PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Characterising Facial Gender Difference Using Fisher-Rao Metric
Simone Ceolin and Edwin Hancock
In: ICPR 2010, 23-26 Aug 2010, Istanbul, Turkey.

Abstract

The aim in this paper is to explore whether the Fisher-Rao metric can be used to measure different facets of facial shape estimated from fields of surface normals using the von-Mises Fisher distribution. In particular we aim to characterise the shape changes due to differences in gender. We make use of the von-Mises Fisher distribution since we are dealing with surface normal data over the sphere R^2. Finally, we show the results achieved using EAR and Max Planck datasets.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
ID Code:7349
Deposited By:Edwin Hancock
Deposited On:17 March 2011