Distance-Texture Signature Duo for Determination of Human Emotion

  • Paramartha DuttaEmail author
  • Asit Barman
Part of the Cognitive Intelligence and Robotics book series (CIR)


Previous Chaps.  2,  3 and  4 consist of three feature descriptors individually distance signature, shape signature and texture signature and Chap.  5 considered as combined descriptor such as distance and shape signature (D-S). In course of Distance-Texture (S-T) signature, respective stability indices and statistical measures supplement the signature features with a view to enhance the performance task of facial expression classification. Incorporation of these supplementary features is duly justified through extensive study and analysis of results obtained thereon.


  1. 1.
    T.F. Cootes, G.J. Edwards, C.J. Taylor, et al., Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)Google Scholar
  2. 2.
    T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  3. 3.
    C. Shan, S. Gong, P.W. McOwan, Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)Google Scholar
  4. 4.
    G. Tzimiropoulos, M. Pantic, Optimization problems for fast aam fitting in-the-wild, in Proceedings of the IEEE International Conference on Computer Vision (2013), pp. 593–600Google Scholar
  5. 5.
    Y. Tong, Y. Wang, Z. Zhu, Q. Ji, Robust facial feature tracking under varying face pose and facial expression. Pattern Recognit. 40(11), 3195–3208 (2007)CrossRefGoogle Scholar
  6. 6.
    X. Zhu, D. Ramanan, Face detection, pose estimation, and landmark localization in the wild, in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2012), pp. 2879–2886Google Scholar
  7. 7.
    L. Zhong, Q. Liu, P. Yang, J. Huang, D.N. Metaxas, Learning multiscale active facial patches for expression analysis. IEEE Trans. Cybern. 45(8), 1499–1510 (2015)Google Scholar
  8. 8.
    A. Barman, P. Dutta, Facial expression recognition using distance and texture signature relevant features. Appl. Soft Comput. 77, 88–105 (2019)CrossRefGoogle Scholar
  9. 9.
    D. Chakrabarti, D. Dutta, Facial expression recognition using eigenspaces. Procedia Technol. 10, 755–761 (2013)CrossRefGoogle Scholar
  10. 10.
    M. Rosenblum, Y. Yacoob, L.S. Davis, Human expression recognition from motion using a radial basis function network architecture. IEEE Trans. Neural Netw. 7(5), 1121–1138 (1996)Google Scholar
  11. 11.
    H. Jaeger, Tutorial on Training Recurrent Neural Networks, Covering BPPT, RTRL, EKF and the “Echo State Network” Approach, vol. 5 (GMD-Forschungszentrum Informationstechnik, 2002)Google Scholar
  12. 12.
    T. Lin, B.G. Horne, P. Tino, C.L. Giles, Learning long-term dependencies in narx recurrent neural networks. IEEE Trans. Neural Netw. 7(6), 1329–1338 (1996)Google Scholar
  13. 13.
    R. Pascanu, T. Mikolov, Y. Bengio, On the difficulty of training recurrent neural networks. ICML 3(28), 1310–1318 (2013)Google Scholar
  14. 14.
    P. Lucey, J.F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, I. Matthews, The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression, in Computer Society Conference on Computer Vision and Pattern Recognition-Workshops (IEEE, 2010), pp. 94–101Google Scholar
  15. 15.
    M.F. Valstar, M. Pantic, Induced disgust, happiness and surprise: an addition to the mmi facial expression database, in Proceedings of International Conference on Language Resources and Evaluation, Workshop on EMOTION (Malta, May 2010), pp. 65–70Google Scholar
  16. 16.
    M. Lyons, S. Akamatsu, M. Kamachi, J. Gyoba, Coding facial expressions with gabor wavelets, in Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998 (IEEE, 1998), pp. 200–205Google Scholar
  17. 17.
    N. Aifanti, C. Papachristou, A. Delopoulos, The mug facial expression database, in Proceedings of 11th International Workshop on Image Analysis for Facial Expression Database (Desenzano, Italy, April 2010), pp. 12–14Google Scholar
  18. 18.
    SL Happy and Aurobinda Routray, Automatic facial expression recognition using features of salient facial patches. IEEE Trans. Affect. Comput. 6(1), 1–12 (2015)CrossRefGoogle Scholar
  19. 19.
    L. Zhang, D. Tjondronegoro, Facial expression recognition using facial movement features. IEEE Trans. Affect. Comput. 2(4), 219–229 (2011)CrossRefGoogle Scholar
  20. 20.
    A. Poursaberi, H.A. Noubari, M. Gavrilova, S.N. Yanushkevich, Gauss–laguerre wavelet textural feature fusion with geometrical information for facial expression identification. EURASIP J. Image Video Process. (1), 1–13 (2012)Google Scholar
  21. 21.
    I. Kotsia, I. Pitas, Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Trans. Image Process. 16(1), 172–187 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    L. Zhong, Q. Liu, P. Yang, B. Liu, J. Huang, D.N. Metaxas, Learning active facial patches for expression analysis, in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2012), pp. 2562–2569Google Scholar
  23. 23.
    H. Boughrara, M. Chtourou, C.B. Amar, L. Chen, Facial expression recognition based on a mlp neural network using constructive training algorithm. Multimed. Tools Appl. 75(2), 709–731 (2016)Google Scholar
  24. 24.
    A.T. Lopes, E. de Aguiar, T. Oliveira-Santos, A facial expression recognition system using convolutional networks, in 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (IEEE, 2015), pp. 273–280Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer and Systems SciencesVisva-Bharati UniversitySantiniketanIndia
  2. 2.Department of Computer Science and Engineering and Information TechnologySiliguri Institute of TechnologySiliguriIndia

Personalised recommendations