face. Raven Press, New York (1994)

    Google Scholar 

  9. Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 224–237. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  10. Guo, Q., Guo, F., Shao, J.: Irregular shape symmetry analysis: theory and application to quantitative galaxy classification. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1730–1743 (2010)

    Article  Google Scholar 

  11. Gupta, S., Markey, M.K., Bovik, A.C.: Anthropometric 3D face recognition. Int. J. Comput. Vis. 90(3), 331–349 (2010)

    Article  Google Scholar 

  12. Hennessy, R.H., Kinsella, A., Waddington, J.L.: 3D laser surface scanning and geometric morphometric analysis of craniofacial shape as an index of cerebro-craniofacial morphogenesis. Biol. Psychiatry 51(6), 507–514 (2002)

    Article  Google Scholar 

  13. Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)

    Article  Google Scholar 

  14. Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Symmetry descriptors and 3D shape matching. In: Proceedings of the Eurographics/ACM SIGGRAPH Symposium on Geometry Processing, pp. 156–164 (2003)

    Google Scholar 

  15. Kortgen, M., Park, G.J., Novotni, M., Klein, R.: 3D shape matching with 3D shape contexts. In: 7th Central European Seminar on Computer Graphics (2003)

    Google Scholar 

  16. Passalis, G., Perakis, N., Theoharis, T., Kakadiaris, I.A.: Using facial symmetry to handle pose variations in real-world 3D face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1938–1951 (2011)

    Article  Google Scholar 

  17. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3212–3217 (2009)

    Google Scholar 

  18. Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1–4 (2011)

    Google Scholar 

  19. Segundo, M.P., Silva, L., Bellon, O.R.P., Queirolo, C.C.: Automatic face segmentation and facial landmark detection in range images. IEEE Trans. Syst. Man Cybern. B Cybern. 40(5), 1319–1330 (2010)

    Article  Google Scholar 

  20. Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: Point feature extraction on 3D range scans taking into account object boundaries. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2601–2608 (2011)

    Google Scholar 

  21. Sukno, F.M., Waddington, J.L., Whelan, P.F.: Comparing 3D descriptors for local search of craniofacial landmarks. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Fowlkes, C., Wang, S., Choi, M.-H., Mantler, S., Schulze, J., Acevedo, D., Mueller, K., Papka, M. (eds.) ISVC 2012, Part II. LNCS, vol. 7432, pp. 92–103. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  22. Sukno, F.M., Waddington, J.L., Whelan, P.F.: Compensating inaccurate annotations to train 3D facial landmark localization models. In: FG Workshop on 3D Face Biometrics (2013)

    Google Scholar 

  23. Sukno, F.M., Waddington, J.L., Whelan, P.F.. Rotationally invariant 3D shape contexts using asymmetry patterns. In: Proceedings of the International Conference on Computer Graphics Theory and Applications, pp. 7–17 (2013)

    Google Scholar 

  24. Szeptycki, P., Ardabilian, M., Chen, L.: A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking. In: Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 1–6 (2009)

    Google Scholar 

  25. Toma, A.M., Zhurov, A., Playle, R., Ong, E., Richmond, S.: Reproducibility of facial soft tissue landmarks on 3D laser-scanned facial images. Orthod. Craniofac. Res. 12(1), 33–42 (2009)

    Article  Google Scholar 

  26. Tombari, F., Salti, S., Di Stefano, L.: Unique shape context for 3D data description. In: Proceedings of ACM Workshop on 3D Object Retrieval, pp. 57–62 (2010)

    Google Scholar 

  27. Zaharescu, A., Boyer, E., Horaud, R.: Keypoints and local descriptors of scalar functions on 2D manifolds. Int. J. Comput. Vis. 99(2), 232–255 (2012)

    Article  MathSciNet  Google Scholar 

  28. Zhang, Y.: Intrinsic shape signatures: a shape descriptor for 3D object recognition. In: Proceedings of the 12th IEEE International Conference on Computer Vision Workshops, pp. 689–696 (2009)

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank their colleagues in the Face3D Consortium (www.face3d.ac.uk) and financial support from the Wellcome Trust (WT-086901 MA) and the Marie Curie IEF programme (grant 299605, SP-MORPH).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Federico M. Sukno .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sukno, F.M., Waddington, J.L., Whelan, P.F. (2014). Asymmetry Patterns Shape Contexts to Describe the 3D Geometry of Craniofacial Landmarks. In: Battiato, S., Coquillart, S., Laramee, R., Kerren, A., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics -- Theory and Applications. VISIGRAPP 2013. Communications in Computer and Information Science, vol 458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44911-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-44911-0_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44910-3

  • Online ISBN: 978-3-662-44911-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics