Hi and welcome to the lecture on facial recognition in this section will do an overview of facial recognition
and a quick intro into VG face and face it.
So let's get started.
So firstly, what is facial recognition?
Well, it's the ability to automatically attach an individual's identity to a face, and that's something
us humans are quite good at.
In fact, we're incredible at it, although mean, not so much.
I do have problems recognizing faces, but that's another story.
Even some animals do can recognize faces.
It's proven that dogs cruise ship, and I believe elephants can do it.
So what about machines?
Can machines do it?
Yes, they can.
They can recognize faces, and this is exactly how facial recognition algorithms rendered out.
But they then localize and identify bounding box for office and then attach the identity based on a
database of faces to the person it's captured here.
So you can see this is my wife and I been recognized by a facial recognition algorithm.
Quite cool, isn't it?
So let's get into the history of facial recognition, and let's start with the early days.
Well, open Siri actually has three of these historic all the facial recognition libraries built in,
all of which do operate similarly in a way because they all take a dataset of labeled faces and incomplete
features to represent the images after which the classifiers did little to utilize these features to
classify quite simple in concept.
And actually, I was saying these networks do something similar as well.
However, we use deep learning to extract a representation of the faces, but we'll get there shortly.
So the tree library is starting with Eigen Faces, which was developed in 1987 and says how you call
it an open CV.
Fisher Faces was also developed in 1997, so you can see this is quite old and local binary patterns.
Histograms also developed in 1996.
Again, these algorithms, they worked fairly well when you had a low number of faces and strong variation
between images.
However, they all want that kind of that good, and this is an illustration of how local binary pattern
histograms is used to basically extract meaningful features of faces here.
So now let's take a look at some modern approaches to facial recognition that utilize deep learning.
So you've seen in the previous section that Siamese networks one of the many applications, is for facial
recognition.
And that's what we'll take a look at in this section.
We'll take a look at two very popular deep learning facial recognition networks, and that was a big
face and face net.
So let's to get started would face that.
So first, that was introduced by Google in 2015, and it transforms a fierce into a simple one 28 dimension
vector Euclidean space embedding.
And it uses the triplet loss function to train this network.
So you can see this is an overview of it here from the official paper.
And here's another view that actually represents it better because we're more familiar with this Siamese
type architecture where we have two similar system networks.
That's the first net convolution that works here.
They create the embedding that's the one by 128 vector, and then we just find Euclidean distance between
them.
And that's pretty much it.
That's how the FirstNet algorithm works.
Now, you may have noticed something called one shot learning using FirstNet.
Well, I don't have a slide on one shot learning.
However, I should have created one.
Basically, one shot learning is the ability to add just one new data input into the database and have
the network be able to recognize that face and future.
You don't need hundreds of samples of this data.
You just need one instance of that face, and you can add it to your database and know the classifier
can figure out if a face belongs to that face.
That's effectively what one shot learning is.
Few shot learning is technically the same thing, however.
It uses a bit more faces, but the whole point of view means that you don't need hundreds of images.
You just need a handful.
So that's the advantage of using the same year networks do because they learn to encapsulate embeddings
for the dataset they've been trained on, which is feces they can now add as pieces accordingly.
You just create the embeddings here final, including distance between them.
And that gives you a similarity score that you can then use to classify.
So you can basically take a face and then look it up to a similarity.
Search to all the faces in your database and a face that's much more similar as a face that most likely
matches that except.
This so let's take a look now at Viji Face.
Well, VIDEO First was introduced by the same research group in Oxford University that produced a digital
network.
It's not officially called the big fix.
The paper, published in 2015, was called deep fierce recognition, and they had a few follow up papers
after that.
They use a gun chip that loss and an embedding vector that was much bigger than fierceness.
It's two thousand six hundred and twenty two or ten twenty four, depending on the configuration.
It had different network architectures and network configurations, and input image size being 224 by
224, which is fairly large, actually.
So this is the biggest architecture.
You can see this in a number of convolution layers here that output to this one dimensional vector here,
that's one like two six two two, and you can see some of the results here.
Actually, these are the results you can see the data set.
The trend on that was had two thousand six hundred twenty two identities.
So it looks like they match this number of putting this here, and it consisted of 2.6 million images.
So you had a lot of instances of multiple faces, like maybe a roughly a thousand faces per subject.
So here's a summary of the big performance.
You can see how well it performs on this on their dataset.
So you can see it got ninety eight point nine five percent accuracy.
And when comparing it to a different dataset, here you can see the accuracy that's always here, where
they have a parameter called equal 100 plus embedding, landing different network configurations, and
you can see it got ninety seven point three percent accuracy.
That's pretty impressive, isn't it?
And LSW, by the way, it stands for level cases in the world.
It's a very popular facial recognition dataset that exist out there for computer vision researchers
to use and train and compare models so it will stop there for now.
And then the next section will go back to our lab notebooks and we'll start looking at facial similarity
using PDG face and then do some facial recognition with it and then look at using FirstNet in PyTorch,
as well as some other facial recognition libraries.
We'll take a look at afterward.
So stay tuned.
We've got a lot of facial recognition experiments coming up.
Thank you.