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Enhancing 3D Face Recognition by Combination of Voiceprint

  • Yueming Wang
  • Gang Pan
  • Yingchun Yang
  • Dongdong Li
  • Zhaohui Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)

Abstract

This paper investigates the enhancement of identification performance when using voice classifier to help 3D face recognition. 3D face recognition is well known for its being superior to 2D due to the invariance in illumination, make-ups and pose. However, it is still challenged by expression variance. The partial ICP method we used for 3D face recognition could implicitly and dynamically extract the rigid parts of facial surface and be able to get much better performance than other methods in 3D face recognition under expression changes. This work serves to further improve the performance of recognition by combining a voiceprint classifier into partial ICP method. We implement 9 combination schemes, and experiments on database of 360 models with 40 subjects, 9 3D face scans with four different kinds of expression and 9 sessions of utterance for each subject, shows improvement of performance is very promising.

Keywords

Face Recognition Gaussian Mixture Model Fusion Method Fusion Scheme Speaker Recognition 
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.

References

  1. 1.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Computing Surveys 35(4), 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Zhao, W., Chellappa, R.: Illumination-insensitive face recognition using symmetric shape-form-shading. In: Proc. IEEE ICCV, vol. 1, pp. 286–293 (2000)Google Scholar
  3. 3.
    Chang, K., Bowyer, K., Flynn, P.: Effects on Facial Expression in 3D Face Recognition. In: SPIE Conference on Biometric Technology for Human Identification (April 2005)Google Scholar
  4. 4.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans.Pattern Anal.Mach.Intell 14, 239–256 (1992)CrossRefGoogle Scholar
  5. 5.
    Bowyer, K., Change, K., Flynn, P.: A short survey fo 3D and multi-modal 3D+2D face recognition. In: IEEE ICPR (2004)Google Scholar
  6. 6.
    Face Recognition Vendor Test (2002), http://www.frvt.org/
  7. 7.
    Wang, Y.M., Pan, G., Wu, Z.H.: Exploring Facial Expression Effects in 3D Face Recognition using Partial ICP. In: IEEE ACCV, Oral (2006) (to Appear)Google Scholar
  8. 8.
    Reynolds, D.A., Rose, R.C.: Robust Text-independent Speaker Identification Using Gaussion Mixture Speaker Models. IEEE Transactions on Speech and Audio Processing, 3–1 (1995)Google Scholar
  9. 9.
    Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision templates for multiple classifier fusion: An Experimental Comparison. Pattern Recognition 34(2), 299–314 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Dempster, A., Laird, N., Rubin, D.: Maximum liklihood from incomplete data via the EM algorithm. J.Royal Stat.Soc. 39, 1–38 (1977)MATHMathSciNetGoogle Scholar
  11. 11.
    Li, D.D., Yang, Y.C., Wu, Z.H.: Combining Voiceprint and Face Biometrics for Speaker Identification Using SDWS. To appeared in Interspeech 2005 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yueming Wang
    • 1
  • Gang Pan
    • 1
  • Yingchun Yang
    • 1
  • Dongdong Li
    • 1
  • Zhaohui Wu
    • 1
  1. 1.Department of Computer Science and EngineeringZhejiang UniversityHangzhouP.R. China

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