3D Face Recognition in Continuous Spaces

  • Francisco José Silva Mata
  • Elaine Grenot Castellanos
  • Alfredo Muñoz-Briseño
  • Isneri Talavera-Bustamante
  • Stefano BerrettiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)


This work introduces a new approach for face recognition based on 3D scans. The main idea of the proposed method is that of converting the 3D face scans into a functional representation, performing all the subsequent processing in the continuous space. To this end, a model alignment problem is first solved by combining graph matching and clustering. Fiducial points of the face are initially detected by analysis of continuous functions computed on the surface. Then, the alignment is performed by transforming the geometric graphs whose nodes are the critical points of the representative function of the surface in previously determined subspaces. A clustering step is finally applied to correct small displacement in the models. The 3D face representation is then obtained on the aligned models by functions carefully selected according to mathematical and computational criteria. In particular, the face is divided into regions, which are treated as independent domains where a set of functions is determined by fitting the surface data using the least squares method. Experimental results demonstrate the feasibility of the method.


3D face recognition Functional representation 


  1. 1.
    University of Notre Dame biometrics database (2008).
  2. 2.
    Bagdanov, A.D., Del Bimbo, A., Masi, I.: The Florence 2D/3D hybrid face dataset. In: Proceedings of Joint ACM Workshop on Human Gesture and Behavior Understanding, pp. 79–80 (2011)Google Scholar
  3. 3.
    Berretti, S., Del Bimbo, A., Pala, P.: 3D face recognition using iso-geodesic stripes. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2162–2177 (2010)CrossRefGoogle Scholar
  4. 4.
    Berretti, S., Del Bimbo, A., Pala, P.: Sparse matching of salient facial curves for recognition of 3-D faces with missing parts. IEEE Trans. Inf. Forensics Secur. 8(2), 374–389 (2013)CrossRefGoogle Scholar
  5. 5.
    Bondi, E., Pala, P., Berretti, S., Del Bimbo, A.: Reconstructing high-resolution face models from Kinect depth sequences. IEEE Trans. Inf. Forensics Secur. 11(12), 2843–2853 (2016)CrossRefGoogle Scholar
  6. 6.
    Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 567–585 (1989)CrossRefzbMATHGoogle Scholar
  7. 7.
    Bowyer, K.W., Chang, K.I., Flynn, P.J.: A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput. Vis. Image Underst. 101(1), 1–15 (2006)CrossRefGoogle Scholar
  8. 8.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Robust expression-invariant face recognition from partially missing data. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 396–408. Springer, Heidelberg (2006). doi: 10.1007/11744078_31 CrossRefGoogle Scholar
  9. 9.
    Cohen-Steiner, D., de Verdière, E.C., Yvinec, M.: Conforming Delaunay triangulations in 3D. Comput. Geom. 28(2–3), 217–233 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Colombo, A., Cusano, C., Schettini, R.: Gappy PCA classification for occlusion tolerant 3D face detection. J. Math. Imaging Vis. 35(3), 193–207 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Drira, H., Ben Amor, B., Srivastava, A., Daoudi, M., Slama, R.: 3D face recognition under expressions, occlusions, and pose variations. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2270–2283 (2013)CrossRefGoogle Scholar
  12. 12.
    Faltemier, T.C., Bowyer, K.W., Flynn, P.J.: A region ensemble for 3D face recognition. IEEE Trans. Inf. Forensics Secur. 3(1), 62–73 (2008)CrossRefGoogle Scholar
  13. 13.
    Huang, D., Ardabilian, M., Wang, Y., Chen, L.: 3D face recognition using eLBP-based facial representation and local feature hybrid matching. IEEE Trans. Inf. Forensics Secur. 7(5), 1551–1564 (2012)CrossRefGoogle Scholar
  14. 14.
    Lu, X., Jain, A.K., Colbry, D.: Matching 2.5D face scans to 3D models. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 31–43 (2006)CrossRefGoogle Scholar
  15. 15.
    Passalis, G., Perakis, P., 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)CrossRefGoogle Scholar
  16. 16.
    Porro-Muñoz, D., José Silva-Mata, F., Revilla-Eng, A., Talavera-Bustamante, I., Berretti, S.: 3D face recognition by functional data analysis. In: Bayro-Corrochano, E., Hancock, E. (eds.) CIARP 2014. LNCS, vol. 8827, pp. 818–826. Springer, Cham (2014). doi: 10.1007/978-3-319-12568-8_99 Google Scholar
  17. 17.
    Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., Akarun, L.: Bosphorus database for 3D face analysis. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BioID 2008. LNCS, vol. 5372, pp. 47–56. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-89991-4_6 CrossRefGoogle Scholar
  18. 18.
    Wang, Y., Liu, J., Tang, X.: Robust 3D face recognition by local shape difference boosting. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 1858–1870 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Francisco José Silva Mata
    • 1
  • Elaine Grenot Castellanos
    • 1
  • Alfredo Muñoz-Briseño
    • 1
  • Isneri Talavera-Bustamante
    • 1
  • Stefano Berretti
    • 2
    Email author
  1. 1.CENATAVHavanaCuba
  2. 2.University of FlorenceFlorenceItaly

Personalised recommendations