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  • In a previous video Mike was talking about how FaceID probably work,

  • and he was telling us that this is probably based on neural nets.

  • [Mike] Exactly how this network works isn't clear, right? Even to the people that trained it.

  • In this video, I'm gonna be talking about fingerprint recognition as an alternative to do authentication systems.

  • So we've been using for a while fingerprint sensors.

  • So maybe on your phone you have one, on your tablet, or even on your laptop

  • and maybe you're wondering is that also based on neural networks and deep learning stuff?

  • Well, it could be, I'm not saying otherwise, but it's actually not normally because you don't need that.

  • You don't need to learn anything. You basically need to make able to distinguish if this fingerprint

  • impression is the same as the one being used before.

  • So basically what we're gonna be doing first is trying to extract meaningful features from your fingertip

  • and trying to characterize your fingerprint and then whenever you want to identify yourself,

  • you need to double check if these are the same what we call minutia points.

  • So let's have a look on the iPad to two fingerprints. Oh it did work. And so here we have two fingerprints.

  • And so here I have a question for you, Sean.

  • Do you think these two are from the same person? Yes, or no?

  • [Sean] This is sort of... I'm gonna call it a roundy bit here and a roundy bit on that one.

  • [Sean] And then there's kind of a triangly bit there and a triangly bit on that one,

  • [Sean] so they look similar. um...

  • [Sean] I think I'd have to cut one out, overlay it over the other one and then maybe I'd be able to work it out.

  • Exactly, exactly. So actually they do belong to the same person. It's a good job you detected that correctly,

  • but you need to be more confident next time. Otherwise, how do you unlock your phone?

  • And so... so the first process we do is call feature extraction.

  • And what you need to do first, and most algorithms will do while trying to identify the region of interest.

  • So basically that means you're gonna try to cut this out because you don't need it, right?

  • And as you can see: there are two impressions of exactly the same finger

  • and they do look slightly different because it depends on where you put your finger on the sensor,

  • and also, it's all about the pressure that you use when you are unlocking your phone, for example.

  • So that will change slightly the picture. So no two impressions of the same fingerprint will look alike.

  • So then, feature extraction. There are so many different features you can extract from here.

  • Many of them, called level one, are all about orientations. What are those?

  • Well, you see here, the orientation of this fingerprint is just like that and it goes in this direction let's say.

  • So this is what we call orientations.

  • There are two singular points which are very distinctive in a fingerprint,

  • and one of them is the core. If you look here on the left this roundy bit that you noticed,

  • it's just a loop, right?

  • So this is what they would call and identify as the core of the fingerprint.

  • And you also actually noticed this very special bit here that looks like a delta on here.

  • So these two are the two most important characteristics for a fingerprint.

  • Are they the ones that we actually use for recognition?

  • Not quite. It depends for classification, yes,

  • but when you're trying to do the matching, they are important, but they are not the only ones.

  • If I zoom in here, you will see those ridges on here.

  • So for example, you see that this one actually ended over here.

  • So what we are gonna be doing is, typically,

  • you're gonna have this coordinate annotated and then you have kind of an arrow here saying,

  • "Well, this is the direction in which I found that minutia point." So that's how they call it.

  • And you also will see nice stuff like this bifurcation on here.

  • So all of the sudden the ridge did something funny just like that. So this is a very special point.

  • So typically this will be annotated and saying,

  • "well, this is the point it changes direction. Exactly in that point."

  • So if you continue you will see plenty of those on here.

  • Here is just an end of the ridge so you can do that and get plenty of them. How do you do that?

  • Will you just address directly the picture?

  • Well, not quite because if you try you're going to detect also minutia points here on the outside of the image.

  • So the first thing is to try to cut out anything not from the fingerprint

  • Then there will be a thinning process in which using somehow kind of segmentation kind of techniques

  • You will actually try to enhance

  • those lines, those black lines here on your fingerprint

  • Something that's also distinctive is if you really zoom in here,

  • you will see some pores within the range of your fingerprint.

  • So they are quite distinctive but you require a very high resolution image to detect those.

  • So that's why they are not normally used for fingerprint matching because you need a very high quality sensor.

  • So they're gonna be based basically on

  • When they collide, two ridges, when they are ending, when they just change.

  • So these are the points that you want to get after a thinning process and a segmentation process.

  • So there we go. We have the feature extraction done.

  • So what is next? So what is next is called matching of the different sets.

  • So anytime that you put your fingerprint on it,

  • you will get a different copy, a different photo basically of your fingerprint.

  • You perform a feature extraction process and therefore you will get different sets of minutia points.

  • And as I said before,

  • depending on how much you press down your finger

  • you will get those minutia points slightly moved one side a bit up or a bit down if you want to.

  • But you can also have a very, well, nasty user somehow,

  • and then they said I just want to use this bit of my fingerprint or I want to rotate that.

  • So the problem is not that easy. A matching algorithm is typically based on an alignment process

  • of those sets and they, there are many different methods.

  • There are local methods and global methods trying to get that match sorted.

  • In this image, what you see is your original fingerprint. The one that you captured the very first time was neat,

  • and so the one actually doing the registration ask you to put the fingerprint perfectly.

  • So one thing that you may actually think are they gonna be storing directly the picture of your fingerprint?

  • Well, normally not. Normally they just saved those minutia points because they don't need to the whole fingerprint.

  • It could be, because there are different techniques for example of trying to do that correlation between images,

  • but they are slower and they are not that precise and they cannot handle well the rotation I said and

  • that differences between the relative position of those characteristic points.

  • [Sean] So what they've done here is, if you like, taking a fingerprint of the fingerprint?

  • Yes somehow yes.

  • [Sean] Yes, so they've got these points and said, "right, okay, they make that image unique."

  • [Sean] So next time we just need to make sure those points are available.

  • Exactly. So next time so the algorithm will use this as the template points,

  • and then whenever you have a different fingerprint and if you look at the one on the left, it's exactly the same one,

  • but you've put the finger slightly rotated and you don't have the full fingerprint.

  • So if you apply again the same feature extraction process you will get again a new set of minutia points,

  • but they do not align perfectly because they are rotated.

  • So feature matching algorithms

  • they will try to find a local structure like this one highlighted in that square and they will try to

  • first match and see if there are other

  • minutia points that actually align well, so in the end if you look at it

  • So you will see that actually this was the incline on

  • that picture to actually get that.

  • So the outcome of the feature matching process is basically a score that will be telling you

  • "How similar. How likely is that this fingerprint belongs to this one?"

  • And then we need to use a very simple thresholding approach to say okay this actually belongs to that person

  • Yes or no. All right. So this problem has been solved already

  • based on feature extraction plus feature matching.

  • There are other techniques as I said, but these two are the classic

  • and the ones that actually work really well. That's sorted.

  • [Brailsford] So an executable binary. The net effect of slotting that T diagram against here slightly downwards

  • [Brailsford] is to show you

  • [Brailsford] that the C you've written gets converted into binary and the net output from this process.

  • [Brailsford] It produces out a program that you probably store in a...

In a previous video Mike was talking about how FaceID probably work,

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指紋識別 - Computerphile (Fingerprint Recognition - Computerphile)

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    林宜悉 發佈於 2021 年 01 月 14 日
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