  The goal of point set registration is to find point-by-point correspondences
between point sets, each of which characterizes the shape of an object. Because
local preservation of object geometry is assumed, prevalent algorithms in the
area can often elegantly solve the problems without using geometric information
specific to the objects. This means that registration performance can be
further improved by using prior knowledge of object geometry. In this paper, we
propose a novel point set registration method using the Gaussian mixture model
with prior shape information encoded as a statistical shape model. Our
transformation model is defined as a combination of the similar transformation,
motion coherence, and the statistical shape model. Therefore, the proposed
method works effectively if the target point set includes outliers and missing
regions, or if it is rotated. The computational cost can be reduced to linear,
and therefore the method is scalable to large point sets. The effectiveness of
the method will be verified through comparisons with existing algorithms using
datasets concerning human body shapes, hands, and faces.
