Now, this is a simple example of how it can build a classifier for images, but
in reality they don't explicitly have a nose detector or eye detector.
What happens is these called image features or
interest points and there's various names for this.
But they really tried to find local image segments,
patches, that are really distinctive.
So then maybe they'll find the corner around the eye,
maybe the corner around the nose, so if you have lots of this corner detectors,
a face is comprised of corners.
Corner detector firings at places around the eye, the mouth, and both eyes.
And if enough of this fire in a particular pattern,
you discover that you have a face.
So this is how computer vision typically works.
So this is how classification works.
Of course, there's more general models and more complex ones, but
this is kind of the basic idea.
For years, these types of detectors of local features are built by hand.
A very popular one was called SIFT features.
And this retransformed their computer vision because they were really quite
applicable and quite cool.
And then, there are many others that improve the accuracy.
So other kinds of features that can be used.