3D Face Recognition with Reconstructed Faces from a Collection of 2D Images

  • João Baptista Cardia NetoEmail author
  • Aparecido Nilceu Marana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Nowadays, there is an increasing need for systems that can accurately and quickly identify a person. Traditional identification methods utilize something a person knows or something a person has. This kind of methods has several drawbacks, being the main one the fact that it is impossible to detect an imposter who uses genuine credentials to pass as a genuine person. One way to solve these kinds of problems is to utilize biometric identification. The face is one of the biometric features that best suits the covert identification. However, in general, biometric systems based on 2D face recognition perform very poorly in unconstrained environments, common in covert identification scenarios, since the input images present variations in pose, illumination, and facial expressions. One way to mitigate this problem is to use 3D face data, but the current 3D scanners are expensive and require a lot of cooperation from people being identified. Therefore, in this work, we propose an approach based on local descriptors for 3D Face Recognition based on 3D face models reconstructed from collections of 2D images. Initial results show 95% in a subset of the LFW Face dataset.


Biometrics 3D face recognition 3DLBP Face reconstruction 



This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES). This study utilizes a GPU granted by the NVIDIA Grant Program.


  1. 1.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992). Scholar
  2. 2.
    Cardia Neto, J.B., Marana, A.N.: Utilizing deep learning and 3DLBP for 3D face recognition. In: Mendoza, M., Velastín, S. (eds.) CIARP 2017. LNCS, vol. 10657, pp. 135–142. Springer, Cham (2018). Scholar
  3. 3.
    Chetverikov, D., Stepanov, D., Krsek, P.: Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image Vis. Comput. 23(3), 299–309 (2005). Scholar
  4. 4.
    Huang, Y., Wang, Y., Tan, T.: Combining statistics of geometrical and correlative features for 3D face recognition. In: Proceedings of the British Machine Vision Conference, pp. 90.1–90.10. BMVA Press (2006).
  5. 5.
    Kakadiaris, I.A., et al.: Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 640–649 (2007). Scholar
  6. 6.
    Learned-Miller, E., Huang, G.B., RoyChowdhury, A., Li, H., Hua, G.: Labeled faces in the wild: a survey. In: Kawulok, M., Celebi, M.E., Smolka, B. (eds.) Advances in Face Detection and Facial Image Analysis, pp. 189–248. Springer, Cham (2016). Scholar
  7. 7.
    Li, B., Mian, A., Liu, W., Krishna, A.: Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 186–192 (2013).
  8. 8.
    Liu, J., Deng, Y., Bai, T., Huang, C.: Targeting ultimate accuracy: face recognition via deep embedding. CoRR abs/1506.07310 (2015).
  9. 9.
    Nguyen, V., Do, T., Nguyen, V.-T., Ngo, T.D., Duong, D.A.: How to choose deep face models for surveillance system? In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q.T. (eds.) Modern Approaches for Intelligent Information and Database Systems. SCI, vol. 769, pp. 367–376. Springer, Cham (2018). Scholar
  10. 10.
    Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)Google Scholar
  11. 11.
    Prabhakar, S., Pankanti, S., Jain, A.: Biometric recognition: security and privacy concerns. IEEE Secur. Priv. 1, 33–42 (2003). Scholar
  12. 12.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes 3rd Edition: The Art of Scientific Computing, 3rd edn. Cambridge University Press, New York (2007)zbMATHGoogle Scholar
  13. 13.
    Roth, J., Tong, Y., Liu, X.: Unconstrained 3D face reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015Google Scholar
  14. 14.
    Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964). Scholar
  15. 15.
    Schafer, R.W.: What is a savitzky-golay filter? [lecture notes]. IEEE Signal Process. Mag. 28(4), 111–117 (2011). Scholar
  16. 16.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015Google Scholar
  17. 17.
    Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015Google Scholar
  18. 18.
    Wen, G., Chen, H., Cai, D., He, X.: Improving face recognition with domain adaptation. Neurocomputing 287, 45–51 (2018). Scholar
  19. 19.
    Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • João Baptista Cardia Neto
    • 1
    Email author
  • Aparecido Nilceu Marana
    • 2
  1. 1.São Carlos Federal University - UFSCARSão CarlosBrazil
  2. 2.UNESP - São Paulo State UniversityBauruBrazil

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