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Fast and robust online-learning facial expression recognition and innate novelty detection capability of extreme learning algorithms

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Abstract

Facial Expression Recognition (FER) is a task usually framed as predicting an emotional state given a facial image. FER has received numerous attentions from both business and academia as it could serve as a crucial component in various applications, e.g., automatic evaluation of customer satisfaction, sign language recognition, and human–computer interaction. Despite active research on the subject, facial expression recognition remains greatly challenging due to the diversity of individual expressions, shapes, and sizes of face, eyes, mouth, and other facial features, as well as orientation, alignment, and lighting. In this paper, we aim to improve the fast and robust online-learning FER with unseen data identification. We compare two widely used feature extraction methods for FER, namely Curvelet Transform (CT) and Local Curvelet Transform (LCT). Furthermore, we explore factors underlying several online extreme learning approaches for unseen data identification. Our experimental results demonstrate that (1) CT is suitable for a cleaner and well-prepared dataset, while LCT seems to work well on a dataset with diverse quality and on level of consistency. (2) The Identity Structural Tolerance Sequential Circular Extreme Learning Machine outperforms other Extreme Learning algorithms employed in FER. (3) LC can provide unseen identification capability to Extreme Learning algorithms. These findings emphasize the common underlying foundation between the extreme learning approach and other traditional learning approaches.

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References

  1. Anderson, K., McOwan, P.W.: A real-time automated system for the recognition of human facial expressions. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(1), 96–105 (2006)

    Article  Google Scholar 

  2. Apolinário, J.A., Apolinário, J.A., Rautmann, R.: QRD-RLS Adaptive Filtering. Springer, Berlin (2009)

    MATH  Google Scholar 

  3. Atsawaraungsuk, S., Kantanyukul, T., Polpinit, P.: Identity activation structural tolerance online sequential circular extreme learning machine for high dimensional data. Eng. Appl. Sci. Res. 2(46), 120–129 (2019)

    Google Scholar 

  4. Atsawaraungsuk, S., Tatpong, K.: Sin activation structural tolerance online sequential circular extreme learning machine for classification problem. Int. J. Technol. 8(4), 601–610 (2017)

    Article  Google Scholar 

  5. Atsawaruangsuk, S., Katanyukul, T., Polpinit, P.: Analyze facial expression recognition based on curvelet transform via extreme learning machine. Presented at the (Springer (2019).)

  6. Baxes, G.A.: Digital Image Processing: Principles and Applications. Wiley, New York (1994)

    Google Scholar 

  7. Biswas, S., Sil, J.:ACM,: An efficient expression recognition method using contourlet transform. Presented at the (2015)

  8. Candès, E.J., Donoho, D.L.: New tight frames of curvelets and optimal representations of objects with piecewise c2 singularities. Commun. Pure Appl. Math. J. Issued Courant Instit. Math. Sci. 57(2), 219–266 (2004)

    Article  Google Scholar 

  9. Cossetin, M.J., Nievola, J.C., Koerich, A.L.: In: Facial expression recognition using a pairwise feature selection and classification approach, pp. 5149–5155. IEEE (2016)

  10. Dahmane, M., Meunier, J.: Prototype-based modeling for facial expression analysis. IEEE Trans. Multimedia 16(6), 1574–1584 (2014)

    Article  Google Scholar 

  11. Decherchi, S., Gastaldo, P., Zunino, R., Cambria, E., Redi, J.: Circular-elm for the reduced-reference assessment of perceived image quality. Neurocomputing 102, 78–89 (2013)

    Article  Google Scholar 

  12. Eckman, P.: Universal and cultural differences in facial expression of emotion. In: Nebraska symposium on motivation, vol. 19, pp. 207–284 (1972)

  13. Golub, G.H., Van Loan, C.F.: Matrix Computations, vol. 3. JHU Press, Baltimore (2012)

    MATH  Google Scholar 

  14. Gu, W., Xiang, C., Venkatesh, Y., Huang, D., Lin, H.: Facial expression recognition using radial encoding of local gabor features and classifier synthesis. Pattern Recogn. 45(1), 80–91 (2012)

    Article  Google Scholar 

  15. Happy, S., Routray, A.: Automatic facial expression recognition using features of salient facial patches. IEEE Trans. Affective Comput. 6(1), 1–12 (2015)

    Article  Google Scholar 

  16. Horata, P., Chiewchanwattana, S., Sunat, K.: Enhancement of online sequential extreme learning machine based on the householder block exact inverse qrd recursive least squares. Neurocomputing 149, 239–252 (2015)

    Article  Google Scholar 

  17. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)

    Article  Google Scholar 

  18. Jobanputra, M., Chaudhary, A., Shah, S., Gandhi, R.: In: Real-time face recognition in hd videos: algorithms and framework, pp. 1–8. IEEE (2018)

  19. Kumar, S., Bhuyan, M.K., Chakraborty, B.K.: Extraction of informative regions of a face for facial expression recognition. IET Computer Vision 10(6), 567–576 (2016)

    Article  Google Scholar 

  20. Liang, N.Y., Huang, G.B., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Networks 17(6), 1411–1423 (2006)

    Article  Google Scholar 

  21. Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: In: The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression, pp. 94–101. IEEE (2010)

  22. Lyons, M.: Facial expression database: Japanese female facial expression (jaffe) database. http://www.kasrl.org/jaffe.html (1998)

  23. Mohammed, A.A., Minhas, R., Wu, Q.J., Sid-Ahmed, M.A.: Human face recognition based on multidimensional pca and extreme learning machine. Pattern Recogn. 44(10–11), 2588–2597 (2011)

    Article  Google Scholar 

  24. Moonen, M., Vandewalle, J.: A square root covariance algorithm for constrained recursive least squares estimation. J. VLSI Signal Process. Syst. Signal Image Video Technol. 3(3), 163–172 (1991)

    Article  Google Scholar 

  25. Nakjai, P., Katanyukul, T.: Automatic hand sign recognition: Identify unusuality through latent cognizance. In: IAPR Workshop on Artificial Neural Networks in Pattern Recognition, pp. 255–267. Springer (2018)

  26. Revina, I.M., Emmanuel, W.S.: A survey on human face expression recognition techniques. Journal of King Saud University-Computer and Information Sciences (2018)

  27. Salmam, F.Z., Madani, A., Kissi, M.: Facial expression recognition using decision trees. In: 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), pp. 125–130. IEEE (2016)

  28. Shi, W., Gong, Y., Tao, X., Wang, J., Zheng, N.: Improving cnn performance accuracies with min-max objective. IEEE Trans. Neural Netw. Learning Syst. 29(7), 2872–2885 (2017)

    MathSciNet  Google Scholar 

  29. Siddiqi, M.H., Ali, R., Idris, M., Khan, A.M., Kim, E.S., Whang, M.C., Lee, S.: Human facial expression recognition using curvelet feature extraction and normalized mutual information feature selection. Multimedia Tools Appl. 75(2), 935–959 (2016)

    Article  Google Scholar 

  30. Soula, A., Said, S.B., Ksantini, R., Lachiri, Z.: In: A novel incremental face recognition method based on nonparametric discriminant model, pp. 1–6. IEEE (2018)

  31. Tang, M., Chen, F.: Facial expression recognition and its application based on curvelet transform and pso-svm. Optik-Int. J. Light Electron Optics 124(22), 5401–5406 (2013)

    Article  Google Scholar 

  32. Trefethen, L.N., Bau, D., III.: Numerical linear algebra 50, Siam (1997)

  33. Uçar, A., Demir, Y., Güzeliş, C.: A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering. Neural Comput. Appl. 27(1), 131–142 (2016)

    Article  Google Scholar 

  34. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Computer Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  35. Zhang, L., Tjondronegoro, D., Chandran, V.: Random gabor based templates for facial expression recognition in images with facial occlusion. Neurocomputing 145, 451–464 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Education, Udon Thani Rajabhat University, 64 Tahan Rd., Muang, Udon Thani, 41000, Thailand

    Sarutte Atsawaraungsuk

  2. Department of Computer Engineering, Khon Kaen University, 123 Moo 16 Mittapap Rd., Muang, Khon Kaen, 40002, Thailand

    Tatpong Katanyukul, Pattarawit Polpinit & Nawapak Eua-anant

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  1. Sarutte Atsawaraungsuk
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  2. Tatpong Katanyukul
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  3. Pattarawit Polpinit
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  4. Nawapak Eua-anant
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Correspondence to Pattarawit Polpinit.

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Atsawaraungsuk, S., Katanyukul, T., Polpinit, P. et al. Fast and robust online-learning facial expression recognition and innate novelty detection capability of extreme learning algorithms. Prog Artif Intell 11, 151–168 (2022). https://doi.org/10.1007/s13748-021-00266-y

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