A 3D Face Recognition Algorithm Based on Nonuniform Re-sampling Correspondence

  • Yanfeng Sun
  • Jun Wang
  • Baocai Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)


This paper proposes an approach of face recognition using 3D face data based on Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA). This approach first aligned 3D faces based on nonuniform mesh re-sampling by computing face surface curves. This step achieves aligning of 3D prototypes based on facial features, eliminates 3D face size information and preserves important 3D face shape information in the input face. Then 2D texture information and the 3D shape information are extracted from 3D face images for recognition. Experimental results for 105 persons 3D face data set obtained by Cyberware 3030RGB/PS laser scanner have demonstrated the performance of our algorithm.


Face Recognition Linear Discriminant Analysis Face Image Principle Component Analysis Face Segmentation 
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.
    Zhao, W., Chellappa, R., Rosenfeld, A.: Face recognition: a literature survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Moses, Y., Adini, Y., Ullman, S.: Face recognition: the problem of compensating for illumination changes. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 721–732 (1997)CrossRefGoogle Scholar
  3. 3.
    Zhao, A.R.W., Chellappa, R., Phillips, P.: Face recognition: a literature survey, revised. Frobnication. Technical Report CS-TR4167R, UMCP (2002)Google Scholar
  4. 4.
    Bowyer, K.W., Chang, K., Flynn, P.J.: An evaluation of multi-modal 2d+3d face biometrics. IEEE PAMI 27(4), 619–624 (2005)Google Scholar
  5. 5.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. The frobnicatable foo filter. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  6. 6.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Using discriminant eigenfeatures for image retrieval. The frobnicatable foo filter. IEEE PAMI 19(7), 711–720 (1997)Google Scholar
  7. 7.
    Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. The frobnicatable foo filter, 2006. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)CrossRefGoogle Scholar
  8. 8.
    Gu, C.L., Yin, B.C., Hu, Y.L., Cheng, S.Q.: UResampling Based Method for Pixel-wise Correspondence between 3D Faces. The frobnicatable foo filter, 2006. In: Proceedings, ITCC 2004, vol. 1, pp. 614–619 (2004)Google Scholar
  9. 9.
    Besl, J., McKay, N.D.: A Method for Registration of 3 -D Shapes.The frobnicatable foo filter, 2006. Proc.of IEEE Trans.on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  10. 10.
    Russ, T., Boehnen, C., Peters, T.: 3D Face Recognition Using 3D Alignment for PCA. The frobnicatable foo filter, 2006. In: Proceeding of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1391–1398 (2006)Google Scholar
  11. 11.
    Milroy, M.J., Bradley, C., Vickers, G.W.: Segmentation of a wrap around model using an active contour. The frobnicatable foo filter, 2006. Computer Aided Designed 29(4), 299–320 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yanfeng Sun
    • 1
  • Jun Wang
    • 1
  • Baocai Yin
    • 1
  1. 1.Beijing Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing 100022China

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