Analysis of human attractiveness using manifold kernel regression

B. C. Davis, S. Lazebnik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper uses a recently introduced manifold kernel regression technique to explore the relationship between facial shape and attractiveness on a heterogeneous dataset of over three thousand images gathered from the Web. Using the concept of the Fréchet mean of images under a diffeomorphic transformation model, we evolve the average face as a function of attractiveness ratings. Examining these averages and associated deformation maps enables us to discern aggregate shape change trends for male and female faces.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages109-112
Number of pages4
DOIs
StatePublished - Dec 1 2008
Externally publishedYes
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
Country/TerritoryUnited States
CitySan Diego, CA
Period10/12/0810/15/08

Keywords

  • Diffeomorphic registration
  • Fréchet mean
  • Human attractiveness
  • Manifold kernel regression

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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