Skip to main content

A Novel and Accurate Local 3D Representation for Face Recognition

  • Conference paper
  • First Online:
Advanced Concepts for Intelligent Vision Systems (ACIVS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10617))

  • 2714 Accesses

Abstract

In this paper, we intend to introduce a novel curved 3D face representation. It is constructed on some static parts of the face which correspond to the nose and the eyes. Each part is described by the level curves of the superposition of several geodesic potentials generated from many reference points. We propose to describe the eye region by a bipolar representation based on the superposition of two geodesic potentials generated from two reference points and the nose by a three-polar one (three reference points). We use the BU-3DFE database of 3D faces to test the accuracy of the proposed approach. The obtained results in the sense of the Hausdorff shape distance prove the performance of the novel representation for 3D faces identification. The obtained scores are comparable to the state of the art methods in the most of cases.

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

References

  1. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  2. Achermann, B., Bunke, H.: Classifying range images of human faces with Hausdorff distance in pattern recognition. In: Proceedings of 15th International Conference, IEEE, vol. 2, pp. 809–813 (2000)

    Google Scholar 

  3. Mian, A.S., Bennamoun, M., Owens, R.: Keypoint detection and local feature matching for textured 3D face recognition. Int. J. Comput. Vis. 79(1), 1–12 (2008)

    Article  Google Scholar 

  4. Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1695–1700 (2006)

    Article  Google Scholar 

  5. Faltemier, T.C., Bowyer, K.W., Flynn, P.J.: A region ensemble for 3-D face recognition. IEEE Trans. Inf. Forensics Secur. 3(1), 62–73 (2008)

    Article  Google Scholar 

  6. Lei, Y., Bennamoun, M., El-Sallam, A.A.: An efficient 3D face recognition approach based on the fusion of novel local low-level features. Pattern Recogn. 46(1), 24–37 (2013)

    Article  Google Scholar 

  7. Wang, Y., Liu, J., Tang, X.: Robust 3D face recognition by local shape difference boosting. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1858–1870 (2010)

    Article  Google Scholar 

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

    Google Scholar 

  9. Yin, L., Wei, X., Sun, Y., Wang, J. and Rosato, M.J.: A 3D facial expression database for facial behavior research. In: 7th international conference on Automatic Face and Gesture Recognition, pp. 211–216, April 2006

    Google Scholar 

  10. Ghorbel, F.: A unitary formulation for invariant image description: application to image coding. Special Issue Annales des Telecommunications, vol. 53 (1998)

    Google Scholar 

  11. Ghorbel, F.: Invariants for shapes and movement, in Eleven cases from 1D to 4D and from euclidean to projectives (French version), Arts-pi Edition, Tunisia (2012)

    Google Scholar 

  12. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  13. Samir, C., Srivastava, A., Daoudi, M.: Three-dimensional face recognition using shapes of facial curves. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1858–1863 (2006)

    Article  Google Scholar 

  14. Srivastava, A., Samir, C., Joshi, S.H., Daoudi, M.: Elastic shape models for face analysis using curvilinear coordinates. J. Math. Imaging Vis. 33(2), 253–265 (2009)

    Article  MathSciNet  Google Scholar 

  15. Gadacha, W., Ghorbel, F.: A new 3D surface registration approach depending on a suited resolution: application to 3D faces. In: 16th IEEE Mediterranean Electrotechnical Conference (MELECON), pp. 649–652, March 2012

    Google Scholar 

  16. Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Nat. Acad. Sci. 93(4), 1591–1595 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  17. Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Robotics-DL Tentative, in International Society for Optics and Photonics, pp. 586–606, April 1992

    Google Scholar 

  18. Ghorbel, F., Jribi, M.: A robust invariant bipolar representation for R3 surfaces: applied to the face description. Annals of Telecommunications-annales des télécommunications 68(3–4), 219–230 (2013)

    Article  Google Scholar 

  19. Jribi, M., Ghorbel, F.: A stable and invariant three-polar surface representation: application to 3D face description. Vaclav Skala-UNION Agency (2014)

    Google Scholar 

  20. Mohammadzade, H., Hatzinakos, D.: Iterative closest normal point for 3D face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 381–397 (2013)

    Article  Google Scholar 

  21. Lei, Y., Guo, Y., Hayat, M., Bennamoun, M., Zhou, X.: A two-phase weighted collaborative representation for 3D partial face recognition with single sample. Pattern Recogn. 52, 218–237 (2016). Elsevier

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soumaya Mathlouthi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mathlouthi, S., Jribi, M., Ghorbel, F. (2017). A Novel and Accurate Local 3D Representation for Face Recognition. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70353-4_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70352-7

  • Online ISBN: 978-3-319-70353-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics