Advertisement

Cluster Computing

, Volume 21, Issue 1, pp 539–548 | Cite as

Facial expression recognition using histogram of oriented gradients based transformed features

  • Muhammad Nazir
  • Zahoor Jan
  • Muhammad SajjadEmail author
Article
  • 227 Downloads

Abstract

Facial expression recognition has been an emerging and long standing research problem in last two decades. Histograms of oriented gradients (HOGs) have proven to be an effective descriptor for preserving the local information using orientation density distribution and gradient of the edge. A robust powerful approach of HOG features has been investigated in this paper. In particular, this paper highlights that the transformation of HOG features to frequency domain can make this descriptor one of the most suitable to characterize illumination and orientation invariant facial expressions. Discrete cosine transform (DCT) is applied to transform the features into frequency domain and obtain the most important discriminant features. Finally, these features are fed to the well-known classifier to determine the underlying emotions from expressive facial images. To validate the proposed framework, we used MMI, Extended Cohn-Kanade dataset (CK+) and cross dataset. The results indicate that the proposed framework is better as compared to other methods in terms of classification accuracy rate with utilization of minimum number of features.

Keywords

Facial expression recognition Histograms of oriented gradients Discrete cosine transform Cross dataset 

References

  1. 1.
    Donia, M.M.F., Youssif, A.A.A., Hashad, A.: Spontaneous facial expression recognition based on histogram of oriented gradients descriptor. Comput. Inf. Sci. 7, 31–37 (2014)Google Scholar
  2. 2.
    Hamburger, C.: Quasimonotonicity, regularity and duality for nonlinear systems of partial differential equations. Ann. Mat. Pura Appl. 169, 321–354 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  4. 4.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. J. Pattern Recognit. 29, 51–59 (1996)CrossRefGoogle Scholar
  5. 5.
    Platt, J.C. : Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods. MIT Press, Cambridge, pp. 185–208 (1999)Google Scholar
  6. 6.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)Google Scholar
  7. 7.
    Kar, N.B., Babu, K.S., Jena, S.K. : Face expression recognition using histograms of oriented gradients with reduced features. In: Proceedings of International Conference on Computer Vision and Image Processing, pp. 209–219 (2016)Google Scholar
  8. 8.
    Liu, Y., Li, Y., Ma, X., Song, R.: Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas. Preprints (2017), 2017010102. doi: 10.20944/preprints201701.0102.v1
  9. 9.
    Happy, S.L., Routray, A.: Robust facial expression classification using shape and appearance features. In: Proceedings of 8th International Conference of Advances in Pattern Recognition (2015)Google Scholar
  10. 10.
    Wang, X., Jin, C., Liu, W., Hu, M., Xu, L., Ren, F.: Feature fusion of HOG and WLD for facial expression recognition. In: Proceedings of the IEEE/SICE International Symposium on System Integration, Kobe International Conference Center, Kobe, Japan, pp. 227–232 (2013)Google Scholar
  11. 11.
    Chen, J., Chen, Z., Chi, Z., Fu, H.: Facial expression recognition based on facial components detection and HOG features. In: Scientific Cooperations International Workshops on Electrical and Computer Engineering Subfields, pp. 64–69 (2014)Google Scholar
  12. 12.
    Fan, X., Tjahjadi. T.: A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences. J. Pattern Recognit. 48(11), 3407–3416 (2015)Google Scholar
  13. 13.
    Khan, R.A., Meyer, A., Konik, H., Bouakaz, S.: Framework for reliable, real-time facial expression recognition for low resolution images. J. Pattern Recognition Letters 34(10), 1159–1168 (2013)CrossRefGoogle Scholar
  14. 14.
    Khan, S.A., Hussain, A., Usman, M.: Reliable facial expression recognition for multi-scale images using weber local binary image based cosine transform features. Multimed. Tools Appl. (2017). doi: 10.1007/s11042-016-4324-z
  15. 15.
    Zhong, L., Jinsha, Y., Hong, Y., Ke, Z. : Wireless communications, networking and mobile computing. In: WiCOM 2008 4th International Conference, pp. 1–4 (2008)Google Scholar
  16. 16.
    Viola, P. Jones, M. : Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’01). 1, 511,(2001)Google Scholar
  17. 17.
    Dollar, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: Proceedings of the British Machine Vision Conference, pp. 68.1–68.11 (2010)Google Scholar
  18. 18.
    Lucey, P., Cohn, J.F., Kanade, T., Saragih, T., Ambadar, Z., Matthews, I.: The Extended Cohn-Kanade Dataset (CK+) A complete dataset for action unit and emotion-specified expression. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 94–101 (2010)Google Scholar
  19. 19.
    Pantic, M., Valstar, M., Radermaker, R., Maat, L.: Web-based database for facial expression analysis. In: Proceedings of the 13th ACM International Conference on Multimedia, pp. 317–321 (2005)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Digital Image Processing Laboratory, Department of Computer ScienceIslamia CollegePeshawarPakistan
  2. 2.Department of Computer Science and EngineeringHITEC UniversityTaxilaPakistan

Personalised recommendations