Human faces are smooth and symmetrical, making superquadrics a good choice for representation and normalization.
We present a novel approach to parameterize 3D faces using the powerful superquadric model in combination with an
Eigen decomposition which represents the finer features of faces. The superquadric fit also provides axes of symmetry
that yield a normalized face coordinate space necessary for applying PCA. Results of fitting on our data set, of 2 scans
each from 107 people, show reliable representation for yaw, pitch, and roll with average rotations of the order 10-3
radians, about each axis.
Parameterization can be used to partition the search space into smaller bins, thus effectively reducing the search
space complexity for matching and recognition. We show that it is possible to create about 20-40 clusters with as few
as 30 parameters. The accuracy of the clustering algorithm, in some cases, is as high as 90%. We believe this approach
to indexing 3D faces is an interesting extension to existing literature.