Estimating average growth trajectories in shape-space using kernel smoothing.

Abstract:

:In this paper, we show how a dense surface point distribution model of the human face can be computed and demonstrate the usefulness of the high-dimensional shape-space for expressing the shape changes associated with growth and aging. We show how average growth trajectories for the human face can be computed in the absence of longitudinal data by using kernel smoothing across a population. A training set of three-dimensional surface scans of 199 male and 201 female subjects of between 0 and 50 years of age is used to build the model.

journal_name

IEEE Trans Med Imaging

authors

Hutton TJ,Buxton BF,Hammond P,Potts HW

doi

10.1109/TMI.2003.814784

subject

Has Abstract

pub_date

2003-06-01 00:00:00

pages

747-53

issue

6

eissn

0278-0062

issn

1558-254X

journal_volume

22

pub_type

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