right now we have three really good
examples of facial recognition being
banned within a specific city San
Francisco is one of them another one is
Oakland and the third is Somerville in
Massachusetts there's also a bill being
discussed right now for the state of
Michigan which is the first statewide
challenge to facial recognition within
the last couple of years we learned that
major cities within Michigan including
Detroit have had facial recognition
deployed for years this is an important
moment for the u.s. because it's the
first time we're starting to see
challenges about surveillance and
biometrics data being captured stored
and used for the purposes of law
enforcement being transferred into the
courts today we're in our mixed reality
studio to get the big picture on facial
recognition okay
so when an artificially intelligent
facial recognition program sees a human
face like this tell us what it's looking
for first well there are lots of
methodologies that we can look at one in
particular called open face the first
thing it needs to do is find out whether
there's even a face in the photo and it
does that by looking for 68 different
facial landmarks that typically
represent what we would think of as a
visibly human face within those 68
different groupings we're looking for
edges or gradients and the edges
typically represent a group of pixels
that's the same color or that shifts
from a darker color to a lighter one in
the open face paper
there's particularly eight different
landmarks that represent a human eye and
many others comprise an eyebrow or a
mouth or nose anything along those lines
that you might see when you look in the
mirror
well now that we know we have a human
face the goal is to get it into a format
that's uniform and using these 68 points
as anchors we can then scale or rotate
or even adjust the angle of the
individuals face so this is gonna allow
the program to be able to make it bigger
or rotate it a couple degrees and still
be able to effectively understand what
it's looking at exactly we want every
face that we're trying to predict on to
be effectively similar enough that we
know things are in the same spot the
challenges come in whenever your
training data is different from what
you're trying to predict so that could
mean if all your training data is done
in low light and you have a really sunny
day then it's going to be really
difficult to predict a match if it's
something as simple as you have on a
pair of sunglasses that removes a lot of
the landmarks and makes it a lot harder
to predict well the next step is to try
to extract information from that face
and we do that by passing the photo
through a neural net each layer until
the last one is going to try to extract
features and then represent them in a
mathematical form so Liz where are these
programs getting the data to match these
faces to well usually a facial
recognition system has a database of
photos that have already been run
through a neural net meaning they're
already translated into math into
multi-dimensional coordinates now if I
have a database and I know that this
mathematical representation looks like
Liz O'Sullivan then I can also take this
new photo and try to figure out how
close that vector space is to the
resulting vector space of my new photo
if it's very close then yes it might be
Liz if they're very far apart it's
likely it's somebody else
people are starting to realize exactly
how powerful this tool is and in some
cases we're finding out that law
enforcement federal agencies have been
using it for some time without our
knowledge or consent I'm a lot of cases
we're finding out that this technology
doesn't work as well on people of color
or women or you know gender fluid or
non-binary people so this is important
now for us to take a moment to
understand fully exactly what we want
our civil liberties to look like in the
US
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