Pose Normalization Using Generic 3D Face Model as a Priori for Pose-Insensitive Face Recognition

  • Xiujuan Chai
  • Shiguang Shan
  • Wen Gao
  • Xin Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3338)


Abrupt performance degradation caused by face pose variations has been one of the bottlenecks for practical face recognition applications. This paper presents a practical pose normalization technique by using a generic 3D face model as a priori. The 3D face model greatly facilitates the setup of the correspondence between non-frontal and frontal face images, which can be exploited as a priori to transform a non-frontal face image, with known pose but very sparse correspondence with the generic face model, into a frontal one by warping techniques. Our experiments have shown that the proposed method can greatly improve the recognition performance of the current face recognition methods without pose normalization.


Feature Point Face Recognition Face Image Affine Transformation Frontal Face 
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.


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  1. 1.
    Brunelli, R., Poggio, T.: Face Recognition: Features versus Template. TPAMI 15(10), 1042–1052 (1993)Google Scholar
  2. 2.
    Pentland, A., Moghaddam, B., Starner, T.: View-based and Modular Eigenspace for Face Recognition. In: IEEE CVPR, pp. 84–91 (1994)Google Scholar
  3. 3.
    Murase, H., Nayar, S.K.: Visual Learning and Recognition of 3-D Objects form Appearance. International Journal of Computer Vision 14, 5–24 (1995)CrossRefGoogle Scholar
  4. 4.
    Murase, H., Nayar, S.: Learning and Recognition of 3D Objects from Appearance. International Journal of Computer Vision, 5–25 (January 1995)Google Scholar
  5. 5.
    SmMcKenna, S.G., Collins, J.J.: Face Tracking and Pose Representation. In: British Machine Vision Conference, Edinburgh, Scotland (1996)Google Scholar
  6. 6.
    Valentin, D., Abdi, H.: Can a Linear Autoassociator Recognize Faces From New Orientations. Journal of the Optical Society of American A-optics, Image Science and Vision 13(4), 717–724 (1996)CrossRefGoogle Scholar
  7. 7.
    Blanz, V., Vetter, T.: A Morphable Model for the Synthesis of 3D Faces. Proc. SIGGRAPH, 187–194 (1999)Google Scholar
  8. 8.
    Cootes, T.F., Walker, K., Taylor, C.J.: View-Based Active Appearance Models. In: IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, p. 227 (March 2000)Google Scholar
  9. 9.
    Zhou, Z., HuangFu, J., Zhang, H., Chen, Z.: Neural Network Ensemble Based View Invariant Face Recognition. Journal of Computer Study and Development 38(9), 1061–1065 (2001)Google Scholar
  10. 10.
    Gross, R., Matthews, I., Baker, S.: Eigen Light-Fields and Face Recognition Across Pose. In: Proceedings of the Fifth International Conference on Face and Gesture Recognition (2002)Google Scholar
  11. 11.
    Gross, R., Matthews, I., Baker, S.: Appearance-Based Face Recognition and Light Fields, Tech. Report CMU-RI-TR-02-20, Robotics Institute, Carnegie Mellon University (August. 2002)Google Scholar
  12. 12.
    Jiang, D.L., Gao, W., Wang, Z.Q., Chen, Y.Q.: Realistic 3D Facial Animation with Subtle Texture Changes. In: ICICS-PCM2003, Singapore (December 2003)Google Scholar
  13. 13.
    Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Transactions on PAMI 25, 1063–1074 (2003)Google Scholar
  14. 14.
    Gao, W., Cao, B., Shan, S.G., Zhou, D.L., Zhang, X.H., Zhao, D.B.: The CAS-PEAL Large-Scale Chinese Face DataBase and Evaluation Protocols, Technique Report No. JDL-TR_04_FR_001, Joint Research & Development Laboratory, CAS (2004), http://www.jdl.ac.cn

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Xiujuan Chai
    • 1
  • Shiguang Shan
    • 2
  • Wen Gao
    • 1
    • 2
  • Xin Liu
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinP.R. China
  2. 2.ICT-ISVISION Joint R&D Laboratory for Face RecognitionCASBeijingP.R. China

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