The Intrinsic Dimensionality of Attractiveness: A Study in Face Profiles

  • Andrea Bottino
  • Aldo Laurentini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


The study of human attractiveness with pattern analysis techniques is an emerging research field. One still largely unresolved problem is which are the facial features relevant to attractiveness, how they combine together, and the number of independent parameters required for describing and identifying harmonious faces. In this paper, we present a first study about this problem, applied to face profiles. First, according to several empirical results, we hypothesize the existence of two well separated manifolds of attractive and unattractive face profiles. Then, we analyze with manifold learning techniques their intrinsic dimensionality. Finally, we show that the profile data can be reduced, with various techniques, to the intrinsic dimensions, largely without loosing their ability to discriminate between attractive and unattractive faces.


manifold learning intrinsic dimensionality dimensionality reduction profiles facial attractiveness 


  1. 1.
    Bashour, M.: History and Current Concepts in the Analysis of Facial Attractiveness. Plastic and Reconstructive Surgery 118(3), 741–756 (2006)CrossRefGoogle Scholar
  2. 2.
    Cunningham, M.R., Roberts, A.R., Barbee, A.P., Druen, P.B.: Their ideas of beauty are, on the whole, the same as ours: Consistency and variability in the crosscultural perceptions of female attractiveness. Journ. Pers. and Social Psyc. 68, 261–279 (1995)CrossRefGoogle Scholar
  3. 3.
    Etcoff, N.: Beauty and the beholder. Nature 368, 186–187 (1994)CrossRefGoogle Scholar
  4. 4.
    Jones, D.: Physical Attractiveness and the Theory of Sexual Selection: Results from Five Populations. University of Michigan Press, Ann Harbour (1996)Google Scholar
  5. 5.
    Bottino, A., Laurentini, A.: The Analysis of Facial Beauty: An Emerging Area of Research in Pattern Analysis. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6111, pp. 425–435. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. 4, 519–524 (1987)CrossRefGoogle Scholar
  7. 7.
    van der Maaten, L.J.P.: An Introduction to Dimensionality Reduction Using Matlab. Int. Report MICC 07-07, Universiteit Maastricht, The Netherlands (2007)Google Scholar
  8. 8.
    Guo, G., Fu, Y., Dyer, C.R., Huang, T.: Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. on Image Processing 17(7), 1178–1188 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Buchala, S., Davey, N., Frank, R.J., Gale, T.M.: Dimensionality reduction of face images for gender classification. In: Proc. IEEE Conf. on Intell. Syst., pp. 88–93 (2004)Google Scholar
  10. 10.
    Eisenthal, Y., Dror, G., Ruppin, E.: Facial Attractiveness: beauty and the machine. Neural Computation 18, 119–142 (2006)CrossRefGoogle Scholar
  11. 11.
    Ozkul, T., Ozkul, M.H.: Computer simulation tool for rhinoplasty planning. Comput. in Biol. and Med. 34, 697–718 (2004)CrossRefGoogle Scholar
  12. 12.
    Tariq, U., Hu, Y., Huang, T.S.: Gender and Ethnicity identification from silhouetted face profiles. In: IEEE Proc. ICIP, pp. 2441–2444 (November 2009)Google Scholar
  13. 13.
    Kakadiaris, I.A., Abdelmunim, H., Yang, W., Theoharis, T.: Profile-based face recognition. In: IEEE Proc. FG 2008, pp. 1–8 (2008)Google Scholar
  14. 14.
    Wu, C.J., Huang, J.S.: Human face profile recognitionby computer. Pattern Recognition 23, 255–259 (1990)CrossRefGoogle Scholar
  15. 15.
    Zhou, X., Bhanu, B.: Human recognition based onface profiles in video. In: Proc. IEEE CVPR, Washington, DC (June 2005)Google Scholar
  16. 16.
    Pantic, M., Patras, I., Rothkrantz, L.: Facial action recognition in face profiles image sequences. In: Proc. IEEE ICME 2002, pp. 37–40 (2002)Google Scholar
  17. 17.
    Davidenko, N.: Silhouetted face profiles: A new methodology for face perception research. Journal of Vision 7(4), 6, 1–17 (2007)Google Scholar
  18. 18.
    Bottino, A., Cumani, S.: A fast and robust method for the identification of face landmarks in profile images. W. Trans. on Comp. 7(8), 1250–1259 (2008)Google Scholar
  19. 19.
    Grassberger, P., Procaccia, I.: Measuring the Strangeness of Strange Attractors. Physica D: Nonlinear Phenomena 9(1-2), 189–208 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Keating, C.F.: Gender and the physiognomy of dominance and attractiveness. Social Psychology Quarterly 48, 61–70 (1985)CrossRefGoogle Scholar
  21. 21.
    Zuk, M.: The role of parasites in sexual selection: current evidence and future directions. Advances in the Study of Behavior 21, 39–67 (1992)CrossRefGoogle Scholar
  22. 22.
    Aarabi, P., Hughes, D., Mohajer, K., Emami, M.: The automatic measurement of facial beauty. In: IEEE SMC 2004, pp. 2168–2174 (2004)Google Scholar
  23. 23.
    Mao, H., Jin, L., Du, M.: Automatic classification of Chinese female facial beauty using Support Vector Machine. In: IEEE SMC 2009, pp. 4842–4846 (2009)Google Scholar
  24. 24.
    Sutic, D., Breskovic, I., Huic, R., Jukic, I.: Automatic evaluation of facial attractiveness. In: MIPRO 2010, Opatija, May 24-28, pp. 1339–1342 (2010)Google Scholar
  25. 25.
    Eisenthal, Y., Dror, G., Ruppin, E.: Facial Attractiveness: beauty and the machine. Neural Computation 18, 119–142 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrea Bottino
    • 1
  • Aldo Laurentini
    • 1
  1. 1.Politecnico di TorinoDipartimento di Automatica e InformaticaItaly

Personalised recommendations