Asymmetric 3D/2D Processing: A Novel Approach for Face Recognition

  • Daniel Riccio
  • Jean-Luc Dugelay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


Facial image analysis is very useful in many applications such as video compression, talking heads, or biometrics. During the last few years, many algorithms have been proposed in particular for face recognition using classical 2-D images. Face is fairly easy to use and well accepted by people but generally not robust enough to be used in most practical security applications because too sensitive to variations in pose and illumination. One possibility to overcome this limitation is to work in 3-D instead of 2-D. But 3-D is costly and more difficult to manipulate and then ineffective to authenticate people in most contexts. Hence, to solve this problem, we propose a novel face recognition approach that is based on an asymmetric protocol: enrolment in 3-D but identification performed from 2-D images. So that, the goal is to make more robust face recognition while keeping the system practical. To make this 3-D/2-D approach possible, we introduce geometric invariants used in computer vision within the context of face recognition. We report preliminary experiments to evaluate robustness of invariants according to pose variations and to the accuracy of detection of facial feature points. Preliminary results obtained in terms of identification rate are encouraging.


Control Point Face Recognition Cross Ratio Geometric Invariant Facial Feature Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(9), 1191–1202 (2002)Google Scholar
  2. 2.
    Bronstein, M., Bronstein, M.M., Kimmel, R.: Expression Invariant 3D Face Recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 62–69. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Huang, J., Blanz, V., Heisele, B.: Face Recognition with Support Vector Macines and 3D Head Models. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 334–341. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Lavagetto, F., Pockaj, R.: The Facial Animation Engine: Toward a High-Level Interface for the Design of MPEG-4 Compliant Animated Faces. IEEE Trans. on Circuits and Systems for Video Technology 2(2) (March 1999)Google Scholar
  5. 5.
    Weinshall, D.: Model-based invariants for 3D Vision. International Journal of Computer Vision 10(1), 27–42 (1993)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Zhu, Y., Seneviratne, L.D., Earles, S.W.E.: A New Structure of Invariant for 3D Point Sets from A single View. In: IEEE International Conference on Robotics and Automation, May 1995, pp. 1726–1731 (1995)Google Scholar
  7. 7.
    Geometrix, Introducing FaceVision-The New Shape of Human Identification, February 13 (2005),

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Daniel Riccio
    • 1
  • Jean-Luc Dugelay
    • 2
  1. 1.Universitá di SalernoFisciano, SalernoItaly
  2. 2.Institut Eurecom, CMMSophia Antipolis, Cedex

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