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To go into some detail, let's take a look
at how one might do locality sensitive

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hashing for fingerprint matching.
This example is covered in

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Rajaraman/Ullman so you can read about it
there as well.

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Fingerprints match if their minutiae
match.

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Now, minutiae is a technical term in the
world of fingerprints, which I don't claim

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to understand in great detail.
But things like islands, ridges,

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bifurcations, cores, crossovers, these are
the terms used in that field.

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We won't get into the details to the
different types of minutiae, instead we

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will only worry about whether a particular
print has some minutiae in a particular

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grid point, So we assume grid of cells to
be placed on the fingerprint and.

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We only measuring whether or not.
A cell contains, a minutia.

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Of some kind.
Now, we define a function f(x) for a

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print'x', which is one if the print has
minutia in a specified set of'k' mid

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positions.
So function'f' depends on these

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particular'k' positions.
We'll use'k' is equal to three in the

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example going forward.
Notice that the function f is dependent on

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the choice of the k-grid positions.
If you choose a different set of grid

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positions, you'll get a different function
f.

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Now let's consider the probability that
any print.

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Has a probab, has a menu shay, in a
particular position, let that be P.

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Not every print has menu shay in every
position, but across all possible prints.

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A particular position has menu shay with
probability P.

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Lets assume that the distribution is
uniform and the probability is P.

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Not necessarily a great assumption but for
our example, it will suffice.

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Now, the probability that f of x is equal
to one, that is that the print x has

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minutiae in all k positions.
Is p raised to the power of k.

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So if you take the probability that a
particular cell has, menu shay is 0.2, the

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probability of that, a set of three chosen
cells, all have menu shay, is obviously

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0.2 raise to the power three which is
0.008.

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Now let's consider another print, y, but
from the same person.

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Its quiet likely that this print will have
when you say in the same position as X,

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but its not always the case.
So lets assign a probability Q, it will be

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quiet high that the print Y will have when
you say, if X also does, in a particular

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grid cell.
Now let's look at the function F.

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What is the probability that F of X is one
and F of Y is also one?

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Well, first of all the probability that F
of X is one is P to the K, so that needs

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to happen.
But then.

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You have the probability that.
Y also has to have menu shay in the same K

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position, so you multiply it by Q, K
times, so you get P Q to the K.

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Q is 0.9 that means there is 90 percent
chance that Y will have menu shay if X

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also does in a particular cell, then P Q
to the three, works out 2.006.

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Well, that's not so nice that both x and y
get a yes match with the function f is

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only.
Happening with the probability.006.
