Hi, and welcome to this fourth and final week
of this course on convolutional neural networks.
By now, you've learned a lot about confidence.
What I want to do this week is show you
a couple important special applications of confidence.
We'll start the face recognition,
and then go on later this week to neurosal transfer,
which you get to implement in the problem exercise as well to create your own artwork.
But first, let's start the face recognition and just for fun,
I want to show you a demo.
When I was leading by those AI group,
one of the teams I worked with led by Yuanqing Lin had built
a face recognition system that I thought is really cool. Let's take a look.
So, I'm going to play this video here,
but I can also get whoever is editing this raw video configure out to this
better to splice in the raw video or take the one I'm playing here.
I want to show you a face recognition demo.
I'm in Baidu's headquarters in China.
Most companies require that to get inside,
you swipe an ID card like this one but here we don't need that.
Using face recognition, check what I can do.
When I walk up, it recognizes my face, it says,
"Welcome Andrew," and I just walk right through without ever having to use my ID card.
Let me show you something else.
I'm actually here with Lin Yuanqing,
the director of IDL which developed all of this face recognition technology.
I'm gonna hand him my ID card,
which has my face printed on it,
and he's going to use it to try to sneak in using my picture instead of a live human.
I'm gonna use Andrew's card and try to sneak in and see what happens.
So the system is not recognizing it,
it refuses to recognize.
Okay. Now, I'm going to use my own face.
So face recognition technology like this is taking off very rapidly in China
and I hope that this type of technology soon makes it way to other countries..
So, pretty cool, right?
The video you just saw demoed both face recognition as well as liveness detection.
The latter meaning making sure that you are a live human.
It turns out liveness detection can be implemented using supervised learning as well
to predict live human versus not live human but I want to spend less time on that.
Instead, I want to focus our time on talking about how to
build the face recognition portion of the system.
First, let's start by going over some of the terminology used in face recognition.
In the face recognition literature,
people often talk about face verification and face recognition.
This is the face verification problem which is if you're
given an input image as well as a name or ID
of a person and the job of the system is to
verify whether or not the input image is that of the claimed person.
So, sometimes this is also called a one to
one problem where you just want to know if the person is the person they claim to be.
So, the recognition problem is much harder than the verification problem.
To see why, let's say,
you have a verification system that's 99 percent accurate.
So, 99 percent might not be too bad but now suppose
that K is equal to 100 in a recognition system.
If you apply this system to a recognition task with a 100 people in your database,
you now have a hundred times of chance of making
a mistake and if the chance of making mistakes on each person is just one percent.
So, if you have a database of
a 100 persons and if you want an acceptable recognition error,
you might actually need a verification system with maybe
99.9 or even higher accuracy before you can run
it on a database of 100 persons that have
a high chance and still have a high chance of getting incorrect.
In fact, if you have a database of 100 persons currently just be even
quite a bit higher than 99 percent for that to work well.
But what we do in the next few videos is focus on building
a face verification system as a building block and then if the accuracy is high enough,
then you probably use that in a recognition system as well.
So in the next video,
we'll start describing how you can build a face verification system.
It turns out one of the reasons that is
a difficult problem is you need to solve a one shot learning problem.
Let's see in the next video what that means.