Advertisement

Gender and Smile Dynamics

  • Hassan UgailEmail author
  • Ahmad Ali Asad Aldahoud
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter is concerned with the discussion of a computational framework to aid with gender classification in an automated fashion using the dynamics of a smile. The computational smile dynamics framework we discuss here uses the spatio-temporal changes on the face during a smile. Specifically, it uses a set of spatial and temporal features on the overall face. These include the changes in the area of the mouth, the geometric flow around facial features and a set of intrinsic features over the face. These features are explicitly derived from the dynamics of the smile. Based on it, a number of distinct dynamic smile parameters can be extracted which can then be fed to a machine learning algorithm for gender classification.

Keywords

Smile dynamics Gender recognition Machine learning k-nearest neighbour 

References

  1. 1.
    Al-dahoud, A., Ugail, H.: A method for location based search for enhancing facial feature detection. In: The Proceedings of the International Conference on Advances in Computational Intelligence Systems, AISC, pp. 421–432 (2016)Google Scholar
  2. 2.
    Brody, L.R., Hall, J.A., Stokes, L.R.: Gender and emotion: theory, findings, and content. In: Barrett, L.F., Lewis, M., Haviland-Jones, J.M. (eds.) Handbook of Emotions, 4th edn, pp. 369–392. The Guildford Press (2016)Google Scholar
  3. 3.
    Bukar, A.M., Ugail, H., Connah, D.: Automatic age and gender classification using supervised appearance model. J. Electron. Imaging 25(6), 061605 (2016)CrossRefGoogle Scholar
  4. 4.
    Cashdan, E.: Smiles, speech, and body posture: how women and men display sociometric status and power. J. Nonverbal Behav. 22(4), 209–228 (1998)CrossRefGoogle Scholar
  5. 5.
    Dantcheva, A., Brémond, F.: Gender estimation based on smile-dynamics. IEEE Trans. Inf. Forensics Secur. 12(3), 719–729 (2017)CrossRefGoogle Scholar
  6. 6.
    Han, X., Ugail, H., Palmer, I.: Gender classification based on 3D face geometry features using SVM. In: Cyberworlds 2009, Bradford, UK (2009)Google Scholar
  7. 7.
    Jacko, J.A.: Human Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications. CRC Press (2012)Google Scholar
  8. 8.
    Langlois, J.H., Roggman, L.A.: Attractive faces are only average. Psychol. Sci. 1(2), 115121 (1990)CrossRefGoogle Scholar
  9. 9.
    Liu, L., Sheng, Y., Zhang, G., Ugail, H.: Graph cut based mesh segmentation using feature points and geodesic distance. In: Cyberworlds 2015, Gotland, Sweden, pp. 115–120 (2015)Google Scholar
  10. 10.
    Loth, S.R., Iscan, M.Y.: Sex Determination, Encyclopedia of Forensic Sciences, vol. 1. Academic Press, San Diego (2000)Google Scholar
  11. 11.
    Ugail, H., Al-dahoud, A.: Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition. Vis. Comput. 34(9), 12431254 (2018)CrossRefGoogle Scholar
  12. 12.
    Yap, M.H., Ugail, H., Zwiggelaar, R.: Intensity score for facial actions detection in near-frontal-view face sequences. Comput. Commun. Eng. 6, 819–824 (2013)Google Scholar
  13. 13.
    Yap, M.H., Ugail, H., Zwiggelaar, R.: Facial analysis for real-time application: a review in visual cues detection techniques. J. Commun. Comput. 9, 1231–1241 (2013)Google Scholar
  14. 14.
    Yap, M.H., Ugail, H., Zwiggelaar, R.: Facial behavioural analysis: a case study in deception detection. Br. J. Appl. Sci. Technol. 4(10), 1485–1496 (2014)CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Engineering and InformaticsUniversity of BradfordBradfordUK
  2. 2.Faculty of Engineering and MathematicsUniversity of BradfordBradfordUK

Personalised recommendations