Exploratory GMDS Integration
Code was customised and integrated into the main framework with the
aim of putting it in a dimensionality reduction algorithm of another
type, alongside signal of nature other than geometric (and geometry-invariant).
If done improperly or applied to faces of different people (as the
figures below show), it can be demonstrably shown that the resultant
correspondence is rather poor. The data dealt with in this case is
illustrated in Figure . Figure
shows this with and Figure shows
the same for . Conversely, as seen in Figure ,
even with the found correspondence is considerably better
when handling images acquired of the same person.
Figure:
Transformation from 3-D face (left)
to a subset of rigid parts and then GMDS handling of the underlying
surface (right)
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Figure:
Nose and eye regions from different people
(FRGC 2.0) as treated by GMDS ()
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Figure:
Nose and eye regions from different people
(FRGC 2.0) as treated by GMDS when
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Figure:
Nose and eye regions of the same person
(FRGC 2.0) as treated by GMDS
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Positive pairs/matches are shown in figures
and , but in the former case (merely the
first image in the set) imprecision can be seen, whereas in the latter
there is bad data creeping in, leading to serious problems when trying
to pipe it into PCA and deal with GMDS as a similarity measure within
the larger framework.
Figure:
The first pair in the set of real matches
(same person in different poses)
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Figure:
An example of a problematic pair with
a false signal spike (left)
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By resolving issues associated with fatal exceptions in the pipeline
it should be trivial to utilise the generalised MDS, which by far
simplifies experiments performed with MDS (still part of the program,
at least as an option to be explored or compared to later).
Roy Schestowitz
2011-08-19