Advertisement

Facial Emotion Recognition Using Different Multi-resolution Transforms

  • Gyanendra K. Verma
  • U. S. Tiwary
  • Mahendra K. Rai
Part of the Communications in Computer and Information Science book series (CCIS, volume 192)

Abstract

The present work investigates the performance of different multi-resolution transforms in the application of emotion recognition from facial images. Multi-resolution analysis of image provides frequency information along with time information in different scale, orientation and locations. The emotion information from facial images was being captured by different multiresolution algorithm such as Wavelet Transform, Curvelet Transform and Contourlet Transform. Wavelet transform mainly approximate frequency information along with time whereas curvelet transform is best to capture edges information with very few coefficients. Various statistical features obtained from different algorithms have been used to build reference model. The classification part was done using support vector machine (SVM) and K-Nearest Neighbor (KNN) classifier with JAFFE, a Japanese facial emotion database. The individual as well as comparative study of different algorithms was done successfully.

Keywords

Multi-resolution transforms Emotion Recognition Curvelet Transform Wavelet Transform Contourlet Transform 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human computer interaction. IEEE Signal Process. Magazine 20, 569–571 (2001)Google Scholar
  2. 2.
    Galateia, I.: Emotional Facial Expressions recognition & classification. MS thesis, Delft University of Technology, Delft, NetherlandGoogle Scholar
  3. 3.
    Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36, 259–275 (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Delac, K., Grqic, M., Bartlett, M.S.: Recent advances in face recognition. In-Tech Publication, Crosia (2008)CrossRefGoogle Scholar
  5. 5.
    Chuang, Y., Yuning, H., Zhao, K.: The Method of Human Facial Expression Recognition Based on Wavelet Transformation Reducing the Dimension and Improved Fisher Discrimination. In: 3rd International Conference on Intelligent Networks and Intelligent Systems (ICINIS), pp. 43–47 (2010)Google Scholar
  6. 6.
    Zhi, R., Ruan, Q.: Robust Facial Expression Recognition Using Selected Wavelet Moment Invariants. In: WRI Global Congress on Intelligent Systems, GCIS 2009, pp. 508–512 (2009)Google Scholar
  7. 7.
    Muharram, M., Charkari, Moghaddam, N.: Multimodal information fusion application to human emotion recognition from face and speech. In: Multimedia Tools and Applications. LNCS, vol. 49(2), pp. 277–297. Springer, Heidelberg (1977)Google Scholar
  8. 8.
    Saha, A., Jonathan, Q.M.: Facial Expression Recognition using Curvelet based local binary patterns. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 2470–2473 (2010)Google Scholar
  9. 9.
    Wu, X., Zhao, J.: Curvelet feature extraction for face recognition and facial expression recognition. In: Sixth International Conference on Natural Computation (ICNC), pp. 1212–1216 (2010)Google Scholar
  10. 10.
    Lee, C.-C., Shih, C.-Y.: Facial expression recognition using contourlets and regularized discriminant analysis-based boosting algorithm. In: International Computer Symposium (ICS), pp. 1–5 (2010)Google Scholar
  11. 11.
    Shen, Y., Li, X., Ma, N.-W., Krishnan, S.: Parametric Time-Frequency Analysis and Its Applications in Music Classification. EURASIP Journal on Advances in Signal Processing 2010, Article ID 380349, 9 pages (2010)Google Scholar
  12. 12.
    Dai, D.-Q., Yan, H.: Wavelets and Face Recognition. I-Tech, Austria (2007)CrossRefGoogle Scholar
  13. 13.
    Tzanetakis, G., Essl, G., Cook, P.: Audio Analysis using the Discrete Wavelet Transform. In: Proc. Conf. in Acoustics and Music Theory Applications, Skiathos, Greece (2001)Google Scholar
  14. 14.
    Curvelet Literature, http://www.curvelet.org
  15. 15.
    Lajevardi, S.M., Hussain, Z.M.: Contourlet structural similarity for facial expression recognition. In: IEEE International Conference on Acoustics Speech and Signal Processing, pp. 1118–1121 (2010)Google Scholar
  16. 16.
    Sumana, I., Islam, M., Zhang, D.S., Lu, G.: Content Based Image Retrieval Using Curvelet Transform. In: Proc. of IEEE International Workshop on Multimedia Signal Processing, Cairns, Queensland, Australia, pp. 11–16 (2008)Google Scholar
  17. 17.
    Esakkirajan, S., Veerakumar, T., Murugan, V.S., Sudhakar, R.: Fingerprint Compression Using Contourlet Transform and Multistage Vector Quantization. International Journal of Biological and Life Sciences 1, 2 (2005)Google Scholar
  18. 18.
    Do, M.N., Vetterli, M.: Pyramidal directional filter banks and curvelets. In: Proc. of IEEE Int. Conf. on Image Processing, Thessaloniki, Greece, vol. 3, pp. 158–161 (2001)Google Scholar
  19. 19.
    Do, M.N., Vetterli, M.: Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Trans. on Image Processing (2001)Google Scholar
  20. 20.
    Lyons, M.J., Akamatsu, S., Kamachi, M., Goba, J.: Coding facial expressions with gabor wavelets. In: IEEE International Conference on Automatic Face and Gesture Recognition (1998)Google Scholar
  21. 21.
  22. 22.
    Verma, G.K., Prasad, S., Bakul, G.: Robust Face Recognition using Curvelet Transform. In: International Conference on Communication, Computing & Security. ACM, Rourkela (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gyanendra K. Verma
    • 1
  • U. S. Tiwary
    • 1
  • Mahendra K. Rai
    • 2
  1. 1.Indian Institute of Informaiton Technology, AllahabadAllahabadIndia
  2. 2.Gyanganga Institute of Technology and Science, JabalpurJabalpurIndia

Personalised recommendations