Testing for Gender Related Size and Shape Differences of the Human Ear canal using Statistical methods

This work deals with the analysis of the shape of the human ear canal. It is described how a dense surface point distribution model of the human ear canal is built based on a training set of laser scanned ear impressions and a sparse set of anatomical landmarks placed by an expert. The dense surface models are built by using the anatomical landmarks to warp a template mesh onto all shapes in the training set.

Testing the gender related differences is done by initially reducing the dimensionality using principal component analysis of the vertices of the warped meshes. The number of components to retain is chosen using Horn's parallel analysis. Finally a multivariate analysis of variance is performed on these components.