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)

Abstract

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.

Keywords

manifold learning intrinsic dimensionality dimensionality reduction profiles facial attractiveness 

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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

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