A 3D Facial Recognition System Using Structured Light Projection

  • Miguel A. Vázquez
  • Francisco J. Cuevas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8480)


In this paper, a facial recognition system is described, which carry out the classification process by analyzing 3D information of the face. The process begins with the acquisition of the 3D face using light structured projection and the phase shifting technique. The faces are aligned respect a face profile and the region of front, eyes and nose is segmented. The descriptors are obtained using the eigenfaces approach and the classification is performed by linear discriminant analysis. The main contributions of this work are: a) the application of techniques of structured light projection for the calculation of the cloud of points related to the face, b) the use of the phase of the signal to perform recognition with 97% reliability, c) the use of the profile of the face in the alignment process and d) the robustness in the recognition process in the presence of gestures and facial expressions.


Biometrics facial recognition structured light projection pattern recognition artificial vision 3D face 3d recovery 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Song, H., Yang, U., Lee, S., Sohn, K.: 3D Face Recognition Based on Facial Shape Indexes with Dynamic Programming. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 99–105. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. Andrews, H.C.: Introduction to mathematical techniques in pattern recognition. Wiley-Interscience, Canada (1972)zbMATHGoogle Scholar
  3. Arevallilo Herráez, M., Burton, D.R., Lalor, M.J., Gdeisat, M.A.: Fast two-dimensional phase-unwrapping algorithm based on sorting by reliability following a noncontinuous path. Applied Optics 41(35), 7437–7444 (2002)CrossRefGoogle Scholar
  4. Arun, A.R., Karthik, N., Anil, K.J.: Hand book of multibiometrics. Springer, New York (2006)Google Scholar
  5. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 39–256 (1992)CrossRefGoogle Scholar
  6. Bow, S.-T.: Pattern recognition. Aplication to data-set problems. Electrocal Engineering and Electronics, Pennsylvania (1984)zbMATHGoogle Scholar
  7. Cabello Pardos, E.: Técnicas de reconocimiento facial mediante redes neuronales. Departamento de tecnología fotonica, facultad de informática, Madrid (2004)Google Scholar
  8. Chenghua, X., Yunhong, W., Tieniu, T., Long, Q.: A new attempt to face recognition using 3d eigenfaces. In: Proc. ACCV 2004, pp. 884–889 (2004)Google Scholar
  9. Colombo, A., Cusano, C., Schettini, R.: 3D face detection using curvature analysis. Pattern Recognition. The Journal of the Pattern Recognition Society 39(3), 445–455 (2006)Google Scholar
  10. Duda, R., Hart, P., Stork, D.: Pattern clasification. A Wiley International Publication (2001)Google Scholar
  11. Fu, Y., Luo, Q.: Fringe projection profilometry based on a novel phase shift method. Optics Express 19(22) (2011)Google Scholar
  12. Gordon, G.G.: Face Recognition from depth maps and surface curvature. In: Conference on Geometric Methods in Computer Vision, pp. 234–247. SPIE, San Diego (1991)CrossRefGoogle Scholar
  13. Gonzalez, R., Woods, R.: Digital image proscessing. Pearson Pretince Hall, New Jersey (2008)Google Scholar
  14. Heseltine, T., Pears, N., Austin, J.: Three-dimensional Face Recognition: an Eigensurface Approach. In: International Conference on Image Processing. IEEE, Singapore (2004)Google Scholar
  15. Jain, A., Flynn, P., Ross, A.: Handbook of biometrics. Springer, New York (2008)CrossRefGoogle Scholar
  16. Krzanowski, W.J.: Principles of Multivariate Analysis: A User’s Perspective. Oxford University Press, New York (1988)zbMATHGoogle Scholar
  17. Kyong, K.I., Bowyer, K.W., Flynn, P.J.: Multiple Nose Region Matching for 3D Face Recognition Under Varying Facial Expression. Transactions on Pattern Analisysis and machine Intelligence, 1695–1700 (2006)Google Scholar
  18. Russ, T., Boehen, C., Peters, T.: 3D Face Recognition Using 3D Alignment for PCA. In: Conference on Computer Vision and Pattern Recognition. IEEE Computer Society (2006)Google Scholar
  19. Saeed, K., Nagashima, T.: Biometrics and Kansei Enginering. Springer, New York (2012)CrossRefGoogle Scholar
  20. Seber, G.: Multivariate Observations. John Wiley & Sons, Inc., Hoboken (1984)CrossRefzbMATHGoogle Scholar
  21. Siva Gorthi, S., Rastogi, P.: Fringe Projection Techniques: Whither we are? Optics and Lasers in Engineering 48(2), 133–140 (2009)CrossRefGoogle Scholar
  22. Turk, M., Petland, A.: Eigenfaces for recognition. Journal of Cognitive Neurosience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  23. Wayman, J.: Introduction to biometrics. Springer, New York (2011)Google Scholar
  24. Woz, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Information Fusion 16, 3–17 (2014)CrossRefGoogle Scholar
  25. Xue, Y., Jianming, L., Takashi, Y.: A method of 3D face recognition based on principal component analysis algorithm. In: IEEE International Symposium on Circuits and Systems, ISCAS 2005 (2005)Google Scholar
  26. Yunqui, L., Haibin, L., Qingmin, L.: Geometric Features of 3D Face and Recognition of It by PCA. Journal of Multimedia 6(2) (April 2002)Google Scholar
  27. Zhang, C.: A survey of recent advances in face detecction. Microsoft corporation (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Miguel A. Vázquez
    • 1
  • Francisco J. Cuevas
    • 1
  1. 1.Centro de Investigaciones en Óptica, A.C.LeónMéxico

Personalised recommendations