Speaker 1: Brought to you by the reinvented two thousand twelve camera.
Speaker 1: It's ready. Are you get in touch with technology with
Speaker 1: tech Stuff from how stuff works dot com. Hello again, everyone,
Speaker 1: and welcome to tech stuff. My name is Chris Poette
Speaker 1: and I am a tech editor here at how stuff
Speaker 1: works dot com. Sitting across from me, as he always does,
Speaker 1: is senior writer Jonathan Strickland. If your life had a face,
Speaker 1: I would punch it, okay, which brings us to a
Speaker 1: little listener mail. This listener mail comes from Dave, and
Speaker 1: Dave says, Hi, guys, love the show. I've been wondering
Speaker 1: how facial recognition technology works. How does it know what
Speaker 1: a face is and whose face it is? What are
Speaker 1: some of its uses fun and practical? Are there dangers
Speaker 1: and controversies around this technology? What's in the future for
Speaker 1: this stuff? Thanks for all your fascinating discussions. Have a
Speaker 1: good one, and I should point out also this email
Speaker 1: other people have asked us about facial recognition technology, so
Speaker 1: Dave's was the first one that I came across. It
Speaker 1: dated from February of two thousand and ten. It is
Speaker 1: currently as we're recording this October of two thousand and
Speaker 1: ten Dave, I'm sorry, we have lots of other topics
Speaker 1: just like that, so we got plenty of stuff. Keep
Speaker 1: letting us know what you want to hear. Yeah. So,
Speaker 1: but a lot of people have asked about face recognition technology,
Speaker 1: and we thought, I thought it would start with kind
Speaker 1: of just a brief discussion about face detection technology because that,
Speaker 1: you know, you really you build upon that. And I
Speaker 1: think a lot of us have used digital cameras at
Speaker 1: this point to have some face detection technology built into them. Yes,
Speaker 1: it's it's not uncommon, No, not at all. Now here's
Speaker 1: the thing about recognizing and detecting faces. People are really
Speaker 1: good at that. Yeah, well most people are, right, most
Speaker 1: people are. There are exceptions, of course, but the the
Speaker 1: average person who doesn't have any uh problems with his
Speaker 1: or her site or have any problems within their brain
Speaker 1: that makes it difficult to detect and recognize faces they check,
Speaker 1: they usually see it right away, right, you know, you
Speaker 1: just look at a person, you see their face, you
Speaker 1: recognize that person if you've if you've seen this person before,
Speaker 1: more often than not, you'll recognize that person. Computers aren't
Speaker 1: very good at this. Uh, It's one of the things
Speaker 1: that that really is a barrier in artificial intelligence is
Speaker 1: that humans are very good at detecting and recognizing patterns
Speaker 1: and and keeping that information stored so that they recognize
Speaker 1: it when they encounter it another time. Yes, in fact,
Speaker 1: we're so good at it, we sometimes make mistakes. This
Speaker 1: is this is often comes out and paradolia, which is
Speaker 1: where you you see patterns in stuff that there's no
Speaker 1: actual pattern there, Like you look up in the clouds
Speaker 1: and you see, hey, that just looks like my buddy
Speaker 1: Joe's face up there. Well, that's that's our brain creating
Speaker 1: a pattern where there's not really a pattern there, right.
Speaker 1: But computers are not traditionally very good at this. It's
Speaker 1: actually a big computing problem and problem in the sense
Speaker 1: of how do you teach a computer to recognize patterns?
Speaker 1: Face detection and face recognition kind of that that's right
Speaker 1: up there with that problem is how do you teach
Speaker 1: a computer what is a face? Well, that's a little
Speaker 1: bit tricky. Of course. This is done with software and
Speaker 1: UM it relies on on algorithms, which are you know,
Speaker 1: a sense of instructions basically for for computers or or
Speaker 1: anything running a kind of software like this. UM and
Speaker 1: what the what the researchers and engineers have had to
Speaker 1: do is install a layer of software that enables these
Speaker 1: devices to uh you know, they had to basically teach
Speaker 1: it what I mean, not literally they we're not talking
Speaker 1: artificial intelligence, but they had to basically teach it what
Speaker 1: is a face? Right, So often software software may or
Speaker 1: may not be the right term, depending upon which device
Speaker 1: you're using. Firmware maybe because often it is hard coded
Speaker 1: directly onto a chip. But yeah, it's the same same principle, right,
Speaker 1: It's it's not it's not hardwired onto the chip itself.
Speaker 1: It's it's a program that exists. There. I apologize for
Speaker 1: my mistake. No, no, no, there's plenty of stuff out
Speaker 1: there where it is a layer of software. It's not firmware.
Speaker 1: It all depends on and we we've talked about those
Speaker 1: definitions being fuzzy anyway, right, so I'm just trying to Yeah,
Speaker 1: so anyway, fuzzy firmware. That'd be a great name for
Speaker 1: a band. That's my Devo cover band name. So so yeah,
Speaker 1: the generally what the this firmware or software, what this
Speaker 1: program is looking for are the basic identifiers of the
Speaker 1: average human face, which would be, uh, your eyes knows
Speaker 1: your your ears, your chin kind of the outline, and
Speaker 1: when it recognizes that basic pattern, the the software identifies
Speaker 1: that as a face. So if you hold a digital
Speaker 1: camera with face identification software on it or or that
Speaker 1: feature is enabled, if it sees a pattern that looks
Speaker 1: like ears, eyes, nose, and chin, it's going to immediately
Speaker 1: assume that that's a face. Which sometimes it can be funny,
Speaker 1: like you can sometimes get face recognition software to recognize
Speaker 1: a face on a on a like a picture, like
Speaker 1: it's not even a person, You're like a mural on
Speaker 1: a building. In fact, I remember Google street View. They
Speaker 1: use an algorithm that's essentially this, you know, it looks
Speaker 1: for those features in order to blur out faces. That's
Speaker 1: part of the privacy uh stance that Google takes is
Speaker 1: you know, people were objecting to having their their pictures
Speaker 1: on Google street View. So what Google did was they
Speaker 1: create this algorithm that looks for the human face and
Speaker 1: then it applies a blurring layer over it so that
Speaker 1: you can't tell who that is. Right. Well, I've seen
Speaker 1: that work on things that were not human beings, mostly
Speaker 1: on things like like billboards or there was one that
Speaker 1: was a mural that was on the side of a
Speaker 1: building and the the face on the mural had been
Speaker 1: blurred out automatically by Google street View. Well it is
Speaker 1: a face. Yeah, it's just an actual three dimensional face.
Speaker 1: There's a good chance that mural did not object to
Speaker 1: having its privacy, uh review, you know, violated. But anyway,
Speaker 1: so it's looking for that basic set and that's just
Speaker 1: your your your very basic face detection. It it it
Speaker 1: it says, it looks for this this pattern of images
Speaker 1: and says this is what a human face is. Now
Speaker 1: when you go to face recognition, where it's going beyond
Speaker 1: detecting a face, it's actually recognizing a face and and
Speaker 1: setting that to an identity, we get a little more complex,
Speaker 1: actually a lot more complex, true enough. Um, yeah, it
Speaker 1: does rely on those facial landmarks. Yes, you know, your
Speaker 1: eyes and nose chin, the depth of depth of your
Speaker 1: eye sockets, the length of your nose, wid your nose, yeah, nostrils,
Speaker 1: everything like that can be part of this. And and
Speaker 1: for the for the camera in this case, it's recognizing
Speaker 1: a face print sort of like your thumb print or fingerprint. Um.
Speaker 1: And so once it it can keep a record of
Speaker 1: what a particular face looks like, um, then it can
Speaker 1: can you know that? That's I think you would probably
Speaker 1: call that the first step and being able to identify
Speaker 1: a particular face. So you take a person's face and
Speaker 1: you look at these different measurements. It might be the
Speaker 1: distance between the eyes. Uh, it can be things like
Speaker 1: the width of the eyes themselves, um, the where the
Speaker 1: ears are in relation to the the head as a whole, um,
Speaker 1: the jawline, all of these kind of features that you
Speaker 1: really want to focus on. Features that are not easily changeable, right.
Speaker 1: You know, like hairstyle would be a bad measurement because
Speaker 1: you could easily change that, right, right. You want things
Speaker 1: that won't change over time, right, So you know it
Speaker 1: might be things like again the width of the forehead,
Speaker 1: that sort of stuff. These are all called nodal points,
Speaker 1: all right, And uh, we have a great article about
Speaker 1: facial recognition technology on how stuff Works dot com. And
Speaker 1: in that article you learned that that the human face
Speaker 1: has around eighty nodal points. Now, not all facial recognition
Speaker 1: technology is going to rely on all e d of
Speaker 1: those in order to create a face print, but they'll
Speaker 1: rely on some combination of those nodal points and through
Speaker 1: the measurements will come up with a numeric value, which
Speaker 1: is the that's the equivalent of the face print. It's
Speaker 1: a numeric value that is unique to that person. More
Speaker 1: or less, identical twins actually can have the same face print. Wow, yeah,
Speaker 1: it's it's so some facial recognition technology cannot discern between
Speaker 1: identical twins. There are other kinds that rely on even
Speaker 1: more specific data that can discern between the two. But
Speaker 1: a basic facial recognition camera, Um, if you had identical
Speaker 1: twins and they really were identical, yeah, like they didn't
Speaker 1: have one of them didn't have some sort of facial
Speaker 1: feature that was remarkably different. Uh, this software might identify
Speaker 1: both as being the same person without without additional layers.
Speaker 1: And we'll get into that in a little bit. So
Speaker 1: you've got this face print, you create a database of
Speaker 1: face prints. Then when you take a picture of someone
Speaker 1: the again, the camera will measure the noal points on
Speaker 1: this person's face, compare it against the database and see
Speaker 1: if there's a match. And it's probably I'm guessing we
Speaker 1: don't really go into a whole detail in our article,
Speaker 1: but I'm guessing it's kind of like fingerprints in a way,
Speaker 1: you look for a percentage of probability that this this
Speaker 1: particular face matches one that's in the database, because you
Speaker 1: got to remember, not all of these images are going
Speaker 1: to be exactly the same. Early facial recognition technology was
Speaker 1: very limited. You had to have someone looking directly into
Speaker 1: the camera, and then you would have to have that
Speaker 1: same person look directly into the camera again later on
Speaker 1: and compare that to the database in order to find
Speaker 1: a match. If the person was looking a little to
Speaker 1: the left or to the right, the technology wasn't good
Speaker 1: enough to to compensate for that and to make a
Speaker 1: model of that person's face to really get the right
Speaker 1: measurements right. So, and you've got to think of all
Speaker 1: the other factors that play into this. It's the lighting.
Speaker 1: If the lighting is bad, those early facial recognition technologies
Speaker 1: weren't very effective. Or if the person was at a
Speaker 1: different distance. The camera has to know how far away
Speaker 1: you are in order to make valid measurements for things
Speaker 1: like how far apart your eyes are. If the camera
Speaker 1: thinks you're fifteen ft away but you're really twelve feet away,
Speaker 1: those measurements are not going to be accurate, and it's
Speaker 1: not going to find the right match in the database. Right,
Speaker 1: So this is this was definitely one of those things
Speaker 1: that was a big learning curve you had to be
Speaker 1: able to build. You had to develop the digital technology
Speaker 1: to detect distance and then accurately measure as many Noble
Speaker 1: points as possible and as little time as possible. And
Speaker 1: we're talking hundreds of a second here. Uh that that
Speaker 1: a chip is scanning a person's face and identifying those
Speaker 1: nobal points. I mean, it takes no time at all
Speaker 1: for this to happen, but it took engineers time to
Speaker 1: develop that technology. And yeah, there there are some really
Speaker 1: fascinating technologies built into facial recognition, including you know, technologies
Speaker 1: such as a surface texture analysis. It's basically creating a
Speaker 1: a another not a face print, but a skin print.
Speaker 1: Where they are doing, um, the system is doing a
Speaker 1: mathematical analogy of sections of your skin if you come
Speaker 1: before the camera. So um, if you had to say
Speaker 1: a birthmark or a mole or something, that would probably
Speaker 1: help it track down who you are because it's going
Speaker 1: to say, well, we know that this is you know
Speaker 1: in sector number seventeen. Um, you know there is this
Speaker 1: different coloration than there there would be in the rest
Speaker 1: of of the face, so um that that you know,
Speaker 1: they're very sophisticated in breaking them down into um, you know,
Speaker 1: all kinds of mathematical UH constructs to enable this to work. Yeah,
Speaker 1: And the service texture analysis is that layer I was
Speaker 1: talking about earlier about how to identify between identical twins.
Speaker 1: Service texture analysis is actually the kind of of technology
Speaker 1: you want in order to do that, because it's looking
Speaker 1: much more closely at the texture of your skin. As
Speaker 1: the name would imply, so uh, even identical twins aren't
Speaker 1: going to have identical lines on their faces, you know, laugh, lines, wrinkles,
Speaker 1: that kind of thing. There might be a freckle or
Speaker 1: a mole that's it's slightly different from one twin to
Speaker 1: the other. And this is the sort of technology that's
Speaker 1: going to pick up on that, as opposed to the
Speaker 1: basic facial recognition technology that might not it might be
Speaker 1: close enough between the two twins in order to identify
Speaker 1: that as the same person. But with this layer, it
Speaker 1: can make it more accurate. In fact, according to one company, UH,
Speaker 1: it can, which is called Identics. UH. Surface texture analysis
Speaker 1: can increase the validity or the the reliability of a
Speaker 1: scan by around which is pretty significant. I mean, you know,
Speaker 1: you know, depending upon how accurate your starting point is.
Speaker 1: The early early facial recognition technology, even when it was
Speaker 1: working well, was not working that well. It was like
Speaker 1: around a sixty success rate. And uh, you think about that,
Speaker 1: it means of the time they get it wrong, and
Speaker 1: that can be pretty serious when you consider that. A
Speaker 1: lot of the facial recognition technology that's out there is
Speaker 1: used in law enforcement practices. Yes, it's in order to
Speaker 1: identify people who you know, let's say that there's unknown
Speaker 1: suspect that is on the loose, may be used to
Speaker 1: try and identify someone like that, or it may even
Speaker 1: be used in a static system where if something happens
Speaker 1: within the view of the camera, the camera would be
Speaker 1: able to to identify that person more quickly. You wouldn't
Speaker 1: have to just you know, stare at this picture and say,
Speaker 1: I wonder if this is you know, is this a
Speaker 1: suspect or you would have compared against the database of
Speaker 1: like mug shots or whatever, and um, that can be
Speaker 1: really useful in UM in urban environments that have lots
Speaker 1: and lots of cameras that they use for law enforcement purposes,
Speaker 1: places like London where there are so many cameras on
Speaker 1: all the different street corners, but also in places like banks. Um,
Speaker 1: if somebody were unknown uh you know, unknown bank robber
Speaker 1: and they didn't where you know, some kind of facial
Speaker 1: obscuring gear. Now, I mean it would be easy enough
Speaker 1: to fool a bank camera, probably if you put on
Speaker 1: a fake mustache or something like that, because that's the
Speaker 1: kind of thing that's going to sort off the facial recognition. Yeah,
Speaker 1: there's some facial recognition technologies that are they're sophistical enough
Speaker 1: to ignore things like facial hair. It'll look at the
Speaker 1: shape and size of your face and the relationship of say,
Speaker 1: like again the distance between your eyes, and it ignores
Speaker 1: things like facial hair because again, facial hair is one
Speaker 1: of those things it's easy to to grow or remove,
Speaker 1: right for some of us anyway, ladies, hopefully not for you.
Speaker 1: But the the most facial recognition technology will ignore that
Speaker 1: kind of stuff. But yeah, if you're wearing something that's
Speaker 1: obscuring your face, that definitely will throw off facial has
Speaker 1: to and that's not gonna be able to recognize it
Speaker 1: because I can't see it. Um. So if you walk
Speaker 1: into the bank wearing a balla clava, the camera probably
Speaker 1: won't recognize you, but the security guard might have some issues, right,
Speaker 1: And this also leads us to the question about what
Speaker 1: sort of problems there can be. Well, clearly, privacy is
Speaker 1: a big concern, right. I mean, if you get to
Speaker 1: the point where you have technology that can recognize faces,
Speaker 1: then you've got your your to the point where you
Speaker 1: have to worry about you being viewed wherever you happen
Speaker 1: to be and identified as being there. And I mean
Speaker 1: that's a big problem. I mean even for people who,
Speaker 1: let's say that you are perfectly innocent, upright wonderful citizen
Speaker 1: and you've never done anything wrong, you still probably wouldn't
Speaker 1: necessarily want all these cameras everywhere identifying your your you
Speaker 1: wherever you happen to be. I mean, it's just it's
Speaker 1: it's an invasion of privacy. And because of that, there
Speaker 1: have been some experiments with this sort of system in
Speaker 1: place in various public areas that ended up getting um
Speaker 1: canceled somewhere along the project because either the public was
Speaker 1: in an uproar about it, saying, hey, this violates our
Speaker 1: privacy and I'm not comfortable with any agency tracking me
Speaker 1: like this, or the reliability was low enough so that
Speaker 1: there were concerns of Hey, I could be at home
Speaker 1: asleep and someone who looks enough like me for the
Speaker 1: facial recognition technology to think that that was me could
Speaker 1: commit a crime, and then I could be charged for it.
Speaker 1: And I mean, if the if the reliable reliability is low,
Speaker 1: like if it it wasn't that six, that's a legitimate concern.
Speaker 1: Why if you happen to be involved in one of
Speaker 1: those mistakes, like Jonathan, why did you break into Tiffany's
Speaker 1: and steal all these diamonds? And I'd be like, wait,
Speaker 1: what did I do? Now? When did that? I? I
Speaker 1: am certain I did not do that. It does not
Speaker 1: say I ever went to Tiffany's on four square, So
Speaker 1: clearly that wasn't me because I check in everywhere, right.
Speaker 1: But there can it can be a lot of positive
Speaker 1: uses of course for facial recognition. Oh yeah, no, there
Speaker 1: there are plenty of really good ones. I mean not
Speaker 1: that those aren't positive, but I mean more fun let's
Speaker 1: see uses. Of course. You know, with cameras being as
Speaker 1: sophisticated as they are, photos are geo tagged, and now
Speaker 1: you know the facial recognition software built into them, you
Speaker 1: can auto tag different photos as basically as you are,
Speaker 1: you know, importing them into your files. Yeah, that to
Speaker 1: me is absolutely amazing stuff. I was amazed back when
Speaker 1: cameras first started being able to detect faces. That to
Speaker 1: me was that to me was really really cool. I
Speaker 1: was like, hey, awesome. And then you had the next
Speaker 1: step beyond detecting faces was detecting when someone was smiling,
Speaker 1: because then they were measuring the person's mouth right, and
Speaker 1: so if a person was smiling or making some sort
Speaker 1: of facial expression that was akin to smiling, because sometimes
Speaker 1: it was like more of a grimace. Uh, your camera
Speaker 1: would merely take the picture. There's some cameras that had
Speaker 1: it where it would it would be ready to take
Speaker 1: the photo and as soon as the person smiled, that's
Speaker 1: when it would take the shot. So that way you
Speaker 1: would get the smile right. And then the next step
Speaker 1: beyond that is what you were talking about, where you
Speaker 1: would tag a photo. You take a picture of someone,
Speaker 1: you tag that that photo with the person's name, and
Speaker 1: then from that point forward, your camera compares the pictures
Speaker 1: you take against the people you've already tagged and says,
Speaker 1: oh wait, this is a person he's tagged already I'm
Speaker 1: just gonna go ahead and throw the tag on there. Yeah,
Speaker 1: that's a good point because just as the manufacturers roly
Speaker 1: on the software engineers to build the technology in so
Speaker 1: that these devices can recognize faces, you have to teach
Speaker 1: it who is who? Um? Who is whom? I should say, um,
Speaker 1: so you know, it won't know that when I take
Speaker 1: a photo of Jonathan that is Jonathan until I tell it,
Speaker 1: you know, this is who this is, and then therefore
Speaker 1: after that it will take photos right well, I reckon
Speaker 1: nize his face. It's Jonathan Strickland. And you know when
Speaker 1: I upload those embarrassing photos of him to Facebook, it will,
Speaker 1: you know, have all the impertinent information included properly, which
Speaker 1: is why every evening I have to sit down and
Speaker 1: untagged photos. Yeah, but you know, I was thinking of
Speaker 1: another uh technology facial recognition application I should say, Um,
Speaker 1: that is also pretty fun, which is uh Microsoft's connect. Ah. Yes,
Speaker 1: that's a good point. Yeah, connect is of course, that's
Speaker 1: the motion detecting right always, because that's what the that's
Speaker 1: that's what the project was called before it hit the public.
Speaker 1: How you associate a name with something and then it's
Speaker 1: sort of hard to unassociated with that. I still call
Speaker 1: segways ginger. That's it. That's going way back. So anyway, Yeah,
Speaker 1: Connect has facial recognition built into it, and it's the implementation.
Speaker 1: Like you were saying, pullet is a great idea weather
Speaker 1: weather or not it works well, I can't say because
Speaker 1: I haven't used Connect yet, but the I love the
Speaker 1: concept that when you step in front of your your
Speaker 1: entertainment center, the camera in the Connect peripheral looks at
Speaker 1: you and then analyzes your face. Does this this process
Speaker 1: that we're talking about where it measures, takes these measurements
Speaker 1: very quickly compares that to the information and database and
Speaker 1: then identifies you. So if you have previously set up
Speaker 1: and a Connect account, let's say with your Xbox, it knows, Hey,
Speaker 1: this is Jonathan. Jonathan likes to play at this particular
Speaker 1: skill level. Jonathan likes these particular games. Um, I'm going
Speaker 1: to present this this block of information and features and
Speaker 1: games to Jonathan because we already know what his preferences are.
Speaker 1: Then when someone else, let's say, Chris, steps in front
Speaker 1: of Connect, it will identify Chris and say, oh, well
Speaker 1: that's Chris. Chris like some of the games Jonathan likes,
Speaker 1: but he also likes these other games, and he prefers
Speaker 1: these kind of movies to the movies that Jonathan likes,
Speaker 1: like Chris prefers the three movies they've seen before to
Speaker 1: the vast database of movies that Jonathan has seen. So
Speaker 1: I'm just going to show them these three movies because
Speaker 1: there's no point in trying to get him to watch
Speaker 1: anything else. And then, um, but that's the thing is
Speaker 1: that it'll it'll tailor the experience to the person based
Speaker 1: upon that person's profile. Now again, like Plett was saying,
Speaker 1: you have to create a profile. You have to tell
Speaker 1: connect Hey, this is who I am. Whenever you see
Speaker 1: this face, this is the profile you should use. Now,
Speaker 1: I'm sorry you're going to say something, Well, I was
Speaker 1: just if you were going to extrapolate from that I
Speaker 1: thought of I just thought of another application similar to
Speaker 1: that that Apparently, as I look it up quickly, here, Um,
Speaker 1: other people have our way ahead of me, and doesn't
Speaker 1: surprise me in the least. Um, smart homes can do
Speaker 1: the same thing, because I know that. I remember reading
Speaker 1: a long time ago that when Bill Gates had built
Speaker 1: his you know, massive home with the Microsoft technology state
Speaker 1: of the art, and it would tell as soon as
Speaker 1: you walked into a room, uh, oh, well, you know
Speaker 1: this is Bill. He likes the temperature at you know,
Speaker 1: seventy two degrees, he likes this kind of music, and
Speaker 1: it would automatically like you could walk around the house
Speaker 1: and and I remember reading this. I don't know if
Speaker 1: it's still true or not, but they could follow you
Speaker 1: with he's like, oh, well, you know, we'll turn on
Speaker 1: the music in this room, we'll turn it off in
Speaker 1: that room because he's no longer there, because you know,
Speaker 1: we know where he is. But you would have to
Speaker 1: as I remember correctly, Um, like I said, I just
Speaker 1: thought of this on the fly and didn't research it.
Speaker 1: But as I remember correctly, it relied on some kind
Speaker 1: of r F I D technology. You had to be
Speaker 1: wearing something like like a little name tag that you wore,
Speaker 1: but you could you could use facial recognition technology instead, sure,
Speaker 1: and not have to carry anything with you. And then
Speaker 1: you wouldn't have to go, oh, well, you know, dude,
Speaker 1: I left my card in the in the laundry room,
Speaker 1: and now I'm in the living room and I really
Speaker 1: don't feel like it not right. You could tell where
Speaker 1: Pallette was based upon the game show and commercial music
Speaker 1: that you heard throughout the place. Oh nice, thank you,
Speaker 1: welcome much. For me, it would be musicals. So I mean,
Speaker 1: I'm gonna go ahead and say, like, who's doing Fossey.
Speaker 1: That's gotta be Jonathan. But yeah, I mean it would be.
Speaker 1: It would be highly useful. And there are people, as
Speaker 1: I you know, run a quick search on the search engine,
Speaker 1: that are already ahead of me on this and have
Speaker 1: you know, started implementing that technology. Of course, it also
Speaker 1: means you have to have cameras everywhere in your house,
Speaker 1: right right, Yeah, they're They're definitely tradeoffs here. It's, like
Speaker 1: I said, the big one being privacy. Um. There's also
Speaker 1: been discussions of using the story of technology for things
Speaker 1: like a t M. A t M so I'm not
Speaker 1: gonna say that, I was gonna say a t M machines.
Speaker 1: I apologize for the redundancy. It's funny how that gets
Speaker 1: into vernacular. But anyway, a t M S could use
Speaker 1: this to identify a person and then theoretically you could
Speaker 1: get money out of your checking Accounter Stavings account without
Speaker 1: having to have a pen number or anything. Did it again?
Speaker 1: I did it again twice? An all pen number. Okay,
Speaker 1: without having to have a pen or any other identification
Speaker 1: identification on you, you could just it would see your
Speaker 1: face and know that that was you and identify you
Speaker 1: with the account. Now, there are some definite problems there
Speaker 1: because if you have my technical twins, Hey, I'm gonna
Speaker 1: go get my brother's cash out of his account today. Uh. Also,
Speaker 1: I'm sorry, No, I was just gonna say, but it
Speaker 1: could cut down on skimming. It's it's a hard it's
Speaker 1: a hard thing to say because I'm reminded of you
Speaker 1: may have heard the story. It was I think a
Speaker 1: couple of years ago. Actually in Japan. Japan is always
Speaker 1: ahead of us on this sort of stuff. Japan had
Speaker 1: cigarette machines. Cigarette machines had facial recognition technology built into
Speaker 1: them to recognize how old someone is. There was this
Speaker 1: The technology was designed to look for things like wrinkles
Speaker 1: and and laugh lines and that sort of stuff to
Speaker 1: identify a person as being old enough to purchase cigarettes,
Speaker 1: because of course it's a vending machine, so otherwise you're
Speaker 1: just kind of working on the honor system. But the
Speaker 1: news broke that kids could easily bypass this just by
Speaker 1: holding up a picture of an old person's face to
Speaker 1: the camera. They just hold up the picture and the
Speaker 1: camera would detect the old person's face and say, yeah,
Speaker 1: this person could totally buy cigarettes, and then the kids
Speaker 1: could buy as much as they wanted to. Um. So
Speaker 1: there is a concern about, well, if you had a
Speaker 1: high enough resolution picture of someone's face and you held
Speaker 1: it up to the camera, with the camera just be
Speaker 1: unable to distinguish the fact that that's a a two
Speaker 1: dimensional representation of a person's face versus an actual three
Speaker 1: dimensional face, and some technology can't do that very well.
Speaker 1: But it's getting better all the time, and uh, you know,
Speaker 1: quite possible by the time this podcast goes live that
Speaker 1: will be solved. I would surprise me. I would imagine
Speaker 1: the best way of doing that would be used to
Speaker 1: be to use a two camera system where you have
Speaker 1: essentially binocular vision using a camera with two lenses, and
Speaker 1: that way you create that whole parallax issue that we
Speaker 1: have natively as human beings. As long as we have
Speaker 1: two eyes that work, we have that parallax issue. That's
Speaker 1: what allows us to to see three D and three
Speaker 1: D movies. That's part of the reason. And uh, if
Speaker 1: you created that parallax, and we're able to create an
Speaker 1: algorithm that compared the two images so that it could
Speaker 1: detect whether something was flat or an actual three dimensional object.
Speaker 1: That would get around that problem. Uh. I just said
Speaker 1: something that sounds like it's simple. It's actually incredibly complex.
Speaker 1: But that would be how that would be my approach.
Speaker 1: Just you know, you look at how do we see
Speaker 1: these things and interpret them and then how could we
Speaker 1: copy that within the realm of technology? Interesting? Interesting? Yeah,
Speaker 1: good times man. It's um yeah, it's it's one of
Speaker 1: those things like so many of the other discussions we've
Speaker 1: had where there are so many positive applications but you know,
Speaker 1: their privacy concerns and and uh, possible ways to misuse
Speaker 1: the technology. It's you know, but I'm it's really fascinating though,
Speaker 1: how they've been able to develop the technology to recognize
Speaker 1: is a face at all, you know, especially when it's
Speaker 1: somebody far away in a photo you have a group picture.
Speaker 1: Of course, I think my camera only has the ability
Speaker 1: to recognize as many as six faces at once. Yeah. Um,
Speaker 1: so there's still limitations, right, some can can do like
Speaker 1: ten or so, but yeah, it's it's still one of
Speaker 1: those things that the processors to work pretty hard to
Speaker 1: keep scanning and identifying like that. And plus there's a
Speaker 1: point where you know, if you have a far enough
Speaker 1: distance from the camera, it's probably gonna ignore it because
Speaker 1: it doesn't want to try and focus on something in
Speaker 1: the background at the expense of whatever is in the foreground.
Speaker 1: And um, yeah, it is really cool. It's it's the
Speaker 1: it's it's a step toward artificial intelligence. Is really what
Speaker 1: we're talking about, teaching computers how to to uh observe
Speaker 1: things and identify them. They're still not thinking, but they
Speaker 1: are able to identify stuff. And again, it's really just
Speaker 1: creating measurements and then assigning a numeric value to the
Speaker 1: election of measurements and using that as the identifier. So
Speaker 1: you might think, hey, that's my brother Bill, and your
Speaker 1: camera's thinking, hey that's six seven four nine dash three
Speaker 1: to a B or something like that, because it's the
Speaker 1: identifier that coincides with this list potentially very long list
Speaker 1: of measurements of that person's face. Yeah. Now, I wonder
Speaker 1: who's going to go back through history to identify people,
Speaker 1: you know, enter it into a database. People like William
Speaker 1: Shakespeare and Thomas Edison and uh, Nicola Tesla and all
Speaker 1: these other people, so that it just auto tags everything
Speaker 1: on the internet. I'm just wondering how many how many
Speaker 1: cameras are going to go out there and mistakenly identify
Speaker 1: people's fathers as Kenny Rogers. You know that website, right,
Speaker 1: My dad looks like Kenny Rogers. Yeah, so I can't
Speaker 1: believe you brought that into this. Okay, just say it's
Speaker 1: apparently a common face to have when facial recognition technology
Speaker 1: is is everywhere, we will also believe that Kenney Rogers
Speaker 1: is omnipresent. You know, you gotta no one to walk away,
Speaker 1: and I think now is the time to run. Yes, yes,
Speaker 1: don't be a gambler. Alright, guys, that wraps up this
Speaker 1: discussion about facial recognition technology. I hope that you enjoyed it.
Speaker 1: We do have a great article on the side if
Speaker 1: you want to read more, that goes into more detail
Speaker 1: about the different the different measurements that these cameras have
Speaker 1: to take and the methodologies they use. So if you
Speaker 1: really want to dive into it, I recommend that they
Speaker 1: also have some really helpful illustrations in there, And if
Speaker 1: you want to send us any questions or comments, or
Speaker 1: you just wanna be our buddies. You can check us
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Speaker 1: an email or email addresses text stuff at how stuff
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