Human Emotion Recognition and Classification from Digital Colour Images Using Fuzzy and PCA Approach

Part of the Advances in Intelligent Systems and Computing book series (volume 167)

Abstract

In this paper, we proposed a new model for recognizing various emotions of humans with different age groups and gender. Fuzzy is used for extracting more accurate region of interest, i.e., face. The dimensionality of face image is reduced by the Principal Component Analysis (PCA) [12] and finally emotion is recognized and classified using Euclidean Distance. Database is prepared and some performance metrics like recognition-rate v/s Eigen-range has been calculated. The proposed method was also tested on FACES Collection database [13]. The experiment results demonstrate that the emotion recognition system has been successful with average recognition rate of 96.66% (with both experiment databases) when approximately or more than 60% eigenfaces used. It is also shown that database can be easily expanded to classify faces and non faces images.

Keywords

Emotion Recognition Fuzzy logic Principal component analysis (PCA) Euclidean Distance Eigen Range 

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References

  1. 1.
    Deng, H.-B., Jin, L.-W., Zhen, L.-X., Huang, I.-C.: A New Facial Ex-pression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA. International Journal of Information Technology 11(11), 86–96 (2005)Google Scholar
  2. 2.
    Amir, J.: A Learning Fuzzy Model for Emotion Recognition. European Journal of Scientific Research 57(2), 206–211 (2011)Google Scholar
  3. 3.
    Kaur, M., Vashisht, R., Nirvair, N.: Recognition of Facial Expressions with Principal Component Analysis and Singular Value Decomposition. International Journal of Computer Applications 9(12), 36–40 (2010)CrossRefGoogle Scholar
  4. 4.
    Kharat, G.U., Dudul, S.V.: Emotion Recognition from Facial Expression Using Neural Networks. In: HIS, Krakow, Poland, May 25-27. IEEE (2008)Google Scholar
  5. 5.
    Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE PAMI 12(1), 103–108 (1990)CrossRefGoogle Scholar
  6. 6.
    Kishore, K.V.K., Varma, G.P.S.: Efficient Facial Emotion Classification with Wavelet Fusion of Multi Features. IJCSNS International Journal of Computer Science and Network Security 11(8) (2011)Google Scholar
  7. 7.
    Kosaka, Y., Kotani, K.: Facial Expression Analysis by Kernal Eigen Space Method based on Class Features (KEMC) Using Non-Linear Basis For Separation of Ex-pression Classes. In: International Conference on Image Processing, ICIP (2004)Google Scholar
  8. 8.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A Optics and Image Science 4(3), 519–524 (1987)CrossRefGoogle Scholar
  9. 9.
    Moriyama, T., Kanade, T., Xiao, J., Cohn, J.F.: Meticu-lously Detailed Eye region Model and It’s Application to Analysis of Facial Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5) (2006)Google Scholar
  10. 10.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  11. 11.
    Yuille, A.L., Cohen, D.S., Hallinan, P.W.: Feature extraction from faces using deformable templates. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1989, June 4-8, pp. 104–109 (1989)Google Scholar
  12. 12.
    Dimitri, P.: Eigenface-based facial recognition (February 2003)Google Scholar
  13. 13.
    http://faces.mpdl.mpg.de/album/escidoc:57488 for downloading the FACE Collection database

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Electronics EngineeringCollege of EngineeringRoorkeeIndia
  2. 2.Department of Electronics EngineeringDITDehradunIndia

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