Advertisement

Dictionary Based Approach for Facial Expression Recognition from Static Images

  • Krishan SharmaEmail author
  • Renu Rameshan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)

Abstract

We present a simple approach for facial expression recognition from images using the principle of sparse representation using a learned dictionary. Visual appearance based feature descriptors like histogram of oriented gradients (HOG), local binary patterns (LBP) and eigenfaces are used. We use Fisher discrimination dictionary which has discrimination capability in addition to being reconstructive. The classification is based on the fact that each expression class with in the dictionary spans a subspace and these subspaces have non-overlapping directions so that they are widely separated. Each test feature point has a sparse representation in the union of subspaces of dictionary formed by labeled training points. To check recognition performance of the proposed approach, extensive experimentation is done over Jaffee and CK databases. Results show that the proposed approach has better classification accuracy than state-of-the-art techniques.

Keywords

Histogram of oriented gradient Local binary pattern Eigenfaces Dictionary learning Sparse representation Facial expression recognition 

References

  1. 1.
    Mehrabian, A.: Nonverbal Communication. Transaction Publishers, Piscataway (1972)Google Scholar
  2. 2.
    Ekman, P., Rolls, E., Perrett, D., Ellis, H.: Facial expressions of emotion: an old controversy and new findings [and discussion]. Philos. Trans. R. Soc. B: Biol. Sci. 335(1273), 63–69 (1992)CrossRefGoogle Scholar
  3. 3.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Discriminative learned dictionaries for local image analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. IEEE, pp. 1–8 (2008)Google Scholar
  4. 4.
    Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311 (2006)CrossRefzbMATHGoogle Scholar
  5. 5.
    Zhang, Q., Li, B.: Discriminative K-SVD for dictionary learning in face recognition. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 2691–2698 (2010)Google Scholar
  6. 6.
    Liu, W., Song, C., Wang, Y.: Facial expression recognition based on discriminative dictionary learning. In: 2012 21st International Conference on Pattern Recognition (ICPR). IEEE, pp. 1839–1842 (2012)Google Scholar
  7. 7.
    Chen, H.C., Comiter, M.Z., Kung, H.T., McDanel, B.: Sparse coding trees with application to emotion classification. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 77–86, June 2015Google Scholar
  8. 8.
    Guo, L.: Smile expression classification using the improved BIF feature. In: 2011 Sixth International Conference on Image and Graphics (ICIG), pp. 783–788, August 2011Google Scholar
  9. 9.
    Cotter, S.F.: Weighted voting of sparse representation classifiers for facial expression recognition. In: 2010 18th European Signal Processing Conference. IEEE, pp. 1164–1168 (2010)Google Scholar
  10. 10.
    Yang, M., Zhang, L., Feng, X., Zhang, D.: Fisher discrimination dictionary learning for sparse representation. In: 2011 International Conference on Computer Vision. IEEE, pp. 543–550 (2011)Google Scholar
  11. 11.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  12. 12.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1991. IEEE, pp. 586–591 (1991)Google Scholar
  14. 14.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    Harandi, M., Sanderson, C., Shen, C., Lovell, B.C.: Dictionary learning and sparse coding on Grassmann manifolds: an extrinsic solution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3120–3127 (2013)Google Scholar
  16. 16.
    Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J., Budynek, J.: The Japanese Female Facial Expression (JAFFE) Database (1998)Google Scholar
  17. 17.
    Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops. IEEE, pp. 94–101 (2010)Google Scholar
  18. 18.
    Liao, C.-T., Chuang, H.-J., Duan, C.-H., Lai, S.-H.: Learning spatial weighting via quadratic programming for facial expression analysis. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops. IEEE, pp. 86–93 (2010)Google Scholar
  19. 19.
    Oliveira, L., Mansano, M., Koerich, A., de Souza Britto Jr., A.: 2D principal component analysis for face and facial-expression recognition. Comput. Sci. Eng. 13(3), 9–13 (2011)CrossRefGoogle Scholar
  20. 20.
    Cheng, F., Yu, J., Xiong, H.: Facial expression recognition in Jaffe dataset based on Gaussian process classification. IEEE Trans. Neural Netw. 21(10), 1685–1690 (2010)CrossRefGoogle Scholar
  21. 21.
    Lubing, Z., Han, W.: Local gradient increasing pattern for facial expression recognition. In: 19th IEEE International Conference on Image Processing (ICIP) (2012)Google Scholar
  22. 22.
    Benli, K.S., Eskil, M.T.: Extraction and selection of muscle based features for facial expression recognition. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1651–1656, August 2014Google Scholar
  23. 23.
    Cheon, Y., Kim, D.: Natural facial expression recognition using differential-aam and manifold learning. Pattern Recogn. 42(7), 1340–1350 (2009)CrossRefzbMATHGoogle Scholar
  24. 24.
    Yang, P., Liu, Q., Metaxas, D.N.: Rankboost with L1 regularization for facial expression recognition and intensity estimation (2009)Google Scholar
  25. 25.
    Taheri, S., Qiu, Q., Chellappa, R.: Structure-preserving sparse decomposition for facial expression analysis. IEEE Trans. Image Process. 23(8), 3590–3603 (2014)CrossRefMathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computing and Electrical EngineeringIndian Institute of TechnologyMandiIndia

Personalised recommendations