Advertisement

EigenExpress Approach in Recognition of Facial Expression Using GPU

  • Qi Wu
  • Mingli Song
  • Jiajun Bu
  • Chun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3979)

Abstract

The automatic recognition of facial expression presents a significant challenge to the pattern analysis and man-machine interaction research community. In this paper, a novel system is proposed to recognize human facial expressions based on the expression sketch. Firstly, facial expression sketch is extracted by an GPU-based real-time edge detection and sharpening algorithm from original gray image. Then, a statistical method, which is called Eigenexpress, is introduced to obtain the expression feature vectors for sketches. Finally, Modified Hausdorff distance(MHD) was used to perform the expression classification. In contrast to performing feature vector extraction from the gray image directly, the sketch based expression recognition reduces the feature vector’s dimension first, which leads to a concise representation of the facial expression. Experiment shows our method is appreciable and convincible.

Keywords

Facial Expression Graphic Process Unit Expression Recognition Facial Expression Recognition Graphic Hardware 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expression: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1424–1445 (2000)CrossRefGoogle Scholar
  2. 2.
    Ekman, P., Friesen, W.V.: Facial Action Coding System: Investigator’s Guide. Consulting Psychologists Press (1978)Google Scholar
  3. 3.
    Ekman, P.: Strong Evidence for Universals in Facial Expressions: A Reply to Russel’s Mistaken Critique. Psychological Bulletin 115(2), 268–287 (1994)CrossRefGoogle Scholar
  4. 4.
    Cohen, I., Sebe, N., Cozman, F.G., Cirelo, M.C., Huang, T.S.: Learning Bayesian Network Classifiers for Facial Expression Recognition with both Labeled and Unlabeled data. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 595–602 (2003)Google Scholar
  5. 5.
    Wilhelm, T., Backhaus, A.: Statistical and Neural Methods for Vision-based Analysis of Facial Expressions and Gender. IEEE International Conference on Systems, Man and Cybernetics 3, 2203–2208 (2004)Google Scholar
  6. 6.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  7. 7.
    Macedonia, M.: The Gpu Enters Computing’s Mainstream. IEEE Computer (October 2003)Google Scholar
  8. 8.
    Krüger, J., Westermann, R.: Linear Algebra Operators for GPU Implementation of Numerical Algorithms. In: ACM Siggraph 2003, pp. 908–916 (2003)Google Scholar
  9. 9.
    Ashikhmin, M.: A Tone Mapping Algorithm for High Contrast Images. In: Eurographics Workshop on Rendering, pp. 1–11 (2002)Google Scholar
  10. 10.
    NVIDIA Corporation, Industry’s Premiere Game Developers Proclaim NVIDIA GeForce 7800 GTX GPUs Platform of Choice for Next-Generation Game Development (2005)Google Scholar
  11. 11.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic, New York (1972)MATHGoogle Scholar
  12. 12.
    Kirby, M., Sirovich, L.: Application of the karhunen-loeve procedure for the characterization of human faces. IEEE Transaction on Pattern Analysis and Machine Intelligence 12, 103–108 (1990)CrossRefGoogle Scholar
  13. 13.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. PAMI 15(9), 9 (1993)CrossRefGoogle Scholar
  14. 14.
    Rucklidge, W.J.: Efficient Visual Recognition Using the Hausdorff Distance. Springer, Heidelberg (1996)CrossRefMATHGoogle Scholar
  15. 15.
    Song, M., Bu, J., Chen, C.: Expression Recognition from Video using A Coupled Hidden Markov Model. In: IEEE TENCON 2004 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qi Wu
    • 1
  • Mingli Song
    • 1
  • Jiajun Bu
    • 1
  • Chun Chen
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouPR China

Personalised recommendations