Selecting, Optimizing and Fusing ‘Salient’ Gabor Features for Facial Expression Recognition

  • Ligang Zhang
  • Dian Tjondronegoro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5863)


This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.


Facial expression recognition Gabor filter (2D)2PCA KNN 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Deng, H.B., Jin, L.W., Zhen, L.X., Huang, J.C.: A new facial expression recognition method based on local gabor filter bank and pca plus lda. International Journal of Information Technology 11, 86–96 (2005)Google Scholar
  2. 2.
    Caifeng, S., Shaogang, G., McOwan, P.W.: Robust facial expression recognition using local binary patterns. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. II-370–II373 (2005)Google Scholar
  3. 3.
    Wei Feng, L., ZengFu, W.: Facial Expression Recognition Based on Fusion of Multiple Gabor Features. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 536–539 (2006)Google Scholar
  4. 4.
    Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 200–205 (1998)Google Scholar
  5. 5.
    Bashyal, S., Venayagamoorthy, G.K.: Recognition of facial expressions using Gabor wavelets and learning vector quantization. Engineering Applications of Artificial Intelligence 21, 1056–1064 (2008)CrossRefGoogle Scholar
  6. 6.
    Chen, H.Y., Huang, C.L., Fu, C.M.: Hybrid-boost learning for multi-pose face detection and facial expression recognition. Pattern Recognition 41, 1173–1185 (2008)zbMATHCrossRefGoogle Scholar
  7. 7.
    Littlewort, G., Bartlett, M.S., Fasel, I., Susskind, J., Movellan, J.: Dynamics of facial expression extracted automatically from video. Image and Vision Computing 24, 615–625 (2006)CrossRefGoogle Scholar
  8. 8.
    Yu, J., Bhanu, B.: Evolutionary feature synthesis for facial expression recognition. Pattern Recognition Letters 27, 1289–1298 (2006)CrossRefGoogle Scholar
  9. 9.
    Gunes, T., Polat, E.: Feature selection for multi-SVM classifiers in facial expression classification. In: 23rd International Symposium on Computer and Information Sciences, ISCIS 2008, pp. 1–5 (2008)Google Scholar
  10. 10.
    Lajevardi, S.M., Lech, M.: Facial Expression Recognition Using Neural Networks and Log-Gabor Filters. In: Digital Image Computing: Techniques and Applications, DICTA 2008, pp. 77–83 (2008)Google Scholar
  11. 11.
    Kong, A.: An evaluation of Gabor orientation as a feature for face recognition. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 (2008)Google Scholar
  12. 12.
    Dadgostar, M., Tabrizi, P.R., Fatemizadeh, E., Soltanian-Zadeh, H.: Feature Extraction Using Gabor-Filter and Recursive Fisher Linear Discriminant with Application in Fingerprint Identification. In: Seventh International Conference on Advances in Pattern Recognition, ICAPR 2009, pp. 217–220 (2009)Google Scholar
  13. 13.
    Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust Object Recognition with Cortex-Like Mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 411–426 (2007)CrossRefGoogle Scholar
  14. 14.
    Kong, H., Wang, L., Teoh, E.K., Li, X., Wang, J.-G., Venkateswarlu, R.: Generalized 2D principal component analysis for face image representation and recognition. Neural Networks 18, 585–594 (2005)CrossRefGoogle Scholar
  15. 15.
    Zhang, D., Zhou, Z.-H. (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing 69, 224–231 (2005)CrossRefGoogle Scholar
  16. 16.
    Kanade, T., Cohn, J.F., Yingli, T.: Comprehensive database for facial expression analysis. In: Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–53 (2000)Google Scholar
  17. 17.
    Liang, D., Yang, J., Zheng, Z., Chang, Y.: A facial expression recognition system based on supervised locally linear embedding. Pattern Recognition Letters 26, 2374–2389 (2005)CrossRefGoogle Scholar
  18. 18.
    Guo, G., Dyer, C.R.: Learning from examples in the small sample case: face expression recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B 35, 477–488 (2005)CrossRefGoogle Scholar
  19. 19.
    Wang, J., Yin, L.: Static topographic modeling for facial expression recognition and analysis. Comput. Vis. Image Underst. 108, 19–34Google Scholar
  20. 20.
    Wong, J.-J., Cho, S.-Y.: A face emotion tree structure representation with probabilistic recursive neural network modeling. Neural Computing & Applications (2008)Google Scholar
  21. 21.
    Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vision Computing 27, 803–816 (2009)CrossRefGoogle Scholar
  22. 22.
    Yongmian, Z., Qiang, J., Zhiwei, Z., Beifang, Y.: Dynamic Facial Expression Analysis and Synthesis With MPEG-4 Facial Animation Parameters. IEEE Transactions on Circuits and Systems for Video Technology 18, 1383–1396 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ligang Zhang
    • 1
  • Dian Tjondronegoro
    • 1
  1. 1.Faculty of Science and TechonoglyQueensland University of TechonoglyBrisbaneAustralia

Personalised recommendations