GCV-Based Regularized Extreme Learning Machine for Facial Expression Recognition

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Abstract

Extreme learning machine (ELM) with a single-layer feed-forward network (SLFN) has acquired overwhelming attention. The structure of ELM has to be optimized through the incorporation of regularization to gain convenient results, and the Tikhonov regularization is frequently used. Regularization benefits in improving the generalized performance than traditional ELM. The estimation of regularization parameter mainly follows heuristic approaches or some empirical analysis through prior experience. When such a choice is not possible, the generalized cross-validation (GCV) method is one of the most popular choices for obtaining optimal regularization parameter. In this work, a new method of facial expression recognition is introduced where histogram of oriented gradients (HOG) feature extraction and GCV-based regularized ELM are applied. Experimental results on facial expression database JAFFE demonstrate promising performance which outperforms the other two classifiers, namely support vector machine (SVM) and k-nearest neighbor (KNN).

Keywords

Facial expression recognition (FER) Extreme learning machine (ELM) Regularization Generalized cross-validation (GCV) Classification 

References

  1. 1.
    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 Computer Society Conference on Computer Vision and Pattern Recognition, 2005, CVPR 2005, vol. 2, pp. 568–573. IEEE (2005)Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  3. 3.
    Deshmukh, S., Patwardhan, M., Mahajan, A.: Survey on real-time facial expression recognition techniques. IET Biom. 5(3), 155–163 (2016)Google Scholar
  4. 4.
    Ekman, P., Friesen, W.V.: Facial action coding system (1977)Google Scholar
  5. 5.
    Ekman, P., Oster, H.: Facial expressions of emotion. Annu. Rev. Psychol. 30(1), 527–554 (1979)CrossRefGoogle Scholar
  6. 6.
    Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recognit. 36(1), 259–275 (2003)CrossRefGoogle Scholar
  7. 7.
    Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Statist. Softw. 33(1), 1 (2010)CrossRefGoogle Scholar
  8. 8.
    Golub, G.H., Heath, M., Wahba, G.: Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21(2), 215–223 (1979)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Huang, G.B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70(16), 3056–3062 (2007)CrossRefGoogle Scholar
  10. 10.
    Huang, G.B., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16), 3460–3468 (2008)CrossRefGoogle Scholar
  11. 11.
    Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)CrossRefGoogle Scholar
  12. 12.
    Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(2), 513–529 (2012)Google Scholar
  13. 13.
    Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998. Proceedings, pp. 200–205. IEEE (1998)Google Scholar
  14. 14.
    MartíNez-MartíNez, J.M., Escandell-Montero, P., Soria-Olivas, E., MartíN-Guerrero, J.D., Magdalena-Benedito, R., GóMez-Sanchis, J.: Regularized extreme learning machine for regression problems. Neurocomputing 74(17), 3716–3721 (2011)CrossRefGoogle Scholar
  15. 15.
    Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)CrossRefGoogle Scholar
  16. 16.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427–437 (2009)CrossRefGoogle Scholar
  17. 17.
    Tian, Y.I., Kanade, T., Cohn, J.F.: Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 97–115 (2001)CrossRefGoogle Scholar
  18. 18.
    Tian, Y., Kanade, T., Cohn, J.F.: Facial expression recognition. In: Handbook of Face Recognition, pp. 487–519. Springer (2011)CrossRefGoogle Scholar
  19. 19.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  20. 20.
    Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.National Institute of Technology GoaPondaIndia

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