Doppelgangers: an identification algorythm

[f***@beethoven.com]

Given a database of images of faces, an algorythm to identify
similarities is given by applying morphing transformations between two
pairs of images for a number of intermediate images. The differential
between the number of points changed would give a measure of the
diffrence between two faces, for a normalized grayscale image. Also a
quantization with octries can help approximate similarities for an
appropriate gradation of grays...

These algorythms work even for small regions of the image if they are
normalized by size too. Of course, a conventional framework for the
inpout of face images will help as a preprocessor for the processing of
the images. Through this method a distribution of facial features can
be found and a measure of distinctiveness between human beings, similar
to the differences in genotypes. It can be complemented with a
psychological research measuring the sensitive to differences between
individuals (twins, for instance) in person and/or pictures...

Using an automatic classification algorythm (kohonen), characterologies
and typologies can be generated, to be correlated to other
characteristics compiled for each individual. This approach can serve
for tasks such as law making, career selection, prediction, marketing,
etc...

Fabrizio J Bonsignore, now Danilo J Bonsignore

[f***@beethoven.com]

Given a database of images of faces, an algorythm to identify
similarities is given by applying morphing transformations between two
pairs of images for a number of intermediate images. The differential
between the number of points changed would give a measure of the
diffrence between two faces, for a normalized grayscale image. Also a
quantization with octries can help approximate similarities for an
appropriate gradation of grays...

These algorythms work even for small regions of the image if they are
normalized by size too. Of course, a conventional framework for the
inpout of face images will help as a preprocessor for the processing of
the images. Through this method a distribution of facial features can
be found and a measure of distinctiveness between human beings, similar
to the differences in genotypes. It can be complemented with a
psychological research measuring the sensitive to differences between
individuals (twins, for instance) in person and/or pictures...

Using an automatic classification algorythm (kohonen), characterologies
and typologies can be generated, to be correlated to other
characteristics compiled for each individual. This approach can serve
for tasks such as law making, career selection, prediction, marketing,
etc...

A simlar previous posting on the same theme:
A classification infrastructure for socioeconomic data

When classifying and comparing socioeconomic data for countries, most
economists recur to the simplest data structure: the list. We ordered
countries according to a single scale, a single quantity, and then we
draw inferences from it. Our econometrics also tends to be linear. But
linear structures may hide more interesting relationships than cannot
be discovered by inspection, besides tending to rate countries in a
`first is better` point of view.

As an alternative, the same structure we use for color quantization,
the octrie, can be used to ordered economic data according to two (or
more) quantities, yielding both a classification and an ordering. This
structure may help to order data in ways that can enrich the discussion

of the phenomena being investigated.

Fabrizio J Bonsignore, now Danilo J Bonsignore
http://www.geocities.com/syntotic/photos/myphotos.htm
(still, no fixed address...) >;(<

[f***@beethoven.com]

This approach leads to a tool to research morphogenesis by correlating
genotypes (DNA mappings) to phenotype variations (algorythm difference
measurements). Thew same approach I have proposed wlsewhere can be used
to derive rules for te expression of genotypes as phenotypes using
genetic algorythms and other search procedures. I guess that a suitable
computing framework is a mesh of supercomputers, to take advantage both
of supercomputing velocity and highly parallel approaches.

Fabrizio J Bonsignore, now Danilo J Bonsignore
(I guess they already heard about it...)

Post by f***@beethoven.com
Given a database of images of faces, an algorythm to identify
similarities is given by applying morphing transformations between two
pairs of images for a number of intermediate images. The differential
between the number of points changed would give a measure of the
diffrence between two faces, for a normalized grayscale image. Also a
quantization with octries can help approximate similarities for an
appropriate gradation of grays...
These algorythms work even for small regions of the image if they are
normalized by size too. Of course, a conventional framework for the
inpout of face images will help as a preprocessor for the processing of
the images. Through this method a distribution of facial features can
be found and a measure of distinctiveness between human beings, similar
to the differences in genotypes. It can be complemented with a
psychological research measuring the sensitive to differences between
individuals (twins, for instance) in person and/or pictures...
Using an automatic classification algorythm (kohonen), characterologies
and typologies can be generated, to be correlated to other
characteristics compiled for each individual. This approach can serve
for tasks such as law making, career selection, prediction, marketing,
etc...
A classification infrastructure for socioeconomic data
When classifying and comparing socioeconomic data for countries, most
economists recur to the simplest data structure: the list. We ordered
countries according to a single scale, a single quantity, and then we
draw inferences from it. Our econometrics also tends to be linear. But
linear structures may hide more interesting relationships than cannot
be discovered by inspection, besides tending to rate countries in a
`first is better` point of view.
As an alternative, the same structure we use for color quantization,
the octrie, can be used to ordered economic data according to two (or
more) quantities, yielding both a classification and an ordering. This
structure may help to order data in ways that can enrich the discussion
of the phenomena being investigated.
Fabrizio J Bonsignore, now Danilo J Bonsignore
http://www.geocities.com/syntotic/photos/myphotos.htm
(still, no fixed address...) >;(<