Facial Expression Recognition Using Modified Local Binary Pattern

  • Suparna Biswas
  • Jaya Sil
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)


In this paper a low computation feature space has been proposed to recognize expressions of face images. The image is divided into number of blocks and binary pattern corresponding to each block is generated by modifying the Local Binary Pattern (LBP). The proposed method generates compressed binary pattern of images and therefore, reduced in size. Features are extracted from transformed image using block wise histograms with variable number of bins. For classification we use two techniques, template matching and Support Vector Machine (SVM). Experiments on face images with different resolutions show that the proposed approach performs well for low resolution images. Considering Cohn-Kanade database, the proposed method is compared with LBP feature based methods demonstrating better performance.


Facial expression Template matching Local binary pattern 


  1. 1.
    Shan, C., Gong, S., McOwan, P.W.: Robust facial expression recognition using local binary patterns. Image Process. ICIP 2 2, 370–373 (2005)Google Scholar
  2. 2.
    Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27, 803–816 (2009)CrossRefGoogle Scholar
  3. 3.
    Bartlett, M.S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., Movellan, J.: Recognizing facial expression: machine learning and application to spontaneous behavior. In: IEEE conference on computer vision and pattern recognition (2005)Google Scholar
  4. 4.
    Yeasi, M., Bullot, B., Sharma, R.: Recognition of facial expressions and measurement of levels of interest from video. IEEE Trans. Multimedia 8(3), 500–508 (2006)CrossRefGoogle Scholar
  5. 5.
    Thai, L.H., Nguyen, N.D.T., Hai, T.S.: A facial expression classification system integrating canny, principal component analysis and artificial neural network. Int. J. Mach. Learn. Comput. 1(4) (2011)Google Scholar
  6. 6.
    Sumathi1, C.P., Santhanam2, T., Mahadevi, M.: Automatic facial expression analysis a survey. Int. J. Comput. Sci. Eng. Surv. (IJCSES) 3(6) (2012)Google Scholar
  7. 7.
    Kalita, J., Das, K.: Recognition of facial expression using eigenvector based distributed features and euclidean distance based decision making technique. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 4(2) (2013) Google Scholar
  8. 8.
    Sarode, N., Bhatia, S.: Facial expression recognition. Int. J. Comput. Sci. Eng. 2(5), 1552–1557 (2010)Google Scholar
  9. 9.
    Shih, F.Y., Chuang, C.H., Wang, P.S.P.: Performance comparisons of facial expression recognition in jaffe database. Int. J. Pattern. Recogn. Artif. Intell. 22(3), 445–459 (2008)CrossRefGoogle Scholar
  10. 10.
    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. Int. J. Inf. Technol. 11(11), 86–96 (2005)Google Scholar
  11. 11.
    Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11(4), 467–476 (2002)CrossRefGoogle Scholar
  12. 12.
    A new approach of facial expression recognition based on Contourlet Transform, wavelet analysis and pattern recognition. ICWAPR, pp. 275–280 (2009)Google Scholar
  13. 13.
    Suresh, R., Audithan, S.: Contourlet transform based human emotion recognition system. Int. J. Sig. Process. Syst. 2(1) (2014)Google Scholar
  14. 14.
    Lajevardi, S.M., Hussain, Z.M.: Contourlet structural similarity for facial expression recognition. ICASSP (2010)Google Scholar
  15. 15.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. CVPR, Kauai (2001)Google Scholar
  16. 16.
    Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. IEEE FG, pp. 46–53 (2000)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringGurunanak Institute of TechnologyKolkataIndia
  2. 2.Department of Computer Science and TechnologyIndian Institute of Engineering Science and Technology UniversityHowrahIndia

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