![](bg6.png)
Figure 4: Matrix of pairwise normalized shape distance be-
tween facial surfaces. The first class of faces belongs to the
same person while the others correspond to different indi-
viduals.
one) thanks to the robustness of the matching phase.
Indeed, the features points extracted present several
desirable properties such as parametrizations over a
canonical domain, stability and invariance to scale
and 3D motion group. They are also ordered. This
makes the correspondence step useless for registra-
tion application. Moreover, the neighborhood reso-
lution (number of geodesic level curves and radial
lines around a given interest point) affects the accu-
racy and quality of the matching results. Therefore, a
study on the optimal resolution of the curves has been
fixed thanks to a generalized version of the Shannon
theorem. Thus, the relationship between the size of
the features and the performance registration has been
studied. Because the features points properties are ro-
bust towards tessellation, an application in 3D imag-
ing field especially 3d face description has been cho-
sen. A good discriminative power in face description
has been noticed over experimentation on 3D facial
database. This works suggests a number of questions
to be addressed in future research such that adopting
database with several class of objects to be applied in
other fields (medical imaging, indexing, etc).
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