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3D Face Recognition under Pose Varying Environments

  • Hwanjong Song
  • Ukil Yang
  • Kwanghoon Sohn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2908)

Abstract

This paper describes a novel three-dimensional (3D) face recognition method when the head pose varies severely. Given an unknown 3D face, we extract several invariant facial features based on the facial geometry. We perform a Error Compensated Singular Value Decomposition (EC-SVD) for 3D face recognition. The novelty of the proposed EC-SVD procedure lies in compensating for the error for each rotation axis accurately. When the pose of a face is estimated, we propose a novel two-stage 3D face recognition algorithm. We first select face candidates based on the 3D-based nearest neighbor classifier and then the depth-based template matching is performed for final recognition. From the experimental results, less than a 0.2 degree error in average has been achieved for the 3D head pose estimation and all faces are correctly matched based on our proposed method.

Keywords

Face Recognition Singular Value Decomposition Gesture Recognition Error Angle Face Recognition System 
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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References

  1. 1.
    Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: A survey. Proceedings of the IEEE 83, 705–740 (1995)CrossRefGoogle Scholar
  2. 2.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phllips, P.J.: Face recognition: A survey. CVL Tech. Report, Center for Automation Research, University of Maryland at College Park (2000)Google Scholar
  3. 3.
    Maurer, T., Malsburg, C.: Tracking and learning graphs and pose on image sequences on faces. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, Vermont, USA, pp. 176–181 (1996)Google Scholar
  4. 4.
    Horprasert, T., Yacoob, Y., Davis, L.S.: Computing 3-D head orientation from a monocular image sequence. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, Vermont, USA, pp. 242–247 (1996)Google Scholar
  5. 5.
    Machin, D.: Real-time facial motion analysis for virtual teleconferencing. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, Vermont, USA, pp. 340–344 (1996)Google Scholar
  6. 6.
    Elagin, E., Steffens, J., Neven, H.: Automatic pose estimation system for human faces based on bunch graph matching technology. In: Proceedings of the Third International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 136–141 (1998)Google Scholar
  7. 7.
    Chen, Q., Wu, H., Fukumoto, T., Yachida, M.: 3D head pose estimation without feature tracking. In: Proceedings of the Third International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 88–93 (1998)Google Scholar
  8. 8.
    Chen, Q., Wu, H., Shioyama, T., Shimada, T.: Head pose estimation using both color and feature information. In: Proceedings of the Fifteenth International Conference on Pattern Recognition, Barcelona, Spain, pp. 2842–2847 (2000)Google Scholar
  9. 9.
    Hattori, K., Matsumori, S., Sato, Y.: Estimating pose of human face based on symmetry plane using range and intensity images. In: Proceedings of the Fifteenth International Conference on Pattern Recognition, Brisbane, Australia, vol. 2, pp. 1183–1187 (1998)Google Scholar
  10. 10.
    Lee, J.C., Milios, E.: Matching range image of human faces. In: Proceedings of the Third International Conference on Computer Vision, pp. 722–726 (1990)Google Scholar
  11. 11.
    Tanaka, H.T., Ikeda, M., Chiaki, H.: Curvature-based face surface recognition using spherical correlation. In: Proceedings of the Third International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 372–377 (1998)Google Scholar
  12. 12.
    Achermann, B., Jiang, X., Bunke, H.: Face recognition using range images. In: International Conference on Virtual Systems and MultiMedia 1997 (VSMM 1997), Geneva, Switzerland, pp. 129–136 (1997)Google Scholar
  13. 13.
    Lao, S., Sumi, Y., kawade, M., Tomita, F.: 3D template matching for pose invariant face recognition using 3D facial model built with isoluminance line based stereo vision. In: Proceedings of the Fifteenth International Conference on Pattern Recognition, Barcelona, Spain, pp. 2911–2916 (2000)Google Scholar
  14. 14.
    Chua, C.S., Han, F., Ho, Y.K.: 3D human face recognition using point signature. In: Proceedings of the Fourth International Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 233–238 (2000)Google Scholar
  15. 15.
    Kim, T.K., Kee, S.C., Kim, S.R.: Real-Time normalization and feature extraction of 3D face data using curvature characteristics. In: Proceedings of the Tenth IEEE International Workshop on Robot and Human Interactive Communication, Paris, France, pp. 74–79 (2001)Google Scholar
  16. 16.
    Nagamine, T., Uemura, T., Masuda, I.: 3D facial image analysis for human identification. In: Proceedings of the International Conference on Pattern Recognition, Amsterdam, Netherlands, pp. 324–327 (1992)Google Scholar
  17. 17.
    Beumier, C., Acheroy, M.: Automatic 3D face authentication. Image and Vision Computing 18(4), 315–321 (2000)CrossRefGoogle Scholar
  18. 18.
    Horn, B.K.P., Hilden, H.M., Negahdaripour, S.: Closed-form solution of absolute orientation using orthonormal matrices. J. Optical Soc. Am. 5, 1127–1135 (1988)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3D point sets. IEEE Trans. Pattern Analysis and Machine Intelligence 9, 698–700 (1987)CrossRefGoogle Scholar
  20. 20.
    Haralick, R.M., Joo, H.N., Lee, C.N., Zhuang, X., Vaidya, V.G., Kim, M.B.: Pose estimation from corresponding point data. IEEE Trans. On Systems, Man and Cybernetics 19(6), 1426–1446 (1989)CrossRefGoogle Scholar
  21. 21.
    Huang, T.S., Netravali, A.N.: Motion and structure from feature correspondences: A Review. Proceedings of the IEEE 82(2), 252–268 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Hwanjong Song
    • 1
  • Ukil Yang
    • 1
  • Kwanghoon Sohn
    • 1
  1. 1.Biometrics Engineering Research Center, Department of Electrical & Electronics EngineeringYonsei UniversitySeoulKorea

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