Robust 3D Face Recognition from Expression Categorisation

  • Jamie Cook
  • Mark Cox
  • Vinod Chandran
  • Sridha Sridharan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


The task of Face Recognition is often cited as being complicated by the presence of lighting and expression variation. In this article a novel combination of facial expression categorisation and 3D Face Recognition is used to provide enhanced recognition performance. The use of 3D face data alleviates performance issues related to pose and illumination. Part-face decomposition is combined with a novel adaptive weighting scheme to increase robustness to expression variation. By using local features instead of a monolithic approach, this system configuration allows for expression variability to be modelled and aid in the fusion process. The system is tested on the Face Recognition Grand Challenge (FRGC) database, currently the largest available dataset of 3D faces. The sensitivity of the proposed approach is also evaluated in the presence of systematic error in the expression classification stage.


Facial Expression Face Recognition IEEE Computer Society Independent Component Analysis Iterative Close 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.
    Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys (CSUR) 35(4), 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: CVPR 2005. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, vol. 1, pp. 947–954. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  3. 3.
    Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Computer Vision and Image Understanding 101(1), 1–15 (2006)CrossRefGoogle Scholar
  4. 4.
    Pantic, M., Rothkrantz, L.: Toward an affect-sensitive multimodal human-computer interaction. Proceedings of the IEEE 91(9), 1370–1390 (2003)CrossRefGoogle Scholar
  5. 5.
    Li, X., Mori, G., Zhang, H.: Expression-Invariant Face Recognition with Expression Classification. In: CRV 2006. The 3rd Canadian Conference on Computer and Robot Vision, p. 77 (2006)Google Scholar
  6. 6.
    Chang, K.I., Bowyer, K., Flynn, P.: Adaptive rigid multi-region selection for handling expression variation in 3d face recognition. In: Computer Vision and Pattern Recognition. 2005 IEEE Computer Society Conference, vol. 3, p. 157. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  7. 7.
    Martinez, A.: Recognizing imprecisely localized, partially occluded, andexpression variant faces from a single sample per class. Pattern Analysis and Machine Intelligence, IEEE Transactions 24(6), 748–763 (2002)CrossRefGoogle Scholar
  8. 8.
    Li, C., Barreto, A.: An integrated 3D face-expression recognition approach. In: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference, vol. 3, p. 1132. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  9. 9.
    Lucey, S., Sridharan, S., Chandran, V.: Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis. EURASIP Journal on Applied Signal Processing 2003(3), 264–275 (2003)CrossRefGoogle Scholar
  10. 10.
    Donato, G., Bartlett, M., Hager, J., Ekman, P., Sejnowski, T.: Classifying Facial Actions. Pattern Analysis and Machine Intelligence, IEEE Transactions 21(10), 974–989 (1999)CrossRefGoogle Scholar
  11. 11.
    Maurer, T., Guigonis, D., Maslov, I., Pesenti, B., Tsaregorodtsev, A., West, D., Medioni, G.: Performance of Geometrix ActiveIDTM3D Face Recognition Engine on the FRGC Data. In: CVPR 2005. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  12. 12.
    Bartlett, M., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., Movellan, J.: Recognizing facial expression: machine learning and application to spontaneous behavior. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 568–573. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  13. 13.
    Brunelli, R., Poggio, T.: Face Recognition: Features versus Templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)CrossRefGoogle Scholar
  14. 14.
    Lucey, S., Chen, T.: Face recognition through mismatch driven representations of the face. In: Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop, pp. 193–199. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar
  15. 15.
    Cook, J., Chandran, V., Fookes, C.: 3D Face Recognition using Log-Gabor Templates. In: BMVC. British Machine Vision Conference (September 2006)Google Scholar
  16. 16.
    Shepherd, J., Davies, G., Ellis, H.: Studies of cue saliency. Perceiving and Remembering Faces, 105–131 (1981)Google Scholar
  17. 17.
    Gokberk, B., Akarun, L., Alpaydin, E.: Feature selection for pose invariant face recognition. In: Proceedings of the 16th International Conference on Pattern Recognition (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jamie Cook
    • 1
  • Mark Cox
    • 1
  • Vinod Chandran
    • 1
  • Sridha Sridharan
    • 1
  1. 1.Speech, Audio, Image and Video Technology (SAIVT) Laboratory, Queensland University of Technology, Brisbane, Queensland, 4000Australia

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