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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
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)
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)
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)
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)
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)
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)
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
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
Ghorbel, F.: A unitary formulation for invariant image description: application to image coding. Special Issue Annales des Telecommunications, vol. 53 (1998)
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)
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)
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)
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)
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
Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Nat. Acad. Sci. 93(4), 1591–1595 (1996)
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
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)
Jribi, M., Ghorbel, F.: A stable and invariant three-polar surface representation: application to 3D face description. Vaclav Skala-UNION Agency (2014)
Mohammadzade, H., Hatzinakos, D.: Iterative closest normal point for 3D face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 381–397 (2013)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
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)