Original subtitles

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Hello and welcome to the course on computer vision.

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Today we're talking about the viola and Jones algorithm.

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This is the algorithm that lies at the foundation of the open CV a library.

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And this is one of the most powerful to date algorithms for computer vision.

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Let's have a look.

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So this hologram was developed by two people.

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Paul Viola and Michael Jones and it was developed in 2001.

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How crazy is that.

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It's been like over 16 years since then and it's still one of the most powerful algorithms in the world.

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It's slowly being surpassed by deep learning.

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But nevertheless it is in its simplicity it's so powerful that it is still being used for computers

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and it's not just detecting faces on images.

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This is actually for real time computer vision detecting faces on videos as well.

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That's how powerful it is.

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The voltage on the algorithm consists of two stages.

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The first stage is the training.

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The second stage is the detection.

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So training of the algorithm and then detection of the actual faces in application.

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And we are going to start off by talking about detection.

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This might be a bit counterintuitive because you think you want to start with training but it just makes

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it be much clearer if we start with the detection and understand how it works.

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Once it's already trained up once everything is in place we will see how it works in action.

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And that way when we're talking about training everything will make much more sense why exactly certain

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things are structured in certain ways.

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So here we go.

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Let's get started.

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We've got a photo here and we're just going to call this person Jaideep to keep them anonymous but they're

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totally comfortable for us using this photo.

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And yet I actually want to add LUNs friends and yeah so JD is is a great example of a photo here because

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you can see the face it's a frontal face and that's exactly what the voltage on the algorithm looks

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for.

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It's designed to look for frontal faces not on us not somebody looking to the side or up or down.

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It's like most of the time it's performs the best with frontal face.

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That's what it's designed for.

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And the first thing we first step that we do in the well Joe's algorithm is that it is turned into a

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gray scale.

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So for simplicity's sake it's just easier to work with grayscale images results are still astonishing.

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And there's just less data to process.

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That's why it turned to gray scale.

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Then once the face is detected Jones algorithm finds that actual location or face on the color image

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so you won't even notice that it's working grayscale but in reality in the background while Jones algorithm

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is working with a greyscale version of your image.

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And so what the very low Jones algorithm does is it starts looking for the face its outlines like a

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little box starts from the top right corner top left corner and moves to the right.

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Step by step and it's looking for the face.

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And how is it looking for the face.

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And this is what we'll discuss in the further towards.

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But for now we're just going to say it's looking for certain features of the face and by features.

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For now we're just going to mean that it's looking for eyebrows eyes the nose the lips the chin the

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forehead the cheeks and so on.

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So how exactly it can find them we'll find out further down the track.

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But philologists agree that it's looking for those features so in this box it can see an eyebrow.

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So it's like looking through out through the pixels in this box is going like k looking looking looking

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looking for any of those features and it can detect an eyebrow and then it thinks OK this might be a

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face but then it realizes that for it to be a face as to be an eye there is to be two eyes a nose as

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mouth and looks for those other features.

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And then as soon as it doesn't detect an eye like it looks at this whole box it doesn't detect any eyes

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at all.

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It understands for itself this is not a phase.

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OK.

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So let's move on.

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So it moves on.

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There you go.

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Now it's moved a bit to the right.

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It again looks through the detects an eyebrow again.

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So does that same thing doesn't detect any eyes.

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So then it's on her face.

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McCain keeps moving and keeps moving.

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Aha now it can detect two eyebrows.

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But again the problem is there's no eyes.

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It's not a face.

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It's moving it's moving one mile Barrow nothing nothing nothing then skips down.

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OK so now now it goes to the image you can see an eyebrow.

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Yes.

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OK.

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That's the score then it looks for an eye.

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You can see the eye.

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That's great.

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Then next it might look for the nose and it's looking so it looks it finds the eyebrow Yes that looks

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again finds the eye.

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Yes.

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And then looks for the nose there's no nose so it's probably not a face because the face has to have

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nose keeps going again and keeps going.

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And then at this point it might say OK I see something that might think that this is a nose because

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it's a very I remember it's a very very kind of basic algorithm.

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And from the features that we'll discuss you might you'll see that it might actually think that this

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part is a nose or in its own right.

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And so even if it thinks that this is a nose it realizes that there's no eye there's no other eye or

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there's no other eyebrow.

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So he keeps looking keeps looking.

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Then here it might or do you think that this is I eyebrow eyebrow.

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I know.

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But then you will see that there's no face there's no mouth there's no cheeks there's not.

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None of these features that are over here at the bottom.

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There's no chin.

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If it's that if that's what it's looking for again.

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And all these things depend on the training of the Algor which we'll discuss further down.

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So we will understand this is not a face.

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Ok keeps going keeps going keeps going.

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And so here you can see now this time it's definitely got an eye an eyebrow a nose but no mouth no other

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or no eye or second eye or second eyebrow.

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So it keeps going.

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And then here this is almost the face but there's no MUF.

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So again discards this and it keeps going keeps going keeps going keeps going and then finally when

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it gets somewhere here it can see that it's got both eyebrows both eyes and nose and a mouth.

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So then it highlights this as a very high very high potential to be a face.

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So it for instance makes his boxers and then keeps going again and now in this box you can see another

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face it can see two eyebrows two eyes and nose and mouth.

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So it highlights that again it keeps going on.

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So after this box office he goes over here it can no longer see the full eyebrow or the full eyes so

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this is no longer a face.

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So he keeps going it's going from the face.

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And then finally here.

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No eyes or no face.

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So there you go.

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That's how it scans this whole image and the size of this box varies because faces can be small or large.

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So you've got an image might have a face of there is this small face.

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So this box this will happen many times there'll be a box.

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This box will be a bigger box will be a small box lots of different boxes.

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And also if you notice like we had very large steps so here you can see the step is quite large and

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also the vertical step is quite large.

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In reality the steps are smaller.

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The books are more frequent.

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This is just a disposition for us to get the intuition.

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And so in reality you might this face might have not been detected just twice might have been detected

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because the box went through many times.

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What were the steps of the box were much smaller this facemask it might have been detected many more

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times something like that.

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You know each box says that there's a high likelihood of being there.

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And when a lot of boxes overlap that means that that is most likely a face and then it will detect the

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face there.

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And that's how you see that Green Square in your Facebook or in in in any other kind of like phase detection

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application that you might have on your phone or and things like that again.

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They might be using other algorithms but the ones that are using the computer vision algorithm which

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Jones created.

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That's exactly how they work.

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And then it all just find the same box the position the same box on the color image too.

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And that's exactly what you will see.

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You see the grayscale you see the color.

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And so there you go.

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We found J.D. found his face.

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So that's in a nutshell how the vial of Jones algorithm works.

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I know it's very basic very straightforward but it was important for us to understand how this box travels

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through the image what exactly happens because now we're going to start building on top of that and

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we're going to talk about the training like for the Donald who about the training for now will talk

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about the features then we'll talk about you know some hacks on how this this process is can be can

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be expedited and how this happens much faster and more efficient.

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Now if you would like to do some additional reading then the best place to start is the original paper

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by pull oil and Michael Jones.

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It's called Rapid Oggi dictation using a boosted cascade of simple features.

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It's actually a very simple paper even though the name might be complex.

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The the language is very friendly it's very easy to read I highly recommend checking it out.

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Maybe not at this stage maybe you know maybe you want to go through tutorials first and then read it

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after you finished the section.

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But it will be very helpful.

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The talk in a very human language are very easy to understand.

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And yeah.

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And so here you can actually see an example of some of the features which are used in the Jones algorithm

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and we'll be talking about these in the next tutorial and I look forward See you next then.

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Until then enjoy computer vision.