Facial expression recognition using the spectral graph wavelet

Facial expression recognition using the spectral graph wavelet

For access to this article, please select a purchase option:

Buy eFirst article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The authors propose a method to recognise facial expressions based on graph signal processing (GSP) techniques. Facial expressions are characterised by local patterns in the facial regions such as eyes, lips etc. and interrelationships among them. A facial expression recognition algorithm needs to capture these variations in the facial regions at the local level and the interrelationships of these regions at the global level. Hence, in the authors’ opinion, GSP seems to be an appropriate tool for the purpose. In this study, a novel method is presented which makes use of graph signals to represent the facial regions. They leverage spectral graph wavelet transform to extract information for creating the feature descriptor. Here, different types of two-channel and three-channel filter banks have been used by setting the weights of their channels for finding the optimum performance of the recognition rate. Through simulation studies, it is observed that the use of Abspline filter bank provides the best result. The experimental investigations on the extended Cohn–Kanade (CK+) and the JAFFE datasets have been carried out and the results confirm the effectiveness of the proposed method in recognition rate improvement.


    1. 1)
      • 1. Freiwald, W.A., Tsao, D.Y., Livingstone, M.S.: ‘A face feature space in the Macaque temporal lobe’, Nat. Neurosci., 2009, 12, (9), pp. 11871196.
    2. 2)
      • 2. Sariyanidi, E., Gunes, H., Cavallaro, A.: ‘Automatic analysis of facial affect: a survey of registration, representation, and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (6), pp. 11131133.
    3. 3)
      • 3. Lyons, M.J., Budynek, J., Akamatsu, S.: ‘Automatic classification of single facial images’, IEEE Trans. Pattern Anal. Mach. Intell., 1999, 21, (12), pp. 13571362.
    4. 4)
      • 4. Turk, M.A., Pentland, A.P.: ‘Face recognition using eigenfaces’. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), June 1991, pp. 586591.
    5. 5)
      • 5. Yu, Z., Zhang, C.: ‘Image based static facial expression recognition with multiple deep network learning’. Proc. ACM Conf. on Multimodal Interaction, November 2015, pp. 435442.
    6. 6)
      • 6. Shuman, D. I., Narang, S. K., Frossard, P., et al: ‘The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains’, IEEE Signal Process. Mag., 2013, 30, (3), pp. 8398.
    7. 7)
      • 7. Sandryhaila, A., Moura, J.M.: ‘Big data analysis with signal processing on graphs: representation and processing of massive data sets with irregular structure’, IEEE Signal Process. Mag., 2014, 31, (5), pp. 8090.
    8. 8)
      • 8. Crovella, M., Kolaczyk, E.: ‘Graph wavelets for spatial traffic analysis’. Proc. 22nd Annual Joint Conf. of the IEEE Computer and Communication (INFOCOM), March 2003, vol. 3, pp. 18481857.
    9. 9)
      • 9. Hammond, D.K., Vandergheynst, P., Gribonval, R.: ‘Wavelets on graphs via spectral graph theory’, Appl. Comput. Harmon. Anal., 2009, 30, (2), pp. 129150.
    10. 10)
      • 10. Narang, S.K., Chao, Y.H., Ortega, A.: ‘Graph-wavelet filterbanks for edge-aware image processing’, IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, MI, 2012, pp. 141144.
    11. 11)
      • 11. Leonardi, N., Van De Ville, D.: ‘Wavelet frames on graphs defined by fMRI functional connectivity’. IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, Chicago, March 2011, pp. 21362139.
    12. 12)
      • 12. Kerola, T., Inoue, N., Shinoda, K.: ‘Cross-view human action recognition from depth maps using spectral graph sequences’, Comput. Vis. Image Underst., 2017, 154, pp. 108126.
    13. 13)
      • 13. Grossmann, A.: ‘Wavelet transforms and edge detection’, in Albeverio, S., Blanchard, P., Hazewinkel, M., et al (Eds.): ‘Stochastic processes in physics and engineering’ (Springer, Netherlands, 1988), pp. 149157.
    14. 14)
      • 14. Baek, Y.H., Byun, O.S., Moon, S.R.: ‘Image edge detection using adaptive morphology Meyer wavelet-CNN’. Proc. Int. Joint Conf. on Neural Networks, Portland, 2003, vol. 2, pp. 12191222.
    15. 15)
      • 15. Perraudin, N., Paratte, J., Shuman, D., et al: ‘GSPBOX: A toolbox for signal processing on graphs’, 2014, arXiv:1408.5781.
    16. 16)
      • 16. Lyons, M. J., Akemastu, S., Kamachi, M., et al: ‘Coding facial expressions with Gabor wavelets’. Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition, Nara, April 1998, pp. 200205.
    17. 17)
      • 17. Lucey, P., Cohn, J. F., Kanade, T., et al: ‘The extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression’. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), San Francisco, 2010, pp. 94101.
    18. 18)
      • 18. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), Kavai, 2001, vol. 1, p. I.
    19. 19)
      • 19. Xie, S., Hu, H.: ‘Facial expression recognition with FRR-CNN’, Electron. Lett., 2017, 53, (4), pp. 235237.
    20. 20)
      • 20. Meena, H.K., Sharma, K.K., Joshi, S.D.: ‘Improved facial expression recognition using graph signal processing’, Electron. Lett., 2017, 53, (11), pp. 718720.
    21. 21)
      • 21. Kahou, S.E., Froumenty, P., Pal, C.: ‘Facial expression analysis based on high dimensional binary features’. Proc. European Conf. on Computer Vision, Springer, Cham, September 2014, pp. 135147.
    22. 22)
      • 22. Happy, S.L., Routray, A.: ‘Automatic facial expression recognition using features of salient facial patches’, IEEE Trans. Affective Comput., 2015, 6, (1), pp. 112.
    23. 23)
      • 23. Mlakar, U., Potočnik, B.: ‘Automated facial expression recognition based on histograms of oriented gradient feature vector differences’, Signal. Image Video Process., 2015, 9, (1), pp. 245253.
    24. 24)
      • 24. Zhang, L., Tjondronegoro, D.: ‘Facial expression recognition using facial movement features’, IEEE Trans. Affective Comput., 2011, 2, (4), pp. 219229.
    25. 25)
      • 25. Shan, C., Gong, S., McOwan, P.W.: ‘Facial expression recognition based on local binary patterns: a comprehensive study’, Image Vis. Comput., 2009, 27, (6), pp. 803816.
    26. 26)
      • 26. Sebe, N., Lew, M.S., Sun, Y., et al: ‘Authentic facial expression analysis’, Image Vis. Comput., 2007, 25, (12), pp. 18561863.
    27. 27)
      • 27. Yeasin, M., Bullot, B., Sharma, R.: ‘Recognition of facial expressions and measurement of levels of interest from video’, IEEE Trans. Multimedia, 2006, 8, (3), pp. 500508.
    28. 28)
      • 28. Philips, G.M.: ‘Interpolation and approximation by polynomials’ (Springer: Science and Business Media, New York, 2003).

Related content

This is a required field
Please enter a valid email address