Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking the podcast.
Speaker 1: Then it looks at the future and says, then I
Speaker 1: saw her face. Now I'm a believer. I'm Jonathan Strickland,
Speaker 1: I'm Lauren Boa, and I'm Joe mcformick. So I saw
Speaker 1: something absolutely delightful the other day on the internet. You
Speaker 1: shared it with us, yes, and then you thought this
Speaker 1: could be a good blog post, and I said, to
Speaker 1: heck with that, that could be a great podcast. That
Speaker 1: is right, That is what you said, because you know
Speaker 1: a good podcast when you see one, which is odd
Speaker 1: because it's an auditory kind of thing that we do.
Speaker 1: But anyway, well thanks a pun. That would have worked
Speaker 1: if we'd already announced what today's podcast was about. What
Speaker 1: we're about to get to it. Um. So I found
Speaker 1: it was through Alexis Madrigals Five Intriguing Things newsletter, which
Speaker 1: if you're not subscribed to that, you should check it out.
Speaker 1: It's excellent. Um. The link was to a project of
Speaker 1: a Korean artist group that's I believe made up of
Speaker 1: two people, Shin Sung Bak and Kim Yong hun, and
Speaker 1: the project was called cat or human. Okay, So for
Speaker 1: those who are listening who have not had the benefit
Speaker 1: of seeing this particular project, what do you how would
Speaker 1: you describe what cat or human is? First of all,
Speaker 1: it's not horrifying, No, it's it wasn't like a taste test. No.
Speaker 1: It was also not one of those animal human hybrids
Speaker 1: that I keep hearing about from the stuff They don't
Speaker 1: want you to any dr Moreau, none of that. No,
Speaker 1: it is merely a collection of fun of images from
Speaker 1: two different algorithms, and it's marking where two different algorithms
Speaker 1: went wrong. One of them was an algorithm that was
Speaker 1: created to recognize human faces and say that's a human
Speaker 1: face just by looking at a picture. Another was an
Speaker 1: algorithm that was created to recognize cat faces by looking
Speaker 1: at a picture and saying, hey, that's a cat face.
Speaker 1: Of course, what's great is when they mess up, specifically
Speaker 1: when the cat recognizing algorithm recognizes a human face as
Speaker 1: a cat face, right, and when the human recognizing algorithm
Speaker 1: recognizes a cat face as a human face. So they
Speaker 1: collected images from both of these sets of mistakes and said,
Speaker 1: according to these various algorithms, these people are cats and
Speaker 1: these cats are people. That's exactly right. It's kind of
Speaker 1: like a weird yearbook. Yeah, so that is what they
Speaker 1: did from what I can tell that. You should check
Speaker 1: it out on their website. The the cat people, you know,
Speaker 1: look like cat people. I agree. I mean it's I
Speaker 1: guess a little bit. They mostly look like people. Yeah,
Speaker 1: they may have some features that are slightly feline. When
Speaker 1: we say cat people, we mean people who may have
Speaker 1: certain feline features. We don't mean people who have twenty
Speaker 1: seven cats. No, not that thing, and also not the
Speaker 1: thing from like doctor Who, not those cat people either. Um,
Speaker 1: maybe slightly cat like eyes. Yeah, we're not talking to sleepwalk.
Speaker 1: They tend to be very happy looking people with like
Speaker 1: big grins ches cheshire people, which is funny because cats
Speaker 1: are so evil. But anyway, the selected cats I'm looking
Speaker 1: at now that we're yeah, the cats that were identified
Speaker 1: as human, they all look really sour. They're not smiling cats.
Speaker 1: I don't know what like, So grumpy cat might have
Speaker 1: ended up being classified as a grumpy cat is not
Speaker 1: one of the ones I've seen. Instead, these are cats
Speaker 1: usually that are kind of wide eyed and looking straight
Speaker 1: on at the camera and in this kind of like
Speaker 1: what what so the reason why we even bring this up,
Speaker 1: as as amazing as this particular art project is, it
Speaker 1: does have to do with the technology of the future,
Speaker 1: we promise. Yeah, we wanted to talk about facial recognition technology.
Speaker 1: What that has to do with artificial intelligence? What is
Speaker 1: it actually doing, how is it doing that, and what
Speaker 1: could that possibly mean to us? Not to mention some
Speaker 1: interesting looks at the future and apparently at least one
Speaker 1: segment that will scare the pants off me, but I
Speaker 1: don't know what it is yet. Yeah, So this we're
Speaker 1: we're going to go on a journey of discovery and
Speaker 1: some of us are going to be discovering right along
Speaker 1: with you. So to jump into this, Uh, facial recognition
Speaker 1: is part of artificial intelligence. When we talk about AI.
Speaker 1: We've said this several times on this show. AI is
Speaker 1: actually a very broad term, right. We often think of
Speaker 1: it in terms of strong AI, some sort of computer
Speaker 1: that can quote unquote think like a person. But in reality,
Speaker 1: artificial intelligence incorporates lots of different elements of intelligence, right, right,
Speaker 1: And one of those elements would be making sense of
Speaker 1: the kind of data that makes sense to animals and
Speaker 1: humans but not to computer. Right, So, like visual data
Speaker 1: I can look at Joe and I can recognize that
Speaker 1: that's one, that's a person. Two, I can recognize that
Speaker 1: it's a male person. Three, I can recognize that it
Speaker 1: is a person I already know for that that person's
Speaker 1: name is Joe. Like all of this kind of stuff
Speaker 1: a batman costume right now, right exactly not right now,
Speaker 1: that you're not a cat, Yeah, they're there are all
Speaker 1: these things. And I can also have all these other
Speaker 1: associations with Joe that that you know, factor into who
Speaker 1: he is and what he is, and all of this
Speaker 1: is very natural to me. But all stuff we have
Speaker 1: to teach computers right right, and it's very natural to
Speaker 1: humans to be able to I mean, you could probably
Speaker 1: recognize Joe from across the room even if he were
Speaker 1: turned around, due to you knowing him well enough to
Speaker 1: know like his gait and posture and like if you
Speaker 1: catch kind of a weird angle from the side of
Speaker 1: his face, probably still identify that as Joe. But a
Speaker 1: computer doesn't have that natural capacity. Yeah, if I look
Speaker 1: at if I if I meet Joe for the first
Speaker 1: time and he looks me right in the eyes, we
Speaker 1: shake ends, and then the next time I see him
Speaker 1: he's in profile, I'm still able to recognize him. But
Speaker 1: for computers that's not a trivial task, right Yeah, because
Speaker 1: they're they're relying on a way of analyzing visual data
Speaker 1: and breaking that down into data points. That makes sense.
Speaker 1: So it is a big part of artificial intelligence, and
Speaker 1: not just facial recognition, but object recognition in general. Now,
Speaker 1: to get into what facial recognition is doing, there are
Speaker 1: a couple of things we have to factor. One that
Speaker 1: has to be able to recognize what is a face?
Speaker 1: What are the elements that make up a typical face?
Speaker 1: And that ends up being sort of the baseline, all right.
Speaker 1: Beyond that, when we talk about facial recognition, we're usually
Speaker 1: referring to two different things that are common to biometrics,
Speaker 1: whether it's facial recognition, fingerprint technology, hand scans, whatever it
Speaker 1: may be. And that's verification and identification. And these are
Speaker 1: two related but different concepts. So verification might be something
Speaker 1: you should think of with thet afore of a key. Yes,
Speaker 1: verification would be something like if we needed to have
Speaker 1: a biometric system to let us into the office, so
Speaker 1: we might have it might be a two step verification process.
Speaker 1: Right there, there's one step where we might use a
Speaker 1: badge or a pen or something else to identify and
Speaker 1: say this is who I claim to be. Now. At
Speaker 1: that point we would then submit whatever happened. Biometric it's
Speaker 1: asking for, whether it's a fingerprint scan or maybe you're
Speaker 1: just looking into a camera and facial recognition software is
Speaker 1: taking a look at who you are. Well, when you
Speaker 1: created that profile for the system, when you created your pen,
Speaker 1: when you had your fingerprint scanned or whatever it may be. UM,
Speaker 1: then that's when it has uh scanned you and taking
Speaker 1: those data points and associated it with your identity. So
Speaker 1: what it's looking for is a match of the person
Speaker 1: who is standing at the doorway with the person who
Speaker 1: have a profile that it already already has set up
Speaker 1: in the system. Right, Really, this is trying to get
Speaker 1: a computer to do what a human would already do,
Speaker 1: looking through the people of a door. So someone knocks
Speaker 1: on your door says hello, it's Joe, and Jonathan comes
Speaker 1: to the door and looks through the people and sees
Speaker 1: that's not Joe, that's a werewolf. Then I'm denied access.
Speaker 1: But at least you have some other questions you need
Speaker 1: to answer before I will open that door. You know,
Speaker 1: you might have had a rough n hope. Yeah, yeah,
Speaker 1: you might have let your beard go a little bit
Speaker 1: um or or or you know, like Jonathan could also
Speaker 1: look through the people and go, that's Joe. He's wearing
Speaker 1: a jaunty hat. But it's definitely still Joe and let
Speaker 1: you in, I mean, depending on his feelings about johnty hat. Right, Yeah,
Speaker 1: do you take your hat off if you come inside?
Speaker 1: That's Joe and clown makeup, but I still recognize him.
Speaker 1: I'm definitely not come anyway. Yeah. But so that's the
Speaker 1: sort of artificial intelligence angle on it. It's just trying
Speaker 1: to recreate what happens in your brain and when you
Speaker 1: look through the peepole at somebody before you let them in.
Speaker 1: And these are the kind of systems, by the way,
Speaker 1: that can can cause problems for people, even when they
Speaker 1: have a legitimate profile set up. I did an interview
Speaker 1: with Dr John Padfield for How Stuff Works where we
Speaker 1: talked about biometrics in general, and he talked about this
Speaker 1: is this is related. It's not facial recognition, but it's related.
Speaker 1: He talked about working at a job where he had
Speaker 1: to put in a pen and have a hand scan
Speaker 1: before he could come into the office, and he normally
Speaker 1: wears a class ring, and one day he left the
Speaker 1: ring off, and so when he went to have his
Speaker 1: hand scanned, his hand had a different readoubt than it
Speaker 1: normally does because he didn't have this enormous lump from
Speaker 1: a class ring on his finger, and it would not
Speaker 1: let him into the building. He had to wait until
Speaker 1: that actual human game and verified that he was who
Speaker 1: he claimed to be before he was let in. Same
Speaker 1: sort of thing can happen with facial recognition software. Okay,
Speaker 1: but there's another thing that facial recognition software might need
Speaker 1: to do, and that's, as you said, identify vacations. So
Speaker 1: unlike looking through a people and saying, oh, okay, is
Speaker 1: the person standing there who they said they were through
Speaker 1: the door, It's more like looking at a person and saying,
Speaker 1: who's that? Yeah, do I recognize this person at all?
Speaker 1: So verification makes it easy for the system because it's
Speaker 1: just looking for a match of who you claim to
Speaker 1: be and who you are. Identification is more tricky. It's
Speaker 1: saying who are you without any of that who you
Speaker 1: claim to be? Stuff? Right? Right? Right? You don't well,
Speaker 1: I mean, you might have a set of vague profiles
Speaker 1: set up. But yeah, so this would be something like
Speaker 1: a system where the police might use an identification system
Speaker 1: in order to figure out the identity of someone who
Speaker 1: has committed a crime and try and match that against
Speaker 1: their entire database of all the photographs that they have stored.
Speaker 1: This is particularly tricky because again, you can run into
Speaker 1: those situations where the image you get during whatever it
Speaker 1: was that happened may not be the same sort of quality,
Speaker 1: the and and condition as the image that you originally
Speaker 1: have in your database. But we'll talk more about that.
Speaker 1: But yeah, I was going to say that's exactly right,
Speaker 1: because one of the things you might be thinking now
Speaker 1: is identification is harder for a lot of reasons. And
Speaker 1: one of the reasons verification is easier is you typically
Speaker 1: when you're verifying someone's identity, can assume that they're being
Speaker 1: cooperative with the scan, that they're looking directly into the camera,
Speaker 1: that you've set up a lighting situation that's conducive to
Speaker 1: to measuring certain bits of their face. Exactly right, They
Speaker 1: want to be verified, and they're getting in the best
Speaker 1: possible scenario for the system to do that identification. You
Speaker 1: don't know they might be willing to let you scan them.
Speaker 1: They might not be willing, they might not be aware.
Speaker 1: You can't depend on them trying to get in position
Speaker 1: and make it easy for the computer or the hardware.
Speaker 1: But all of us will make more sense once we
Speaker 1: have explained how this facial recognition technology works. So what
Speaker 1: are these computer programs looking for now? Basically, what they're
Speaker 1: doing is breaking down faces into a collection of landmarks,
Speaker 1: right of identical identity points I should say not identical points,
Speaker 1: but identity points. So they're looking and they, depending on
Speaker 1: the system, they break down the face in a different
Speaker 1: way and they tend to get obviously more high resolution
Speaker 1: as the technology evolves. But what they do is they
Speaker 1: look for points like the the corners of eyes, the
Speaker 1: width of the nose, the length of the nose, the
Speaker 1: relationship between the nose and the mouth, the relationship between
Speaker 1: the eyes and the mouth. All of these are little
Speaker 1: data points, nodal points that the system can look at.
Speaker 1: And then it's all the relationships between these points that
Speaker 1: make up the identity. Right, So it's not just oh,
Speaker 1: there's Joe, Joe has two eyes. That's not quite specific enough.
Speaker 1: It's talking specifically about things like the width of the
Speaker 1: eyes from one another, the distance between the the the
Speaker 1: inner part of the eye from one to the other,
Speaker 1: or the triangle that the eyes make with the nose. Uh.
Speaker 1: Imagine that, but multiplied by a dozen or or a hundred,
Speaker 1: and you've got all the different little points that make
Speaker 1: up the the landscape fingerprint, if you will, of Joe's face.
Speaker 1: So that when the system looks at another face, it
Speaker 1: compares those data points, and it looks for those data
Speaker 1: points and then compares it against what's in the database already,
Speaker 1: And if there's a match that reaches a certain threshold,
Speaker 1: then the system says that's Joe. Now, if it doesn't
Speaker 1: reach that threshold, it may say it's someone else, or
Speaker 1: it may say I do not know who this person is.
Speaker 1: I don't have a record of this person, so, uh,
Speaker 1: you know, I can't help you. Um. And it's all
Speaker 1: based on statistical probability, right. So we talked about this
Speaker 1: before in the past. IBM S. Watson is the example
Speaker 1: I love to use, where Watson on Jeopardy would only
Speaker 1: give an answer if it met a certain threshold of certainty,
Speaker 1: and if it was below that then Watson wouldn't guess.
Speaker 1: So that was kind of a calculated UH risk on
Speaker 1: the part of IBM S teams saying, well, we don't
Speaker 1: want it to guess incorrectly because there's right. Um, some
Speaker 1: some of these programs will, for example, if it's not
Speaker 1: super sure who a person is, will spit out a
Speaker 1: series of guesses like like maybe for for human identification. Yeah, yeah,
Speaker 1: they're in fact, usually what you'll do if you're talking
Speaker 1: about if you're talking specifically about identification not verification, and
Speaker 1: you're and you're looking at a person and you're trying
Speaker 1: to mind an enormous database. Let's say that there are
Speaker 1: thousands of people that are within this database, because in
Speaker 1: law enforcement databases there can be many thousand people. Then
Speaker 1: you usually have a couple of different passes at this
Speaker 1: where one past will narrow it down to a certain
Speaker 1: group of people that that fit the general UH scan
Speaker 1: that goes on, and then they'll start looking for a
Speaker 1: more in depth comparison to see which ones are the
Speaker 1: most likely to be the same as the target, the
Speaker 1: one that was captured whatever image or video was that
Speaker 1: you're you're analyzing. So uh, they start looking at things
Speaker 1: like the depth of your eye sockets, the width of
Speaker 1: your nose, your cheap bone shape and height, your jawline.
Speaker 1: When I when I read about this kind of stuff,
Speaker 1: it makes me think of the character creation process of
Speaker 1: something like Skyrim, where you get into the minute details
Speaker 1: of what your character looks like. You're like, well, all
Speaker 1: those things are the things that facial recognition software actually
Speaker 1: looks at to say this is the person, because it's
Speaker 1: what makes you look like an individual human being. Aren't
Speaker 1: there some cat people in Skyrim? Yes? There are. Yeah,
Speaker 1: it all comes back. I believe they're what they're called
Speaker 1: head Head shape comes into play too, like the distance
Speaker 1: from the eyes to the ears, the distance from the
Speaker 1: center of the nose to the ears, any facial hair
Speaker 1: that you may or may not have, your hairlines, stuff
Speaker 1: like that, and that's one of the things that also
Speaker 1: can make it tricky for a verification. For example, if
Speaker 1: you have a distinct hairstyle and then you change it,
Speaker 1: then you may encounter a system that no longer really
Speaker 1: recognizes you as easily and it may mean that you
Speaker 1: have to go and update that profile with whatever you know,
Speaker 1: however you've changed. And of course there are other things
Speaker 1: that can cause some issues too, but robust systems usually
Speaker 1: will will depend upon multiple measurements. They're not looking for
Speaker 1: just one thing. Like if you're looking at a very
Speaker 1: simple facial recognition technology, where it's maybe something that's built
Speaker 1: into a digital camera, and all it's supposed to do
Speaker 1: is just detect that there is a face. Period. Yeah,
Speaker 1: it's usually really simple stuff. It's just looking for for uh,
Speaker 1: for shapes that correspond to what it thinks of faces,
Speaker 1: which means they can sometimes detect faces where there are
Speaker 1: no faces. I've seen this happen in nature photography, where
Speaker 1: a person will hold up a camera at a tree
Speaker 1: that has an interesting pattern on it and it will
Speaker 1: detect it as a face. You know, you want to
Speaker 1: exactly and you're thinking, you think, this isn't Game of Thrones.
Speaker 1: The tree does not have red eyes. Now that's not
Speaker 1: a This is not one of those trees that the
Speaker 1: Starks talk about all the time. So there are times
Speaker 1: where that kind of technology can rely on a very
Speaker 1: simplified view of what is a face. But for things
Speaker 1: that are really about verification and identification, they usually depend
Speaker 1: upon multiple inputs to make sure because I mean, clearly,
Speaker 1: if you're talking about verifying someone's identity to give them
Speaker 1: access to something that could be really uh you know,
Speaker 1: classified information or just uh top secret type stuff or
Speaker 1: just just sensitive materials. Then you want to be absolutely
Speaker 1: sure that that is who they say they are. So
Speaker 1: some of them will even go so far as to
Speaker 1: use multiple cameras to get more of a three dimensional
Speaker 1: look at a person, because we know, if you're using
Speaker 1: a single camera with a single lens, it's a flat image.
Speaker 1: You know, it's once you see in a regular movie
Speaker 1: or a regular picture, regular photograph, you don't get any
Speaker 1: real sense of up there. I mean, you can see
Speaker 1: contours and stuff, and you can tell if something is
Speaker 1: closer or further away, and if something has been lit
Speaker 1: really well, it might look three dimensional to you and
Speaker 1: you might even your your brain plays a little bit
Speaker 1: of a trick, but it's still a two D image, right,
Speaker 1: And so if you're using a verification system or any
Speaker 1: identification system that's reliant on a single camera, then that
Speaker 1: makes it a little more complicated too, because it's looking
Speaker 1: at three dimensional images but representing it as a two
Speaker 1: dimensional image. So it that that has some elements of
Speaker 1: difficulty there. If you have multiple cameras, then you can
Speaker 1: get around that. You can rely on multiple inputs to
Speaker 1: figure out how you know, how sure are you that
Speaker 1: this person is who you think they are? Okay, but
Speaker 1: we know facial recognition is not perfect yet. So what
Speaker 1: are some of the challenges that are really facing facial
Speaker 1: recognition software today. Well, facial recognition faces many problems, Joe,
Speaker 1: thank you for throwing it to me like that. Uh, well,
Speaker 1: we talked about the lighting issue. That's a really good example.
Speaker 1: So if I go to create my profile, uh for
Speaker 1: the company I work for. Let's say that I'm working
Speaker 1: for you know, um M six, I've somehow gained clearance
Speaker 1: to work in Britain's Superspy uh Academy. And I don't
Speaker 1: know how I've managed to get there, but I don't
Speaker 1: really doubt that you have. I think that's a pretty
Speaker 1: likely Yes, this accident I'm putting on is actually not
Speaker 1: my real accent. But at any rate, let's say that, uh,
Speaker 1: I need to go and fill out my profile, and
Speaker 1: so they put me in a well lit room, they
Speaker 1: take my photograph from several angles. All of that is
Speaker 1: factored in so that I can get into Superspy Academy
Speaker 1: whenever I need to, uh, And then I walk up
Speaker 1: but Superspy Academy. Unfortunately, maintenance hasn't changed out a lightbulb
Speaker 1: that really needs to change. It's gotten very dim, it's
Speaker 1: not giving nearly enough light, and that that dim light
Speaker 1: is now casting odd shadows on me. And that might
Speaker 1: be enough to throw off a facial recognition system. I
Speaker 1: mean that you're ultimately talking about detecting light that's reflecting
Speaker 1: off of a person. And if the lighting is different enough,
Speaker 1: then from whatever your base photograph is, whatever your base
Speaker 1: amount of data is for your the system, then it
Speaker 1: may not return enough information, or at least not enough, uh,
Speaker 1: you know, a good quality information for the system to
Speaker 1: be able to identify you. This is true whether it's
Speaker 1: a security camera that's on the wall, whether it's something
Speaker 1: that's actually set up to be a verification system. I mean,
Speaker 1: it doesn't really matter what the context is. It's it's
Speaker 1: a real problem. Yeah, how about something even simpler. The
Speaker 1: way you're holding your head? Yeah, yeah, greater than fifteen
Speaker 1: degrees of tilt can really throw a wrench in facial recognition.
Speaker 1: But I've seen this in action. Actually, when I've I've
Speaker 1: looked up pictures of faces that we're supposed to be blurred.
Speaker 1: We'll get into that in a minute. But weren't a
Speaker 1: lot of the times a program that's supposed to detect
Speaker 1: a face and blur it will miss a face that's
Speaker 1: I agonal, right. Yeah. I mean if if you're looking
Speaker 1: for a specific uh series of shapes and that is
Speaker 1: what defines a face, and that those shapes are in
Speaker 1: an orientation that's different from what you would expect, then
Speaker 1: you can understand why a system would overlook that and
Speaker 1: not recognize it as a face. Yeah. So my suggestion is,
Speaker 1: if you're a criminal trying to avoid the law, just
Speaker 1: do the limbo all the time. Just lean over. My
Speaker 1: suggestion is don't go crime doesn't pay. That's my suggestion.
Speaker 1: But Joe, you know, we understand that you're in the
Speaker 1: ferious now. Now that you've let on, I am gonna
Speaker 1: have to train my superspy techniques on you. So just
Speaker 1: fair warning. Uh, but yeah, I mean they're there. We
Speaker 1: have the same thing about the single camera systems having
Speaker 1: problem detecting depth. That means that you might be able
Speaker 1: to fool such a system by holding up a photograph
Speaker 1: of let's let's say that you have captured a superspy
Speaker 1: and gotten their pin and you go into superspy Academy
Speaker 1: and you type in the in. Then you hold up
Speaker 1: a picture of said spies face to the camera. And
Speaker 1: it's just a single lens camera. It's not able to
Speaker 1: detect depth. It may think that that flat image is
Speaker 1: in fact the actual person's face. Yeah, then you get
Speaker 1: an entrance. Yeah, how about this problem. Your face isn't
Speaker 1: quite as unchanging as say your fingerprint or your retina. Right,
Speaker 1: you could gain weight or lose weight, which might change
Speaker 1: certain aspects of your face. Yeah, you could grow facial
Speaker 1: hair or shave facial hair or your hairstyle. Like we
Speaker 1: mentioned before, you might suffer an injury and you might
Speaker 1: have some scarring, or you may end up having plastic
Speaker 1: surgery or some other thing that alters your face in
Speaker 1: a way that would be enough to throw it off
Speaker 1: from a facial recognition software from being able to verify
Speaker 1: or identify it. Right, It makes it harder. It means that, uh,
Speaker 1: these systems have less variables they can work with. They
Speaker 1: need to look for data points that aren't going to
Speaker 1: be changing when these types of things happen to a
Speaker 1: person's face, and that really limits what you can look at.
Speaker 1: And I mean even even heavy eye makeup could change
Speaker 1: the shape of your face enough to throw something up. Yeah,
Speaker 1: there's some some elements of your face that are less
Speaker 1: likely to change unless you've had something radically done, right
Speaker 1: you reconstructive relative distance between your eyes and your nose,
Speaker 1: and the curvature of your nose again, unless you've had
Speaker 1: some sort of plastic surgery, the shape of your jaw.
Speaker 1: These are things that are are less easy to change
Speaker 1: without some extreme intervention. And so those are things that
Speaker 1: a lot of facial recognition software packages rely heavily on.
Speaker 1: Or it maybe that the relationship of marks that you
Speaker 1: have on your face, like a mole or freckle, things
Speaker 1: like that, things that aren't likely to change a lot
Speaker 1: unless again you go in to have them removed, right,
Speaker 1: you could apply makeup to cover them up. That also
Speaker 1: could be enough to fool them. So these are all
Speaker 1: things that that are uh that are taken into consideration
Speaker 1: by people who designed these systems. Yeah. Yeah, there's a
Speaker 1: new story that came out in August that I wanted
Speaker 1: to mention here. Um that a fourteen year old case
Speaker 1: against an alleged child sex abuser in kidnapper, a terrible
Speaker 1: guy all around, um came back into courts when the
Speaker 1: FBI caught their suspect thanks to their facial recognition software
Speaker 1: of two passport style photos of the guy who had
Speaker 1: not changed his face in the past fourteen years. Um.
Speaker 1: I mean the dude put in like a visa application
Speaker 1: to the U. S Embassy in Nepal, and the software
Speaker 1: was like, hey, hey, you guys, this is totally that guy. Yeah.
Speaker 1: So it was a really great example of of how
Speaker 1: this kind of technology can can work towards towards catching criminals,
Speaker 1: which is terrific um, but kind of under vary. This
Speaker 1: one very specific circumstance in which a very good photograph
Speaker 1: was obtained of the suspect um. And there are lots
Speaker 1: of ways in which you can mess up. That's right.
Speaker 1: For for all the times it works really big, there
Speaker 1: are lots of times when facial recognition software goes wrong,
Speaker 1: and often just goes wrong in a hilarious way. Yeah.
Speaker 1: Sometimes a uh you know, you see where the the
Speaker 1: drawbacks or the limitations of the technology are in in
Speaker 1: incredibly um profound ways, you might say, so, like like
Speaker 1: the Japanese cigarette vending machines. I remember hearing this story
Speaker 1: when it first broke I was listening to. I think
Speaker 1: it was probably seen nets Buzz Out Loud at the time,
Speaker 1: which is no longer a show, but I was a
Speaker 1: big fan and they covered this story when it first happened. Now,
Speaker 1: these vending machines were famous because they had cameras built
Speaker 1: into them that were supposed to be able to detect
Speaker 1: what the age was of the person who came up
Speaker 1: to the vending machine. So they're looking for specific features
Speaker 1: things that would indicate someone who has reached a certain
Speaker 1: level of maturity, and that was supposed to prevent these
Speaker 1: machines from selling cigarettes to underage people without checking an
Speaker 1: I D share. Yeah, yeah, because I mean it's we
Speaker 1: used to have these cigarette machines all over the place
Speaker 1: in the United States. They aren't really anywhere that I
Speaker 1: know of at this point. Yeah, yeah, you can. Yeah,
Speaker 1: you can find them in those kind of places where
Speaker 1: presumably someone's idea has already been checked before they've entered
Speaker 1: the premises. So the idea would be you would walk up,
Speaker 1: you would put you know, the camera would detect whether
Speaker 1: or not you're old enough, and then you could buy cigarettes.
Speaker 1: And what they what people discovered was that if you
Speaker 1: just walked up and held up a picture of a person.
Speaker 1: The example an adult person. Yeah, the example I saw
Speaker 1: was a picture of Bruce Willis, uh uh, like die
Speaker 1: Hard four era Bruce Willis. If you held up a
Speaker 1: picture of Bruce Willis, it would totally sell you cigarettes
Speaker 1: and probably you know, say that it was a big
Speaker 1: fan of your work. What about like a really baby
Speaker 1: faced adult. Probably not, because again it was looking for
Speaker 1: specific kind of features that would indicate adult nous. They
Speaker 1: didn't find out that you didn't have to have a
Speaker 1: very large picture for this to work. You could. You
Speaker 1: you can hold up as something as small as a
Speaker 1: three inch picture. We're talking about like seven and a
Speaker 1: half centimeter picture of an adult. They did discover that
Speaker 1: if you tried to hold up a picture as small
Speaker 1: as one inch, that did not fly. But but you know,
Speaker 1: something something like a three inch picture like you could
Speaker 1: you could just carry that around and put that in
Speaker 1: front of the camera and that was enough for you
Speaker 1: to be able to purchase cigarettes from it. So that
Speaker 1: shows you that this is not an infallible technology, especially
Speaker 1: in that particular implementation. I got another funny one for you, okay,
Speaker 1: Google street View. Yeah, so, first of all, Google street
Speaker 1: View did not always blur out faces. Uh. In fact,
Speaker 1: the reason why Google street View would blur out faces
Speaker 1: was due to the kind of uproar people had when
Speaker 1: street view first became a thing. Because what Google is
Speaker 1: made by engineers, like engineers power Google and all of
Speaker 1: Google's projects, and engineers are really good at figuring out
Speaker 1: how do we create this amazing experience, but not necessarily
Speaker 1: the best at thinking of all the implications of said
Speaker 1: amazing experience. So sure, like, you know, it's terrific that
Speaker 1: we have a picture of this storefront of a doctor's office,
Speaker 1: for example, But make the people who happened to be
Speaker 1: in front of that doctor's office that day don't want
Speaker 1: to be permanently associated with it, right, Or you don't
Speaker 1: want your picture on the Internet walking out of the
Speaker 1: liquor store or a strip joint, or maybe you do.
Speaker 1: I don't know, but there are a lot of There
Speaker 1: are a lot of cases where someone would say, uh, yeah,
Speaker 1: I even if I wasn't going to set establishment and
Speaker 1: I just happened to be walking by it at the time,
Speaker 1: Now it appears that I am giving my custom to
Speaker 1: something that I do not approve of, and I would
Speaker 1: much rather not be associated forever and ever with this
Speaker 1: particular location. Right, But of course you can't just manually
Speaker 1: blur all the faces. There's too many. You know, it'd
Speaker 1: take it would take ages, because you know, you've got
Speaker 1: these fleet of Google street View cars that are going
Speaker 1: through different neighborhoods and documenting them. So Google created an
Speaker 1: algorithm that would detect faces and then blur them automatically
Speaker 1: to halarious results. Well, it was, as I believe you
Speaker 1: said in the notes here, it's a bit overcautious maybe,
Speaker 1: So it ends up blurring some wonderful things like the
Speaker 1: colonel's face on the KFC billboard. Right. There are murals
Speaker 1: where you you see this gorgeous artwork, except there's this
Speaker 1: amazing blur part of it, or sometimes multiple blurs. I've
Speaker 1: seen images on the Internet where it blurred animal faces.
Speaker 1: So they're like some ponies by the road and it
Speaker 1: blurred the bony faces. Look, those ponies deserve just as
Speaker 1: much privacy as you and I. Can you tell a
Speaker 1: horse face from a human face? Hey? Art project horse
Speaker 1: or humans? So many jokes that I'm not gonna do it. Also,
Speaker 1: I've seen it blurring statues. Yeah, you might have a
Speaker 1: Buddha by the road and it's got a blurred face
Speaker 1: like it's a criminal on America's Most Wanted. It also
Speaker 1: does miss some others though, which which which cracks me
Speaker 1: up on on the flip side of it, Like I've
Speaker 1: seen like a like a dude sitting in a spa
Speaker 1: and his mirror image is not blurred, and it's like, well,
Speaker 1: thanks Google, Well that's that's his evil, evil twin version
Speaker 1: right in the mirror world. Yeah. I've also seen some
Speaker 1: images that were at least alleged to be from Google
Speaker 1: street View that missed people's faces, often at an angle,
Speaker 1: people who had their heads turned to the side, right,
Speaker 1: So it again, it is not infallible, and so you know,
Speaker 1: it's it may be that that that street View ends
Speaker 1: up being one of those things where some folks at
Speaker 1: Google occasionally have to go in and manually blur stuff.
Speaker 1: But the hope is that, yeah, the hope is that
Speaker 1: the algorithm catches the vast majority. And I'm sure that
Speaker 1: the company would rather err on the side of caution
Speaker 1: and have some hilarious unintentional blurring going on than miss
Speaker 1: one or has to do it all by hand, which
Speaker 1: would just be ridiculous, and it does. It does rely
Speaker 1: on users to send in feedback like if you if
Speaker 1: you find something that isn't blurred or should be blurred
Speaker 1: or either way. On on an unrelated note, I love
Speaker 1: the communities that have taken Google street viewed as an
Speaker 1: opportunity to do art projects along streets that normally wouldn't
Speaker 1: happen there. I remember there was one street where there
Speaker 1: was an entire almost like a parade, like a story
Speaker 1: that happens as you go down the street, where people
Speaker 1: had uh created these tableau all the way down, ranging
Speaker 1: from everything from uh you know, families posing together like
Speaker 1: a nineteen fifties style sitcom to Larper's in all out combat.
Speaker 1: It was amazing and it was just down one streets.
Speaker 1: I think. I'm sure I've seen Google street view capture
Speaker 1: a lot of the infamous horsehead mask. Yeah. The question then,
Speaker 1: is the horsehead mask does it blur it? Well, I guess,
Speaker 1: I guess we'll have to ask our listeners to find
Speaker 1: uh you know, instances of the horsehead mask blurred and unblurred.
Speaker 1: Maybe we can crack this mystery. But now, you know,
Speaker 1: this also leads into the fact that we have this
Speaker 1: blurring going on with street view shows that there is
Speaker 1: there are some concerns with face recognition. And it's not
Speaker 1: just the fact that are you looking up horse bording
Speaker 1: go the horse said mask Joe. We're doing a show
Speaker 1: right now. The time for research is over your all right. Uh.
Speaker 1: The other cool thing, well, not cool thing. The other
Speaker 1: thing that we have to keep in mind is the
Speaker 1: fact that there are some concerns about facial recognition. That
Speaker 1: you know, in this case, Google is using facial recognition
Speaker 1: to meet concerns, but their concerns about the technology all
Speaker 1: by itself. Right. So when we talk about officials and
Speaker 1: organizations and governments using facial recognition technology, that sounds really
Speaker 1: good when it's like, oh, they used it to catch
Speaker 1: somebody who was really bad, like a dangerous violent offender. Okay,
Speaker 1: But at the same time you have to think, well,
Speaker 1: should we be concerned about privacy? Yeah, And the answer
Speaker 1: to that is yes, we should be concerned about privacy. Absolutely.
Speaker 1: We should be concerned about pay all the time. Basically. Yeah.
Speaker 1: In the United States, more than half i think twenty
Speaker 1: six states and the District of Columbia all allow law
Speaker 1: enforcement to use facial recognition technology. Uh. In in the
Speaker 1: course of their duties as peace officers. And uh, I
Speaker 1: think there's something like twelve that do not, and then
Speaker 1: or maybe it's it's eleven that do not and then
Speaker 1: twelve that um have some form of of facial recognition
Speaker 1: technology but are not currently using it. That kind of thing.
Speaker 1: At any rate, UM, you also have this enormous database,
Speaker 1: not just of all the different criminals have been processed
Speaker 1: and taking mug shots have been taken that kind of thing.
Speaker 1: I mean, mug shots really are an early form of
Speaker 1: facial recognition that was all manual, right, It wasn't algorithms,
Speaker 1: it was algorithms. It was like rather than manual, Well
Speaker 1: that's fair fans. Yeah, well you have to sort through
Speaker 1: the pictures. But at any rate, Uh, you know, now
Speaker 1: a long process. Right now, this technology can rely on
Speaker 1: those mugshots, but it can also you know, some of
Speaker 1: these law enforcement agencies might be tapping into databases for
Speaker 1: people who have I don't know, received a driver's license
Speaker 1: or an i D, a state issued i D where
Speaker 1: they've got an image on file that is tagged with
Speaker 1: their identity because it is part of ascertaining that person's identity.
Speaker 1: It's a it's a way of verifying that person's identity. Yeah,
Speaker 1: and it also has their address and lots of other stuff,
Speaker 1: other vitals, things like that. Yeah, that starts to maybe
Speaker 1: become a problem when you realize. Okay, so all kinds
Speaker 1: of people are being visually scrutinized and perhaps in a
Speaker 1: very broadway, all considered as potential suspects in identifying faces
Speaker 1: without even really being at the scene of or associated
Speaker 1: with the crime in any way. Yeah, it's it's a
Speaker 1: worry that it turns it could quickly turn into a
Speaker 1: true surveillance state, right, not just not just that we
Speaker 1: have this capability of looking at uh, people in the
Speaker 1: in the in the context of a crime that's been committed,
Speaker 1: and trying to figure out who was there, what were
Speaker 1: their roles in the crime? Uh, and how can we
Speaker 1: get in touch with them so that we can either
Speaker 1: you know, detain them if we suspect that they were
Speaker 1: part of it, or ask them questions if they were
Speaker 1: not part of it, but they were present and uh.
Speaker 1: And it's it's also a question of letting the public
Speaker 1: know whether these systems are in place and whether the
Speaker 1: law enforcement agencies have access to them. There was a
Speaker 1: story in Ohio where there was a big backlash of
Speaker 1: of citizens who were unaware that these systems were in
Speaker 1: place and could be used, and the law enforcement response was,
Speaker 1: these systems are in place, we have our own policies
Speaker 1: in place to make sure that misuse does not happen,
Speaker 1: because if it does happen, it will be treated with severity.
Speaker 1: That sort of thing to try and let people know
Speaker 1: we are relying on these things so that we can
Speaker 1: help pursue criminals, but we're not we're not going to
Speaker 1: tolerate any kind of misuse of the system, which I
Speaker 1: think is an important I mean, they have to prove
Speaker 1: that that is in fact their policy, right right, Just
Speaker 1: saying it isn't quite enough. No, But you know, it's
Speaker 1: one of those things that is clearly you know, it's
Speaker 1: it's it's a it's a big concern. Oh yeah. And
Speaker 1: it's not only on the state levels. There. The the FBI,
Speaker 1: the Federal Bureau of Investigation here in the United States,
Speaker 1: has an increasingly robust system called the Next Generation Identification
Speaker 1: System or n g I, which includes and I quote
Speaker 1: an image searching capability of photographs associated with criminal identities
Speaker 1: um paired with this advanced increasingly advanced facial recognition software
Speaker 1: that they're building out. Um, it just rolled out this
Speaker 1: uh interstate Photo system or i p S yesterday September
Speaker 1: fIF um. Really timely news. Yeah. I mean it's been
Speaker 1: working on this for for years. I think that they
Speaker 1: had announced their intention to do it back in two
Speaker 1: thousand six or two thousand eight, um something around there,
Speaker 1: and that the contract for developing all of the technology
Speaker 1: was awarded at that time to Lockheed Martin Transportation and
Speaker 1: Security Solutions. Not a big surprise. Luckheed has a long
Speaker 1: history when it comes to surveillance. Look at the history
Speaker 1: of Area fifty one for more, right, right, yeah, um. So,
Speaker 1: so this system basically pools data from from as far
Speaker 1: as I can tell, all levels of law enforcement and
Speaker 1: criminal justice and distributes that data back out to those
Speaker 1: agencies once the FBI has collected it. So in this case,
Speaker 1: you have a kind of national system where uh you
Speaker 1: might you know, otherwise have to rely upon a regional system.
Speaker 1: Like a state might have access to all criminals that
Speaker 1: have been processed in that state, but if it's a
Speaker 1: criminal from another state that has done something, you may
Speaker 1: not be able to link the identities easily, you would
Speaker 1: have to have interstate cooperation, which is an incredibly complex
Speaker 1: in the law enforcement world. Oh yeah, yeah, and and
Speaker 1: that's what this system is intending to do. UM. At
Speaker 1: the very least on the surface. There there are a
Speaker 1: bunch of organizations like, for example, the Electronic Frontiers Foundation
Speaker 1: that are calling for greater disclosure of UM and and
Speaker 1: interoffice checks and balances on the privacy and security and
Speaker 1: scope of this entire project UM because it's obviously concerning
Speaker 1: well yeah, I mean it's one of those things where
Speaker 1: right now, if you're talking about this is specifically uh
Speaker 1: limited to people who have a criminal background, then for
Speaker 1: a lot of people they're just going to say, oh, well,
Speaker 1: then that that's fine. But the implication here is obviously,
Speaker 1: well one, if you've been processed, that doesn't necessarily mean
Speaker 1: that you're actually criminal. Yeah, yeah, you may you may
Speaker 1: have been processed, but it was because you had been
Speaker 1: mistakenly identified as a criminally in the system or I mean,
Speaker 1: you know, in the case of and this is treading
Speaker 1: on slightly opinionated territory here, apologies guys, but you know,
Speaker 1: if you were maybe processed for participating in a non
Speaker 1: violent protest or something. That's a great example if you
Speaker 1: you could have been protesting something and then rounded up
Speaker 1: as part of an effort to clear out the streets,
Speaker 1: so after a curfew or something along those lines. I mean, uh,
Speaker 1: that at the time we're recording this, obviously the uh,
Speaker 1: the events in Ferguson are brought to mind in that case,
Speaker 1: and you could easily see where people would be very
Speaker 1: concerned about the potential misuse of the system, or even
Speaker 1: just how the system could could inaccurately identify somebody and
Speaker 1: that could very much impact their life in a negative way.
Speaker 1: I mean, it's understandable why this is a frightening thing
Speaker 1: and it needs to have a responsible and accountable administration. Right.
Speaker 1: It's really easy, I think, especially for the general, perhaps
Speaker 1: less informed public's imagination, to jump to something like minority
Speaker 1: report as an example of how wrong it could go. Yeah. Yeah,
Speaker 1: Like the example that I put here was minority report,
Speaker 1: and I was thinking specifically about how a minority report
Speaker 1: John Innerton walks around and is immediately identified and then
Speaker 1: the world responds to his identity. Uh. And we think
Speaker 1: about this in the context of the Internet of Things,
Speaker 1: about how our our environments will adapt to us, which
Speaker 1: is a great part of the Internet things. It's really
Speaker 1: this magical kind of world. But it also means that
Speaker 1: it needs to know who you are in order for
Speaker 1: this to work. And if it knows who you are,
Speaker 1: then maybe other people know who you are and they
Speaker 1: may use it for good or for ill. And right now,
Speaker 1: as it stands, we don't have like an incredible database
Speaker 1: that has everybody's picture and everybody's identity. There are a
Speaker 1: giant databases of that regionally, but it's not all linked
Speaker 1: together yet. But I see that there's a note here
Speaker 1: that's going to fill me with terror. Yeah. So this
Speaker 1: new system the FBI is setting up will be able
Speaker 1: to collect photos and other biometrics from civil sources. Um,
Speaker 1: and what does that mean? That's a terrific question, y'all.
Speaker 1: I'm not entirely share. Civil sources could include I suppose, um,
Speaker 1: businesses that had taken fingerprints and and photographs. Um, if
Speaker 1: those businesses choose to disclose that data to the FBI.
Speaker 1: Could it include the Department of Motor Vehicles because that
Speaker 1: would be everyone's driver's license. You know, that's a that
Speaker 1: is a terrific question that I do not personally know.
Speaker 1: The answer to um. But you know they'll they'll also
Speaker 1: have that capacity to collect and retain images from certainly
Speaker 1: from from crime crime scene security cameras for for later use.
Speaker 1: So you know, even if you were just walking by
Speaker 1: and would not have normally been called in, it's going
Speaker 1: to be in their files. UM. I have a quote
Speaker 1: here from their Privacy the Impact Assessment for the next
Speaker 1: Generation Identification Interstate Photosystem from that was written in on
Speaker 1: June nine of two eight. Actually so so a minute ago. UM,
Speaker 1: kind of explaining what this civil business is a little
Speaker 1: bit about. So you know part this, if you will,
Speaker 1: Authorized non criminal justice agencies and entities will be permitted
Speaker 1: to submit civil photographs along with civil fingerprints submissions that
Speaker 1: were collected for non criminal purposes. These photos may either
Speaker 1: be provided to the submitting agency by the individual or
Speaker 1: taken directly by the submitting agency. Civil photos will supplement
Speaker 1: the biographical information and narrative physical descriptions that are already
Speaker 1: provided under existing practices. Yeah, that sounds a little ominous.
Speaker 1: I mean that doesn't. That doesn't it doesn't about privacy. Actually,
Speaker 1: it doesn't say a lot is it's a lot of
Speaker 1: words that doesn't actually describe it sounds to me like
Speaker 1: we can totally ask if you have this information, we
Speaker 1: can totally ask for it, you can totally give it
Speaker 1: to us, and then we'll have this enormous database. See
Speaker 1: the issue that we bring up here, the reason why
Speaker 1: this privacy issue is really important. And you know, again
Speaker 1: there's always the argument of if you if you just
Speaker 1: behave you don't don't do wrong, then but that's the
Speaker 1: problem is who defines what is wrong. So if right now,
Speaker 1: what is wrong means you know, you're obeying the law,
Speaker 1: and the law is reasonable and the law reflects what
Speaker 1: society feels is important, then that's one thing. But if
Speaker 1: the law ends up changing where let's say, you aren't
Speaker 1: allowed to uh to associate yourself with a certain political
Speaker 1: group or a certain ideology, and then they're using these
Speaker 1: sort of systems to identify people who do associate with
Speaker 1: those things, that becomes a real problem. I think the
Speaker 1: whole if you haven't done anything wrong, you don't have
Speaker 1: anything to worry about. Argument against privacy is nonsense, false.
Speaker 1: It just anyone who says that say to them, Okay,
Speaker 1: can I read all your email now? I mean you're
Speaker 1: not a criminal, are you? Yeah? I mean Obviously, people
Speaker 1: have reasons for wanting privacy other than wanting to conceal
Speaker 1: the fact that they've committed crimes. People, you want to
Speaker 1: have some things that are kept private. You don't want
Speaker 1: people knowing more about you than you want, then you
Speaker 1: give them permission to to know. I mean, that's just
Speaker 1: a sort of basic fact about human psychology. That's perfectly normal,
Speaker 1: and we all feel it. I don't know anybody who
Speaker 1: would be like, oh, yeah, you can just read all
Speaker 1: my emails here, my text messages. Sure. And also, I mean,
Speaker 1: you know, like I know that this is a pretty
Speaker 1: common thing to to bring up in a discussion like this,
Speaker 1: but you know, go back to the to the nineteen fifties,
Speaker 1: the Cold War and the Red Scare, and the amount
Speaker 1: of like weird blacklisting and stuff that happened to to
Speaker 1: in courts, like legally in courts at the time. And
Speaker 1: of course now most people are like, yeah, no, that
Speaker 1: was our our bad are bad, sorry, guys, But yeah,
Speaker 1: you know, you could see similar rumblings Host nine eleven too.
Speaker 1: So that's the thing is that we cannot, you know,
Speaker 1: we see these these these these dark sides of human
Speaker 1: nature come up over and over again, given the right
Speaker 1: set of circumstances, and uh, you know, when you get
Speaker 1: a technology that enables a much more efficient means of
Speaker 1: sorting people in that way, And of course, I mean
Speaker 1: obviously there are some concerns there. So again the big
Speaker 1: concern here, keeping in mind, technology itself is neither good
Speaker 1: nor evil. It's how we employ application. So it's one
Speaker 1: of those things that if we are responsible, if we
Speaker 1: hold the people who have the keys to this technology
Speaker 1: responsible and accountable, then we have a much better chance
Speaker 1: of making sure it's being used in an appropriate way. Right.
Speaker 1: And another thing about technology is I mean you can't
Speaker 1: stop it. That's also true. We're not going to stop it.
Speaker 1: It's going to happen. So it's better to set in
Speaker 1: place standards for how it should be. Can't and say no, no, no,
Speaker 1: we can't go there. Somebody's going to go there. It's
Speaker 1: better to do it, right. Yeah. So here's the thing.
Speaker 1: Now that we have these concerns, I'm starting to wonder,
Speaker 1: are there ways to make my face undetectable? Um? Yeah,
Speaker 1: I mean basically yes, yeah, I mean apart from just
Speaker 1: like sticking knives in it and stuff. I yeah, No,
Speaker 1: I don't necessarily recommend that Joe. Um, lots lots of
Speaker 1: people are are working on that kind of thing. Um
Speaker 1: that there's an artist by the name of Adam Harvey
Speaker 1: who's gotten a lot of press for his ideas and
Speaker 1: suggestions via his website, which is c V Dazzle. Um. Dazzle,
Speaker 1: of course, being an old term for camouflage that was
Speaker 1: used I believe starting during about World War One. Um.
Speaker 1: He suggests not wearing makeup that enhances your facial features.
Speaker 1: So cut out that terriffic HATI eyeliner, Joe. Um, partially
Speaker 1: obscuring the bridge of your nose, partially obscuring your eyes,
Speaker 1: creating asymmetry on the two sides of your face. Um,
Speaker 1: you using makeup or hairstyling or accessories to create unfacedlike
Speaker 1: and unheadlike shapes. Cut yeah, yeah, something like that. Sure,
Speaker 1: and um and furthermore, being careful not to really overdo it,
Speaker 1: which would make you more conspicuous to humanize. If I
Speaker 1: walk out and I've got, you know, harlequin style makeup
Speaker 1: so that facial recognition software can't tell it's me, chances
Speaker 1: are someone else is going to look at me and say,
Speaker 1: all right, well now, I'm even more alert to your
Speaker 1: presence than I would have been otherwise. Sure, sure, And
Speaker 1: you know certain things like like like masks are illegal
Speaker 1: to wear around on the street in certain cities or
Speaker 1: certain municipalities. Atlanta, Atlanta is one of those. I didn't
Speaker 1: know that. I mean, I've never tried to just wear
Speaker 1: a mask out when when Anonymous, when Anonymous were holding
Speaker 1: various protests and they were getting together in public. You know,
Speaker 1: the symbol of Anonymous is the guy Fox mask. And
Speaker 1: there were quite a few places and I think Atlanta
Speaker 1: was one of them where they were not allowed to
Speaker 1: wear those masks. It was against the local laws. So
Speaker 1: um yeah, there are there are cases of that, so
Speaker 1: uh yeah, And I mean and lots of lots of
Speaker 1: other folks are interested in designing technology to counteract the
Speaker 1: sort of thing. Back in twenty thirteen, Japan's National Institute
Speaker 1: of Informatics designed this pair of goggles that was fitted
Speaker 1: with eleven near infrared LEDs, which made them invisible to
Speaker 1: the human eye basically, but would confuse the heck out
Speaker 1: of infrared sensitive cameras, which are often used in order
Speaker 1: to capture images in low light situations, right exactly. Also,
Speaker 1: it would mean that you wouldn't be able to buy cigarettes.
Speaker 1: Of course, part of the problem is that as we
Speaker 1: we come up with these techniques to disguise our faces,
Speaker 1: I mean, whether or not we actually want to go
Speaker 1: to those links. At the same time, visual identification technology
Speaker 1: is probably just going to continue getting better and better.
Speaker 1: I mean, it can get a lot better than it
Speaker 1: where it is today. One of the things I thought
Speaker 1: is going to improve obviously is wider diffusion of high
Speaker 1: resolution imaging, because if it's trying to get your face
Speaker 1: from a low res image, which a lot of security
Speaker 1: cameras and things like that are fairly low resolution, it's
Speaker 1: going to have more trouble. If if it can get
Speaker 1: a better shot to work with, that's obviously more data well.
Speaker 1: And also if it can rely on more than one
Speaker 1: camera angle than that helps too because you get that
Speaker 1: three dimensional look. Yeah, and as so as camera technology
Speaker 1: becomes cheaper and more widespread, And as one thing, just storage,
Speaker 1: I mean, you know how much data can you store
Speaker 1: on these things. It's cheaper and easier to do more.
Speaker 1: I can see that happening. Another thing is whole body recognition.
Speaker 1: We all ever thought about this, Yeah, yeah, I've heard
Speaker 1: I mean, like I said earlier in the podcast about
Speaker 1: how Jonathan might be able to personally recognize you from
Speaker 1: just your your stance. Yeah, exactly. So maybe the way
Speaker 1: your body is shaped, your height, in the length of
Speaker 1: your show older, is your build, your your posture, the
Speaker 1: way you gesture. There was a study published in the
Speaker 1: journal Psychological Science where a team led by the UT
Speaker 1: Dallas researcher Alison Rice reported that seeing the whole body
Speaker 1: is actually a pretty crucial factor in identifying a person,
Speaker 1: especially when the facial data can is ambiguous. Um. So
Speaker 1: they did a few experiments. They asked college students to
Speaker 1: look at images of people who were somewhat difficult to identify.
Speaker 1: So they were either two different people who look similar
Speaker 1: or the same face, or the same person in two
Speaker 1: different pictures where their faces looked fairly different, and they
Speaker 1: judge these by having a computer algorithm look for the
Speaker 1: ones that we're giving them trouble. Um. So the students
Speaker 1: could judge identity pretty accurately when they were allowed to
Speaker 1: look at the person's face and whole body, but not
Speaker 1: when they saw only the faces. And the funny thing
Speaker 1: was the students seemed to think they were relying on
Speaker 1: faces to determine identity, but the researchers used eye tracking
Speaker 1: software to show that they were actually looking at the
Speaker 1: rest of the body basically for supplemental data when the
Speaker 1: faces were somewhat ambiguous. And so the way this applies
Speaker 1: to the software and hardware we're talking about is that,
Speaker 1: obviously there's more data to account for. If you you
Speaker 1: can take a picture of somebody's whole body, that gives
Speaker 1: you a lot more to work with than just the face. Yeah, yeah, yeah,
Speaker 1: these are the I mean, obviously, anything that's involving biometrics.
Speaker 1: The ultimate answer here is that the more data you have,
Speaker 1: the more sure you can be of the result. That's
Speaker 1: exactly right. And one of the things is another advantage
Speaker 1: that's going to be given to the people identifying you
Speaker 1: in the futures that we're all giving up tons of
Speaker 1: data right now. Yeah, I'm sorry, I was posting a
Speaker 1: Facebook What are you talking about. We upload pictures of
Speaker 1: ourselves and our friends and our family members and our
Speaker 1: cats to Facebook and Google and et cetera basically all
Speaker 1: the time, did it? Yes? Yeah, I mean I think
Speaker 1: that doesn't it funny? We like complain about privacy and
Speaker 1: then we do this, yeah, yeah, and then we do
Speaker 1: we're like, check out this great vacation I had. Look
Speaker 1: how awesome my look? Yeah, totally. The projected number of
Speaker 1: photographs that the FBI is supposed to have in this
Speaker 1: new database of THEIRS by next year, I think it's
Speaker 1: something like fifty to fifty two million, and the number
Speaker 1: of photographs that are on Facebook is like seven or
Speaker 1: eight times that. It's easily Yeah, yeah, it's a lot
Speaker 1: of photographs. Well, and keep in mind that we're talking
Speaker 1: about facial recognition here, but there's also image matching algorithms. Right,
Speaker 1: So if you end up being able to tap into
Speaker 1: something like let's say that you get back door access
Speaker 1: to the behind the scenes of one of these big
Speaker 1: social networking sites where you can do a quick image
Speaker 1: matching search of a of a picture you have versus
Speaker 1: all the images that are actually contained within that social network,
Speaker 1: most of which are tagged in some way or at
Speaker 1: least are associated with a specific person, then you suddenly
Speaker 1: have an incredibly powerful surveillance tools. Yeah, and there's there's
Speaker 1: certainly concern um you know, from from activist groups among
Speaker 1: many other human people. I think that it's similar to
Speaker 1: how the n s A has definitely collected data from
Speaker 1: these private organizations and businesses that for example, the FBI
Speaker 1: might might also start tapping into that kind of thing.
Speaker 1: So again, just not to scare everybody out there, not
Speaker 1: to say, you know, never share anything ever with anyone. Rather,
Speaker 1: again I'd like to to reiterate that the answer here
Speaker 1: is that we hold people accountable, and we we do.
Speaker 1: We demand transparency for the use of these kind of
Speaker 1: technologies so that they are used responsibly because people are people,
Speaker 1: you know, and and while we often will think of
Speaker 1: these organizations as their own kind of sentient entities that
Speaker 1: operate completely independently of the human uh you know, experience,
Speaker 1: they're made up of humans. So we got to hold
Speaker 1: them accountable and make sure that they're being responsible. And
Speaker 1: there are a lot of ways that this technology is
Speaker 1: going to be incredible and really help us out, like
Speaker 1: in that Internet of Things scenario, but the way that
Speaker 1: we get there is that we make sure it's being
Speaker 1: used correctly. Yeah, so, so you know, educate yourself. Well,
Speaker 1: I'll try to remember to to do a blog post
Speaker 1: or something with some links for further reading, but you know,
Speaker 1: get out there and be aware of what your local
Speaker 1: and state and national governments are doing with your image. Yes,
Speaker 1: it's it's important. Well, I hope you guys learned something
Speaker 1: from this podcast. Make sure if you have any suggestions
Speaker 1: for future episodes. Maybe you have a question that came
Speaker 1: up during this episode, or there's just something else you
Speaker 1: always wanted to know about. What is this going to
Speaker 1: be like in the future. You should let us know.
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Speaker 1: the conversation. Because you know we're heading to the future.
Speaker 1: We don't want to leave you guys behind. So that
Speaker 1: wraps up this episode and we'll talk to you again
Speaker 1: really soon. For more on this topic in the future
Speaker 1: of technology, visit forward Thinking dot com h brought to
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