1 00:00:04,200 --> 00:00:07,200 Speaker 1: Get in touch with technology with text Stuff from how 2 00:00:07,280 --> 00:00:14,640 Speaker 1: Stuff Work dot com. Either everyone, and welcome to Text Stuff. 3 00:00:14,680 --> 00:00:18,320 Speaker 1: I'm Jonathan Strickland and I'm La. And you know, we 4 00:00:18,400 --> 00:00:22,920 Speaker 1: had to actually verify our identities before walking into the podcast, 5 00:00:23,000 --> 00:00:25,759 Speaker 1: which is just standard operating procedure here in How Stuff Works. 6 00:00:25,800 --> 00:00:28,440 Speaker 1: Oh right, it's a it's a blood sample, retina scan, 7 00:00:28,680 --> 00:00:32,320 Speaker 1: and fingerprint before we're allowed to enter any doors. Yeah, 8 00:00:32,320 --> 00:00:35,560 Speaker 1: it's really awkward. That's why most of us no longer 9 00:00:35,680 --> 00:00:37,360 Speaker 1: wait till the last minute to have to run to 10 00:00:37,400 --> 00:00:40,720 Speaker 1: the bathroom because disaster can strike. But it does mean 11 00:00:40,800 --> 00:00:43,240 Speaker 1: that we thought, hey, we should talk about biometrics, and 12 00:00:43,280 --> 00:00:45,200 Speaker 1: then we started looking into it and getting really excited, 13 00:00:45,200 --> 00:00:51,400 Speaker 1: and then we realized, hey, there's a lot to talk about, right. Biometrics, 14 00:00:51,440 --> 00:00:58,080 Speaker 1: of course, being the measurable biological or behavioral characteristics used 15 00:00:58,120 --> 00:01:01,000 Speaker 1: for for any given individual. Yes, this is what how 16 00:01:01,040 --> 00:01:05,400 Speaker 1: the FBI says, this is what the biometrics are. So, uh, 17 00:01:05,440 --> 00:01:07,200 Speaker 1: you know, it's one of those things where we knew 18 00:01:07,240 --> 00:01:09,600 Speaker 1: it was a huge topic and we decided to narrow 19 00:01:09,640 --> 00:01:14,600 Speaker 1: it down. So today we're specifically focusing on your fingerprints, 20 00:01:14,640 --> 00:01:17,920 Speaker 1: well not your fingerprints, I mean, I mean everyone's fingerprints. No, 21 00:01:18,120 --> 00:01:20,320 Speaker 1: see the person sitting next to you, No, not that one, 22 00:01:20,400 --> 00:01:23,800 Speaker 1: the other one, that person's fingerprints. That's that's the one 23 00:01:23,840 --> 00:01:28,520 Speaker 1: we're concentrating on. So yeah, because everyone has has different fingerprints, 24 00:01:28,560 --> 00:01:30,959 Speaker 1: I mean like everyone, right, And this is something that 25 00:01:31,040 --> 00:01:33,640 Speaker 1: has been known for a while but then forgotten and 26 00:01:33,680 --> 00:01:35,840 Speaker 1: then rediscovered. So we're going to talk about that because 27 00:01:35,840 --> 00:01:38,440 Speaker 1: it's kind of funny. So these days we think of 28 00:01:38,520 --> 00:01:41,360 Speaker 1: biometrics as sort of those automated ways to verify your 29 00:01:41,400 --> 00:01:44,679 Speaker 1: identity based on some sort of biological characteristic, like you know, 30 00:01:44,720 --> 00:01:48,640 Speaker 1: the eye scan or the fingerprints scan or whatever. Vocal 31 00:01:48,800 --> 00:01:52,480 Speaker 1: scans as well, you know, like the voice imprint. Lots 32 00:01:52,520 --> 00:01:56,520 Speaker 1: of different Hollywood versions of this. But you know, again, 33 00:01:56,560 --> 00:02:00,200 Speaker 1: going into that fingerprint approach, we thought that, you know, 34 00:02:00,240 --> 00:02:02,880 Speaker 1: we we look at not just how we've defined it 35 00:02:02,880 --> 00:02:05,840 Speaker 1: and how stuff works, but look at a whole history 36 00:02:05,840 --> 00:02:09,560 Speaker 1: of fingerprinting. U We're not the first podcast to do this, 37 00:02:09,600 --> 00:02:12,640 Speaker 1: by the way, certainly not. Josh and Check of Stuff 38 00:02:12,800 --> 00:02:16,639 Speaker 1: you Should Know did an episode on April called how 39 00:02:16,720 --> 00:02:20,400 Speaker 1: Fingerprinting Works, and they go pretty deeply into the history 40 00:02:20,440 --> 00:02:22,760 Speaker 1: and say many pithy things. So if you would like 41 00:02:22,800 --> 00:02:25,520 Speaker 1: to check that episode out. You can go to Stuff 42 00:02:25,520 --> 00:02:28,160 Speaker 1: you Should Know dot com right and just stick around 43 00:02:28,160 --> 00:02:30,079 Speaker 1: because we're gonna say some pithy things too. We are 44 00:02:30,080 --> 00:02:32,120 Speaker 1: going to cover some of the history, kind of a 45 00:02:32,240 --> 00:02:35,480 Speaker 1: quick overview. But first I thought I would read a 46 00:02:35,560 --> 00:02:38,679 Speaker 1: couple of little excerpts from our article on how fingerprint 47 00:02:38,760 --> 00:02:40,880 Speaker 1: scanners work, because there are a couple that I thought 48 00:02:40,880 --> 00:02:43,840 Speaker 1: were really interesting from how stuff Works dot Com. Neither 49 00:02:43,840 --> 00:02:46,079 Speaker 1: of us actually wrote this. Nope, Nope, I didn't write 50 00:02:46,080 --> 00:02:49,480 Speaker 1: this one. This one says people have tiny ridges of 51 00:02:49,480 --> 00:02:52,960 Speaker 1: skin on their fingers because this particular adaptation was extremely 52 00:02:53,040 --> 00:02:56,920 Speaker 1: advantageous to the ancestors of the human species. The pattern 53 00:02:56,960 --> 00:02:59,720 Speaker 1: of ridges and valleys on fingers make it easier for 54 00:02:59,760 --> 00:03:01,919 Speaker 1: the ends to grip things in the same way a 55 00:03:02,000 --> 00:03:05,880 Speaker 1: rubber tread pattern helps to attire grip the road. The 56 00:03:06,000 --> 00:03:10,440 Speaker 1: other function of fingerprints is a total coincidence. Like everything 57 00:03:10,440 --> 00:03:13,440 Speaker 1: in the human body, these ridges form through a combination 58 00:03:13,480 --> 00:03:16,800 Speaker 1: of genetic and environmental factors. The genetic code and DNA 59 00:03:16,919 --> 00:03:18,919 Speaker 1: gives general orders in the way skin should form in 60 00:03:18,919 --> 00:03:21,760 Speaker 1: the developing fetus, but the specific way it forms is 61 00:03:21,800 --> 00:03:24,680 Speaker 1: a result of random events. The exact position of the 62 00:03:24,680 --> 00:03:26,920 Speaker 1: fetus in the womb at a particular moment, and the 63 00:03:26,960 --> 00:03:30,680 Speaker 1: exact composition and density of surrounding amniotic fluid decides how 64 00:03:30,760 --> 00:03:34,120 Speaker 1: every individual ridge will form. This, by the way, is 65 00:03:34,160 --> 00:03:38,400 Speaker 1: how identical twins can have different fingerprints. Pretty cool because 66 00:03:38,440 --> 00:03:42,360 Speaker 1: you know, genetically they're identical, but their fingerprints are different, 67 00:03:42,400 --> 00:03:48,920 Speaker 1: still different. Interesting. So looking at this history, you might think, Okay, 68 00:03:49,000 --> 00:03:53,560 Speaker 1: I've heard about fingerprinting, particularly when it applies to law enforcement. 69 00:03:53,640 --> 00:03:55,640 Speaker 1: That's probably where a lot of people are familiar with it. 70 00:03:55,680 --> 00:03:58,680 Speaker 1: Besides the verification. Sure, sure, and uh and a couple 71 00:03:59,040 --> 00:04:02,720 Speaker 1: popular media pieces have talked recently about Um. I mean 72 00:04:02,880 --> 00:04:05,160 Speaker 1: you you get things like Ripper Street or Sleepy Hollow 73 00:04:05,520 --> 00:04:09,280 Speaker 1: having characters going like this new fingerprinting thing in the amazing, 74 00:04:09,560 --> 00:04:13,760 Speaker 1: amazing age of Victoria. Um, not not so much. Actually, 75 00:04:13,800 --> 00:04:16,159 Speaker 1: so fingerprints people, Well, first of all, people have been 76 00:04:16,200 --> 00:04:19,920 Speaker 1: aware of them for ages because we're curious folks, you know, 77 00:04:20,040 --> 00:04:24,320 Speaker 1: human beings. We we were very curious and narcissistic. We 78 00:04:24,440 --> 00:04:27,200 Speaker 1: like to learn stuff, and we like to look at ourselves. 79 00:04:27,760 --> 00:04:31,400 Speaker 1: And how do I know that this dates back? Are 80 00:04:32,040 --> 00:04:34,839 Speaker 1: ages and ages and ages ago because we have prehistoric 81 00:04:34,880 --> 00:04:39,159 Speaker 1: depictions found in Nova Scotia uh, and it depicted a 82 00:04:39,240 --> 00:04:41,960 Speaker 1: hand with ridge patterns on the skin. Now, that does 83 00:04:42,000 --> 00:04:46,720 Speaker 1: not mean that the ancient Nova Scotians were familiar with 84 00:04:46,760 --> 00:04:50,359 Speaker 1: the fact that all the fingerprints were unique and therefore 85 00:04:50,440 --> 00:04:53,440 Speaker 1: no two individuals had the same ones, but at least 86 00:04:53,440 --> 00:04:56,400 Speaker 1: show that, you know, yeah, they noticed them. Yeah, but 87 00:04:56,560 --> 00:05:00,360 Speaker 1: the ancient Babylonians may have actually used ping of prints 88 00:05:00,360 --> 00:05:04,400 Speaker 1: to differentiate people. Um, we've found fingerprints in clay to 89 00:05:04,400 --> 00:05:08,159 Speaker 1: to sign business transactions, and the ancient Chinese used inked 90 00:05:08,160 --> 00:05:12,000 Speaker 1: fingerprints for both business purposes and child identification. And in 91 00:05:12,080 --> 00:05:17,600 Speaker 1: the thirteen hundreds in Persia, official government documents often included fingerprints, 92 00:05:18,400 --> 00:05:22,000 Speaker 1: probably to indicate they were authorized and official. Now, according 93 00:05:22,040 --> 00:05:24,920 Speaker 1: to the US Marshall's Office, which has an entire web 94 00:05:24,920 --> 00:05:28,800 Speaker 1: page devoted to fingerprinting in the history of it, one 95 00:05:28,880 --> 00:05:32,120 Speaker 1: government official in Persia at that time made the observation 96 00:05:32,120 --> 00:05:34,800 Speaker 1: that no two fingerprints were alike, which obviously would be 97 00:05:34,880 --> 00:05:38,440 Speaker 1: very important if you're making a document official or authorized. 98 00:05:39,000 --> 00:05:42,560 Speaker 1: But it wasn't until the eighteen eighties that that amazing 99 00:05:42,600 --> 00:05:46,240 Speaker 1: Age of Victoria previously mentioned that we got some kind 100 00:05:46,240 --> 00:05:50,000 Speaker 1: of official classification system, right. It wasn't until the modern 101 00:05:50,080 --> 00:05:53,200 Speaker 1: era that we started seeing it in UH story. It 102 00:05:53,839 --> 00:05:55,520 Speaker 1: wasn't even in wide use at the beginning. It was 103 00:05:55,600 --> 00:05:59,520 Speaker 1: just kind of exploratory. Dr. Henry Faulds back in eighteen 104 00:05:59,600 --> 00:06:03,200 Speaker 1: eighty posed using fingerprints for identification as well as a 105 00:06:03,240 --> 00:06:07,359 Speaker 1: means of classifying them. So he forwarded these ideas to 106 00:06:07,520 --> 00:06:12,320 Speaker 1: a certain Charles Darwin, very important historical figure in his 107 00:06:12,360 --> 00:06:14,720 Speaker 1: own right. But Darwin at the time was towards the 108 00:06:14,800 --> 00:06:16,760 Speaker 1: end of his life and felt that he did not 109 00:06:16,960 --> 00:06:21,400 Speaker 1: have the necessary UH time and energy to devote to 110 00:06:21,440 --> 00:06:23,800 Speaker 1: this thought. It was really interesting, however, so he forwarded 111 00:06:23,839 --> 00:06:27,679 Speaker 1: the information along to a cousin of his named Francis Galton. 112 00:06:28,200 --> 00:06:31,920 Speaker 1: So false would write a scientific paper about his methods 113 00:06:32,040 --> 00:06:36,599 Speaker 1: and actually identified a fingerprint. Ascertain the identity of the 114 00:06:36,640 --> 00:06:42,600 Speaker 1: person who left that fingerprint on bottle of alcohol shouldn't 115 00:06:42,600 --> 00:06:45,760 Speaker 1: come as any surprise. I guess who brings it all together? 116 00:06:45,960 --> 00:06:50,640 Speaker 1: It does. Then in three Mark Twain would would use 117 00:06:50,720 --> 00:06:55,040 Speaker 1: this this new startling scientific information in a story. Yeah, 118 00:06:55,080 --> 00:06:58,000 Speaker 1: it was in Life on the Mississippi, and in that 119 00:06:58,480 --> 00:07:00,640 Speaker 1: one of the elements of that stories of order is 120 00:07:00,680 --> 00:07:03,880 Speaker 1: identified through the use of fingerprints. And he would revisit 121 00:07:03,920 --> 00:07:07,800 Speaker 1: the idea in a book called Putting Head Wilson. Putting 122 00:07:07,839 --> 00:07:11,320 Speaker 1: Head Wilson. Yea, so it's interesting. Now this was before 123 00:07:11,880 --> 00:07:15,600 Speaker 1: anyone was actually using uh, fingerprints in any kind of 124 00:07:15,600 --> 00:07:18,640 Speaker 1: criminal investigations on an official basis. It had not been 125 00:07:19,680 --> 00:07:22,720 Speaker 1: science fiction, really was it kind of was so so 126 00:07:22,800 --> 00:07:25,680 Speaker 1: that was exciting. But but soon in eighteen eighty eight, 127 00:07:25,960 --> 00:07:30,080 Speaker 1: Sir Francis Galton, remember the cousin to Charles Darwin, who 128 00:07:30,120 --> 00:07:33,600 Speaker 1: had received information about this about ten years previous, um 129 00:07:33,680 --> 00:07:36,120 Speaker 1: began his own study of fingerprints. Yep. He wrote a 130 00:07:36,120 --> 00:07:38,800 Speaker 1: book in eighteen ninety two and put forth a formal 131 00:07:38,920 --> 00:07:44,200 Speaker 1: classification system and identify the tiny characteristics used to differentiate fingerprints, 132 00:07:44,200 --> 00:07:48,320 Speaker 1: which now we call Galton's details. He also observed that 133 00:07:48,360 --> 00:07:51,200 Speaker 1: fingerprints don't change over the lifetime of a person, so 134 00:07:51,240 --> 00:07:52,840 Speaker 1: the ones you have when you're a kid are the 135 00:07:52,880 --> 00:07:56,840 Speaker 1: same ones you have when you're old. Originally wanted to 136 00:07:57,000 --> 00:08:00,640 Speaker 1: kind of link fingerprints to certain types of traits like 137 00:08:00,760 --> 00:08:05,480 Speaker 1: intelligence or heredity, which is so racist. Yeah, kind of essentially, 138 00:08:06,040 --> 00:08:08,760 Speaker 1: I think this was almost in an attempt. And I 139 00:08:08,760 --> 00:08:10,920 Speaker 1: don't know enough about Galton to say this for sure, 140 00:08:11,000 --> 00:08:15,880 Speaker 1: but it seems like an attempt to justify certain society's 141 00:08:16,320 --> 00:08:19,600 Speaker 1: beliefs in certain people, that kind of thing. Yeah, it 142 00:08:19,680 --> 00:08:23,200 Speaker 1: wasn't necessarily so racist, but um, but it's kind of 143 00:08:23,280 --> 00:08:25,480 Speaker 1: it kind of runs in that direction. Yeah, because I mean, 144 00:08:25,520 --> 00:08:28,320 Speaker 1: the idea that you could identify a person's intelligence based 145 00:08:28,320 --> 00:08:31,800 Speaker 1: on their fingerprints already seems a little sketchy, And in fact, 146 00:08:31,960 --> 00:08:35,560 Speaker 1: he did determined that there was no connection. There was 147 00:08:35,600 --> 00:08:38,360 Speaker 1: no there were no identifier marks in a person's fingerprints 148 00:08:38,360 --> 00:08:40,640 Speaker 1: that would give you any clue as to that person's 149 00:08:40,679 --> 00:08:45,600 Speaker 1: intelligence or genetic background. However, he did figure out that 150 00:08:45,640 --> 00:08:48,480 Speaker 1: there were a lot of differences and that people didn't 151 00:08:48,600 --> 00:08:52,440 Speaker 1: weren't likely to have the same fingerprints. Yeah, unlikely, in 152 00:08:52,480 --> 00:08:56,360 Speaker 1: the order of one in sixty four billion, that's pretty unlikely. 153 00:08:57,400 --> 00:09:01,320 Speaker 1: So in eight we get the first use of fingerprints 154 00:09:01,440 --> 00:09:06,640 Speaker 1: in a criminal investigation on an official level. Uh one vs. 155 00:09:06,800 --> 00:09:10,640 Speaker 1: Tech And I'm sure I butchered his last name. Who 156 00:09:10,679 --> 00:09:12,920 Speaker 1: was a police officer in Argentina used them in a 157 00:09:13,000 --> 00:09:16,719 Speaker 1: murder investigation. It was actually a really tragic case, but 158 00:09:17,320 --> 00:09:21,839 Speaker 1: the discovery meant that he was able to solve this 159 00:09:22,000 --> 00:09:25,400 Speaker 1: mystery and prove that the person that was believed to 160 00:09:25,440 --> 00:09:28,600 Speaker 1: have been the murderer was in fact not the killer. 161 00:09:29,120 --> 00:09:32,160 Speaker 1: So Uh, not only was it the first use, but 162 00:09:32,200 --> 00:09:36,040 Speaker 1: it was a pretty dramatic example of it. Now, between 163 00:09:36,360 --> 00:09:39,240 Speaker 1: we're gonna skip to nineteen eighteen, but between the late 164 00:09:39,320 --> 00:09:43,000 Speaker 1: nineteenth century and the early twentieth century he started to 165 00:09:43,000 --> 00:09:48,160 Speaker 1: see fingerprints get adopted into various legal organizations all around 166 00:09:48,160 --> 00:09:50,440 Speaker 1: the world. The United Kingdom and the United States were 167 00:09:50,520 --> 00:09:52,559 Speaker 1: leading the way, but it was all over the place. 168 00:09:53,200 --> 00:09:56,840 Speaker 1: So by nineteen eighteen you have Edmund Lekard who says 169 00:09:56,920 --> 00:10:00,280 Speaker 1: that you only need twelve points of similarity between an 170 00:10:00,320 --> 00:10:03,560 Speaker 1: individual's fingerprint and a target fingerprint to serve as a 171 00:10:03,600 --> 00:10:08,880 Speaker 1: positive identification. Now you may have heard about those twelve 172 00:10:08,920 --> 00:10:12,360 Speaker 1: points of singularity, that this is somehow like a legal thing, 173 00:10:12,400 --> 00:10:14,320 Speaker 1: that if you were able to meet those twelve points 174 00:10:14,360 --> 00:10:16,679 Speaker 1: of singularity, you have the legal basis to say this 175 00:10:16,720 --> 00:10:23,600 Speaker 1: person did this. Uh, not necessarily a legal definition at all. 176 00:10:23,640 --> 00:10:28,320 Speaker 1: In fact, UH, countries have very very different ways of 177 00:10:28,360 --> 00:10:30,839 Speaker 1: saying whether or not a fingerprint is a valid match 178 00:10:30,920 --> 00:10:34,600 Speaker 1: for another one, and some, like the United States, do 179 00:10:34,720 --> 00:10:38,560 Speaker 1: not have a minimum number of resemblances that need to 180 00:10:38,600 --> 00:10:40,920 Speaker 1: be there in order for you to call it a match. Now, 181 00:10:41,000 --> 00:10:45,719 Speaker 1: usually law enforcement agencies rely on experts who give their 182 00:10:45,760 --> 00:10:48,520 Speaker 1: expert opinion and therefore putting their own reputation on the 183 00:10:48,559 --> 00:10:51,160 Speaker 1: line as to whether or not something matches. And of 184 00:10:51,160 --> 00:10:56,280 Speaker 1: course now these days we rely very heavily on digital information, 185 00:10:56,360 --> 00:11:00,640 Speaker 1: which with very very complex and intelligent i'll rhythms, that 186 00:11:00,720 --> 00:11:03,800 Speaker 1: will that will do some really interesting like work. Yeah, 187 00:11:03,840 --> 00:11:06,960 Speaker 1: and so with that, you know, the the level of 188 00:11:07,320 --> 00:11:11,280 Speaker 1: a confidence grows quite a bit. So, uh, just so 189 00:11:11,360 --> 00:11:13,920 Speaker 1: you know that twelve points of similarity not necessarily a 190 00:11:14,040 --> 00:11:18,840 Speaker 1: legal standard. Nineteen four, that's when Congress enabled had an 191 00:11:18,840 --> 00:11:21,720 Speaker 1: act that established the new division for the Federal Bureau 192 00:11:21,800 --> 00:11:25,160 Speaker 1: of Investigation also known as the f b I. UH 193 00:11:25,160 --> 00:11:28,920 Speaker 1: and this division was called the Identification Division. I bet 194 00:11:28,960 --> 00:11:31,520 Speaker 1: you can guess what it did. So it became kind 195 00:11:31,520 --> 00:11:35,040 Speaker 1: of a centralized fingerprint file for the entire country. So 196 00:11:35,559 --> 00:11:39,760 Speaker 1: not it wasn't necessarily uh standard procedure for every law 197 00:11:39,840 --> 00:11:43,400 Speaker 1: enforcement agency out there to send a copy to the FBI. 198 00:11:43,960 --> 00:11:47,520 Speaker 1: But that's kind of what started to happen, so that, uh, 199 00:11:47,559 --> 00:11:51,319 Speaker 1: there could be this sort of cooperation between different departments 200 00:11:51,360 --> 00:11:54,800 Speaker 1: which often wouldn't have any communication with each other. Sure sure, 201 00:11:54,880 --> 00:11:57,480 Speaker 1: by ninety six, the FBI would have processed more than 202 00:11:57,520 --> 00:12:01,920 Speaker 1: a hundred million fingerprint cards. Yeah, not just processed, but 203 00:12:02,040 --> 00:12:05,000 Speaker 1: they did that by hand, right, right, and it would 204 00:12:05,000 --> 00:12:12,040 Speaker 1: be two million by two million physical cards with fingerprints 205 00:12:12,040 --> 00:12:14,360 Speaker 1: on them. I mean, just imagine how many, Like how 206 00:12:14,440 --> 00:12:16,520 Speaker 1: how much storage you need just for me? It's two 207 00:12:17,080 --> 00:12:21,400 Speaker 1: I can't even I can't even imagine it. But by 208 00:12:21,559 --> 00:12:26,839 Speaker 1: the FBI introduced the Integrated Automated Fingerprint Identification System or 209 00:12:27,120 --> 00:12:31,240 Speaker 1: if SO. It's the largest fingerprint database in the world, 210 00:12:31,360 --> 00:12:35,240 Speaker 1: and its computer automated. It takes about twenty seven minutes 211 00:12:35,320 --> 00:12:38,440 Speaker 1: for the system to comb through every single file in 212 00:12:38,480 --> 00:12:41,839 Speaker 1: its database to find out if there is a potential match. 213 00:12:42,240 --> 00:12:45,800 Speaker 1: During a criminal investigation, it's different. If it's a civil case, 214 00:12:46,120 --> 00:12:49,600 Speaker 1: it's actually like more than an hour. But for criminal investigation, 215 00:12:50,160 --> 00:12:52,480 Speaker 1: for all the criminal files that are storing this database, 216 00:12:52,480 --> 00:12:55,040 Speaker 1: twenty seven minutes from the time you actually input the 217 00:12:55,800 --> 00:12:59,600 Speaker 1: suspects fingerprint. I imagine that's a lot faster than whatever 218 00:12:59,679 --> 00:13:02,800 Speaker 1: in her and they sent down to the basement, Well yeah, 219 00:13:02,800 --> 00:13:04,800 Speaker 1: because I mean you would have to narrow down the 220 00:13:04,840 --> 00:13:09,640 Speaker 1: person quite a bit before you could ever start coming right, 221 00:13:10,240 --> 00:13:13,480 Speaker 1: this is a man, So cut out all the women 222 00:13:13,600 --> 00:13:16,320 Speaker 1: and then go like there are seventy million of them 223 00:13:16,320 --> 00:13:18,800 Speaker 1: down there. Uh yeah, that would be that would be 224 00:13:18,800 --> 00:13:22,640 Speaker 1: a daunting task. So in the digital age, we can 225 00:13:22,679 --> 00:13:26,240 Speaker 1: actually analyze this stuff way better than we ever could, 226 00:13:26,280 --> 00:13:28,040 Speaker 1: Like you don't have to use the naked eye anymore 227 00:13:28,080 --> 00:13:30,480 Speaker 1: and try and find those little ridges and stuff. You 228 00:13:30,480 --> 00:13:34,800 Speaker 1: can actually rely very heavily upon computer systems. And once 229 00:13:34,840 --> 00:13:39,479 Speaker 1: we started getting those computer systems in place, they pretty 230 00:13:39,520 --> 00:13:44,000 Speaker 1: soon thereafter became um commercially available. Yeah. Yeah, we had 231 00:13:44,040 --> 00:13:47,200 Speaker 1: some fingerprint verification systems that have been around for a 232 00:13:47,240 --> 00:13:50,120 Speaker 1: few decades now. On the consumer level, they've only been 233 00:13:50,160 --> 00:13:53,079 Speaker 1: available fairly recently. And you might be thinking, oh, yeah, yeah, 234 00:13:53,120 --> 00:13:56,079 Speaker 1: the iPhone five s because it has that that fingerprint 235 00:13:56,120 --> 00:13:58,640 Speaker 1: scanner where you can use that to log into your phone. 236 00:13:58,920 --> 00:14:03,040 Speaker 1: That's the first smart ohne to ever use that, right, Nope, no, no, 237 00:14:03,160 --> 00:14:05,880 Speaker 1: there's actually a mobile device. The first mobile device that 238 00:14:05,920 --> 00:14:08,520 Speaker 1: I found was a one that dated from two thousand three, 239 00:14:08,559 --> 00:14:11,960 Speaker 1: the H and Boy. The name of this is phenomenal. 240 00:14:12,120 --> 00:14:15,720 Speaker 1: HP names their products in such catchy ways. It's the 241 00:14:15,960 --> 00:14:23,320 Speaker 1: HP I pack, I p a Q P PC pocket PC. 242 00:14:24,000 --> 00:14:26,760 Speaker 1: Oh yeah, it just rolls off the tongue. Yeah, it 243 00:14:26,760 --> 00:14:28,680 Speaker 1: really is. I mean, with a name like that, how 244 00:14:28,680 --> 00:14:33,160 Speaker 1: could you resist I phone my phone. So at any rate, 245 00:14:33,200 --> 00:14:36,960 Speaker 1: this was the first mobile device to incorporate finger scanning technology. 246 00:14:37,160 --> 00:14:38,480 Speaker 1: But it was also sort of the edge of a 247 00:14:38,560 --> 00:14:42,280 Speaker 1: boom for the technology. UM it was being extended for 248 00:14:42,280 --> 00:14:44,200 Speaker 1: for wide consumer use at the time. I mean, you know, 249 00:14:44,280 --> 00:14:46,360 Speaker 1: keyboards and mice had UM, laptops had him. You can 250 00:14:46,400 --> 00:14:50,160 Speaker 1: buy a USB scanner for multipurpose use and encrypt everything 251 00:14:50,240 --> 00:14:54,360 Speaker 1: with your fingerprint, right, you could end up creating, for example, 252 00:14:54,440 --> 00:14:56,840 Speaker 1: with a copier, you could end up telling people, all right, 253 00:14:57,280 --> 00:15:00,600 Speaker 1: this group of folks are authorized to make copy. Anyone 254 00:15:00,600 --> 00:15:03,000 Speaker 1: who's not on this list cannot make copies. And you 255 00:15:03,000 --> 00:15:04,760 Speaker 1: would walk up to make copies. And if you weren't 256 00:15:04,800 --> 00:15:08,680 Speaker 1: on that list, then I guess no fun Christmas party 257 00:15:08,720 --> 00:15:11,720 Speaker 1: Shenanigans for you. But but but but also opening this up 258 00:15:11,720 --> 00:15:14,560 Speaker 1: to the consumer market meant that a lot of people 259 00:15:14,680 --> 00:15:18,200 Speaker 1: started finding the flaws in the technology. Oh yes. In 260 00:15:18,200 --> 00:15:22,120 Speaker 1: December five, clarks And University researchers announced that they could 261 00:15:22,120 --> 00:15:28,440 Speaker 1: fool of the world's fingerprint scanners using an incredibly sophisticated, 262 00:15:29,000 --> 00:15:34,640 Speaker 1: expensive substance. Yeah, they could just go out to toy 263 00:15:34,680 --> 00:15:38,200 Speaker 1: store and buy some Plato and essentially make a copy 264 00:15:38,360 --> 00:15:41,280 Speaker 1: of a fingerprint and put it on any optical scanner, 265 00:15:41,280 --> 00:15:43,200 Speaker 1: and we'll talk about the optical scanners in a little bit. 266 00:15:43,640 --> 00:15:48,080 Speaker 1: And it worked really well. So that gave people some 267 00:15:48,120 --> 00:15:50,280 Speaker 1: pause and thought, maybe we should come up with something 268 00:15:50,320 --> 00:15:55,240 Speaker 1: besides optical scanners too, because fingerprint identification is a great idea. 269 00:15:55,720 --> 00:15:57,440 Speaker 1: But if it's if it's that easy to fool, we 270 00:15:57,440 --> 00:15:59,680 Speaker 1: have to find a different way of measuring it. So 271 00:16:00,040 --> 00:16:03,840 Speaker 1: then there was a totally different expose on September twenty one, 272 00:16:04,000 --> 00:16:07,840 Speaker 1: two thousand and six, when our beloved MythBusters decided to 273 00:16:08,160 --> 00:16:10,920 Speaker 1: do a fingerprint scanner scam of their own. They decided 274 00:16:10,920 --> 00:16:12,560 Speaker 1: to see if they could fool one. And this one 275 00:16:12,640 --> 00:16:14,760 Speaker 1: was a little different. It wasn't just an optical scanner. 276 00:16:14,800 --> 00:16:18,360 Speaker 1: It was supposed to also detect sweat from pores in 277 00:16:18,400 --> 00:16:21,200 Speaker 1: the fingertips. Okay, so so you can't just use a 278 00:16:21,280 --> 00:16:24,800 Speaker 1: lump of plato or photograph. You have to have something 279 00:16:24,840 --> 00:16:28,200 Speaker 1: that sweats has to have to otherwise it's never gonna work. 280 00:16:28,400 --> 00:16:31,680 Speaker 1: So what did they do. They made a latext copy 281 00:16:31,880 --> 00:16:35,040 Speaker 1: of a fingerprint and then they did a very sophisticated 282 00:16:35,160 --> 00:16:40,560 Speaker 1: thing in order to simulate sweat. Yeah, and it worked. 283 00:16:41,240 --> 00:16:45,680 Speaker 1: So if you can't play dough it, lick it, I 284 00:16:45,720 --> 00:16:48,440 Speaker 1: guess is the moral of that story. I think I 285 00:16:48,720 --> 00:16:50,880 Speaker 1: think that that's really what we all should learn from 286 00:16:50,880 --> 00:16:54,320 Speaker 1: this today. Probably, So anyway, they're still seeing this kind 287 00:16:54,360 --> 00:16:57,120 Speaker 1: of technology being rolled out, but it's not just an 288 00:16:57,160 --> 00:17:00,760 Speaker 1: optical scanners, and even optical skins have gotten a lot 289 00:17:00,800 --> 00:17:03,000 Speaker 1: better than they were back in two thousand and five. 290 00:17:03,520 --> 00:17:05,920 Speaker 1: So we're gonna we're gonna cover exactly how they work. 291 00:17:05,960 --> 00:17:08,639 Speaker 1: But before we do that, let's take a quick break 292 00:17:08,880 --> 00:17:11,959 Speaker 1: to thank our sponsor. Alright, I had just talked about 293 00:17:12,119 --> 00:17:14,439 Speaker 1: optical scanners, and those are one of the ones that 294 00:17:14,480 --> 00:17:18,080 Speaker 1: are the easiest to explain and uh and fairly common 295 00:17:18,119 --> 00:17:21,919 Speaker 1: even today. In fact, not my work computer, but my 296 00:17:22,000 --> 00:17:25,040 Speaker 1: home computer has in my home laptop has a little 297 00:17:25,080 --> 00:17:26,960 Speaker 1: optical scanner. So if I want to log in, I 298 00:17:27,000 --> 00:17:30,440 Speaker 1: can just swipe my finger across it. So optical scanners 299 00:17:30,640 --> 00:17:34,720 Speaker 1: use something that is found in digital cameras and camcorders 300 00:17:34,800 --> 00:17:38,760 Speaker 1: called a charge coupled device or c c D. Now, 301 00:17:38,840 --> 00:17:41,960 Speaker 1: essentially that's a light sensor. It's got an array of 302 00:17:42,000 --> 00:17:46,320 Speaker 1: things called photo sites. Now that's light sensitive diodes, and 303 00:17:46,400 --> 00:17:49,400 Speaker 1: it's works pretty much the way you would imagine. So photons, 304 00:17:49,440 --> 00:17:52,840 Speaker 1: those little particles of light when they make a collision 305 00:17:52,880 --> 00:17:56,479 Speaker 1: with these photo sites, it generates a little electrical signal 306 00:17:56,920 --> 00:17:59,840 Speaker 1: and those can then be compiled and converted into a 307 00:18:00,000 --> 00:18:02,800 Speaker 1: digital image. And it's essentially the same process that digital 308 00:18:02,840 --> 00:18:06,320 Speaker 1: cameras used to take pictures. And here's the thing though, 309 00:18:06,680 --> 00:18:09,919 Speaker 1: So if I put my finger on an optical scanner, 310 00:18:09,960 --> 00:18:13,600 Speaker 1: I'm actually blocking light. And unless you happen to be 311 00:18:13,880 --> 00:18:18,840 Speaker 1: et the extraterrestrial, your finger probably isn't emitting light. So 312 00:18:18,880 --> 00:18:21,600 Speaker 1: how the heck is it getting this picture? Well, it 313 00:18:21,720 --> 00:18:24,359 Speaker 1: uses a flash, yeah, except in this case of flash 314 00:18:24,440 --> 00:18:26,880 Speaker 1: is probably like a single LED or for some scanners 315 00:18:26,880 --> 00:18:30,520 Speaker 1: maybe an array of LEDs light emitting diodes in other words, 316 00:18:31,320 --> 00:18:34,399 Speaker 1: and so that provides the light that is necessary to 317 00:18:34,480 --> 00:18:38,120 Speaker 1: take this image. And uh, then the c c D 318 00:18:38,480 --> 00:18:41,679 Speaker 1: creates an image of your fingertip. However, it's a little funky. 319 00:18:42,080 --> 00:18:45,520 Speaker 1: It's inverted, right. The dark areas are going to represent 320 00:18:45,600 --> 00:18:48,239 Speaker 1: the ridges that the raised portions of your fingertip, and 321 00:18:48,280 --> 00:18:50,840 Speaker 1: the light areas are going to represent the values. Yeah, 322 00:18:50,840 --> 00:18:52,800 Speaker 1: so it's kind of like looking at a negative, a 323 00:18:52,800 --> 00:18:56,680 Speaker 1: photo negative. Uh. And it makes sense because stuff that's 324 00:18:56,720 --> 00:18:59,159 Speaker 1: reflecting more light is going to create a lot a 325 00:18:59,200 --> 00:19:03,600 Speaker 1: bigger electra called signal bus creating that. You know, the 326 00:19:03,920 --> 00:19:06,159 Speaker 1: charge couple device is going to make that into a 327 00:19:06,240 --> 00:19:09,240 Speaker 1: darker portion the valleys. The light is not as much 328 00:19:09,280 --> 00:19:11,040 Speaker 1: of it's going to reflect back, so you get a 329 00:19:11,080 --> 00:19:14,200 Speaker 1: weaker electrical signal. That's why it gets lighter. Uh So, 330 00:19:15,000 --> 00:19:19,280 Speaker 1: if you were to scan your finger and UM and 331 00:19:19,320 --> 00:19:21,520 Speaker 1: it tells you that it's a good scan, because most 332 00:19:21,560 --> 00:19:24,679 Speaker 1: of these devices also have some sort of fail safe 333 00:19:24,680 --> 00:19:27,640 Speaker 1: in them that will alert you if there wasn't enough 334 00:19:28,440 --> 00:19:32,119 Speaker 1: enough differentiation between the dark and light parts, right, that 335 00:19:32,200 --> 00:19:34,760 Speaker 1: the same way that you're that your camera UM sometimes 336 00:19:34,840 --> 00:19:38,000 Speaker 1: will take a picture that's that's overdeveloped or underdeveloped, the 337 00:19:38,040 --> 00:19:40,320 Speaker 1: same thing can happen here. Yeah, So if you wanted to, 338 00:19:40,840 --> 00:19:43,359 Speaker 1: like if you most of the software has let error 339 00:19:43,359 --> 00:19:46,119 Speaker 1: function built into it, so it can tell and it 340 00:19:46,119 --> 00:19:47,720 Speaker 1: will ask you to scan again, right, So then you 341 00:19:47,720 --> 00:19:49,840 Speaker 1: would scan again. So if it's the first time you're 342 00:19:49,920 --> 00:19:52,480 Speaker 1: using it, then you would also end up creating a 343 00:19:52,520 --> 00:19:54,639 Speaker 1: profile in some way. It might not be you, it 344 00:19:54,720 --> 00:19:58,160 Speaker 1: might be an administrator, but something that links the fingerprint 345 00:19:58,240 --> 00:20:01,000 Speaker 1: to who you are and what says you are supposed 346 00:20:01,040 --> 00:20:05,120 Speaker 1: to have will happen then on subsequent uses, what will 347 00:20:05,160 --> 00:20:07,600 Speaker 1: happen is that when you scan your finger it will 348 00:20:07,640 --> 00:20:11,879 Speaker 1: go through its database of of identities that have fingerprints 349 00:20:11,920 --> 00:20:15,080 Speaker 1: attached to them look for you. If you're there, then 350 00:20:15,119 --> 00:20:18,440 Speaker 1: it will authorize you for whatever use you're allowed. So, 351 00:20:18,760 --> 00:20:22,440 Speaker 1: for instance, on my home computer, I've given myself very 352 00:20:22,480 --> 00:20:25,440 Speaker 1: strict restrictions because I am not to be trusted. Uh, 353 00:20:25,480 --> 00:20:27,919 Speaker 1: And so all I'm able to do is play a 354 00:20:28,040 --> 00:20:33,080 Speaker 1: pirated copy of Flappy Bird. That's not true, I don't 355 00:20:33,080 --> 00:20:35,879 Speaker 1: have Flappy Bird. But at any rate, you know that's 356 00:20:36,080 --> 00:20:40,000 Speaker 1: the basic You know, that's the basic procedure, right, and 357 00:20:40,000 --> 00:20:42,840 Speaker 1: and so if your finger print is not found in 358 00:20:42,880 --> 00:20:45,679 Speaker 1: that database, you get an error. So either you have 359 00:20:45,760 --> 00:20:48,119 Speaker 1: to swipe it again or or scan it again if 360 00:20:48,119 --> 00:20:50,639 Speaker 1: it's not swipe all depends on what kind of scanner 361 00:20:50,680 --> 00:20:54,560 Speaker 1: you're at, or you end up saying the jig is up, 362 00:20:54,920 --> 00:20:58,040 Speaker 1: you got me, I don't really belong here, and then 363 00:20:58,080 --> 00:21:01,960 Speaker 1: you run away. Um. So that that's the basic thing, 364 00:21:02,240 --> 00:21:05,960 Speaker 1: and what they're looking for. It's not the entire pattern 365 00:21:06,359 --> 00:21:10,080 Speaker 1: of your fingertip. It's looking for for a specific minutia 366 00:21:10,320 --> 00:21:13,080 Speaker 1: about it. Uh. Certain types of patterns and it and 367 00:21:13,119 --> 00:21:15,480 Speaker 1: it depends on the software that the scanner is using. 368 00:21:15,680 --> 00:21:19,320 Speaker 1: Um it might be the places where the ridges converge 369 00:21:19,600 --> 00:21:22,679 Speaker 1: um or or split apart at the end, or um 370 00:21:22,720 --> 00:21:24,399 Speaker 1: any any other kind of detail. It's going to be 371 00:21:24,480 --> 00:21:27,080 Speaker 1: unique to you. Right. So in other words, all has 372 00:21:27,119 --> 00:21:29,440 Speaker 1: to do is say, hey, there are these three points 373 00:21:29,440 --> 00:21:33,399 Speaker 1: on this fingertip, uh that are unique that that's all 374 00:21:33,440 --> 00:21:36,560 Speaker 1: I'm concerned about. And if the fingerprint that scan has 375 00:21:36,600 --> 00:21:38,760 Speaker 1: those three points, I know it's this person and they 376 00:21:38,760 --> 00:21:41,680 Speaker 1: can be let through by constraining on the minutia, then 377 00:21:41,680 --> 00:21:43,399 Speaker 1: you really cut down on all the rest of the 378 00:21:43,480 --> 00:21:46,800 Speaker 1: data that's necessary to have a verification. Right, you can 379 00:21:46,840 --> 00:21:49,880 Speaker 1: kind of throw everything else out and concentrate on that 380 00:21:49,960 --> 00:21:52,520 Speaker 1: and right. Uh And and they're pretty good, like we said, 381 00:21:52,560 --> 00:21:55,000 Speaker 1: though they can sometimes be fooled by a really high 382 00:21:55,080 --> 00:21:58,600 Speaker 1: quality picture of a fingerprint. Right. So what if you 383 00:21:58,800 --> 00:22:02,399 Speaker 1: instead of looking at a picture, you were to measure 384 00:22:02,440 --> 00:22:08,000 Speaker 1: the fingerprint in some other way, such as capacitance right, 385 00:22:08,160 --> 00:22:11,800 Speaker 1: because capacitense touch screens are totally a thing. Yeah, so 386 00:22:12,480 --> 00:22:15,840 Speaker 1: it's very similar to what a regular capacityans touch screen is. 387 00:22:15,840 --> 00:22:17,560 Speaker 1: So you know, there are different ways of doing touch 388 00:22:17,600 --> 00:22:19,680 Speaker 1: screens as well. There's somewhere you have to use pressure. 389 00:22:20,359 --> 00:22:22,080 Speaker 1: With the benefit of that is you could be wearing 390 00:22:22,080 --> 00:22:26,800 Speaker 1: gloves and still work a pressure version. The downside is 391 00:22:26,840 --> 00:22:30,840 Speaker 1: that when people are expected to apply pressure to delicate material, 392 00:22:30,960 --> 00:22:34,639 Speaker 1: they sometimes destroy it completely, right, or it'll at least 393 00:22:35,440 --> 00:22:40,919 Speaker 1: decrease the lifespan of that object by quite a bit. Capacitance, however, 394 00:22:41,400 --> 00:22:46,439 Speaker 1: uses weak electric fields, So when you make contact with 395 00:22:46,600 --> 00:22:49,600 Speaker 1: a screen, a touch screen, that's using capacitance. Uh see, 396 00:22:49,600 --> 00:22:52,840 Speaker 1: you're a conductor. I don't mean that you conduct trains, 397 00:22:53,000 --> 00:22:55,399 Speaker 1: Nor do I mean that you Maybe you do. Maybe 398 00:22:55,400 --> 00:22:58,680 Speaker 1: you do, Maybe you do. Maybe you conduct orchestras. Maybe 399 00:22:58,680 --> 00:23:01,199 Speaker 1: you conduct orchestras on a train. I don't know, but 400 00:23:01,240 --> 00:23:02,879 Speaker 1: I know what I'm talking about. If you do, call us, 401 00:23:02,880 --> 00:23:05,160 Speaker 1: because that sounds fascint, would be kind of cool. Actually, 402 00:23:05,200 --> 00:23:09,080 Speaker 1: But now I'm talking about electrical conductivity. So we can 403 00:23:09,160 --> 00:23:11,800 Speaker 1: conduct electricity. It's not great for us to have a 404 00:23:11,840 --> 00:23:14,600 Speaker 1: lot of it, but tiny amounts don't hurt us. Sure, 405 00:23:14,720 --> 00:23:17,000 Speaker 1: And as it turns out, the ridges and valleys on 406 00:23:17,080 --> 00:23:21,359 Speaker 1: your fingers conducts slightly different amounts of electricity. This blows 407 00:23:21,440 --> 00:23:24,080 Speaker 1: my mind. I mean to think that the raised parts 408 00:23:24,119 --> 00:23:26,920 Speaker 1: of your fingerprint, and the valleys of your fingerprint are 409 00:23:27,160 --> 00:23:32,639 Speaker 1: distinct enough to create a measurable difference in capacitance. You know, 410 00:23:32,720 --> 00:23:35,080 Speaker 1: It's something I never would have imagined. And it's really 411 00:23:35,119 --> 00:23:36,840 Speaker 1: a sensor issue here. I mean the fact that we 412 00:23:36,880 --> 00:23:40,560 Speaker 1: can create these these cells, these capacitor plate cells that 413 00:23:40,680 --> 00:23:44,840 Speaker 1: are sensitive enough to tell that yeah technology. So what's 414 00:23:44,840 --> 00:23:46,800 Speaker 1: happening is when you put your finger down on one 415 00:23:46,840 --> 00:23:51,919 Speaker 1: of these capacity scanners, you are actually you're acting as 416 00:23:51,960 --> 00:23:54,800 Speaker 1: a capacitance plate, right, your fingertip is acting is one, 417 00:23:54,840 --> 00:23:57,680 Speaker 1: and you already have other ones inside the scanner itself, 418 00:23:58,320 --> 00:24:01,720 Speaker 1: and it will end up creating voltages and there will 419 00:24:01,760 --> 00:24:05,199 Speaker 1: be difference differences in those voltages. The differences between the 420 00:24:05,280 --> 00:24:07,919 Speaker 1: ridges and the valleys based on how far away they 421 00:24:07,960 --> 00:24:10,720 Speaker 1: are from the from the cells. Yeah, so if it's 422 00:24:10,720 --> 00:24:13,119 Speaker 1: a valley, it's going to be lower capacitance because the 423 00:24:13,240 --> 00:24:17,520 Speaker 1: distance is greater. Capacitance is very dependent upon distance. So 424 00:24:17,640 --> 00:24:20,239 Speaker 1: if you move to capacitance plates far enough apart, they 425 00:24:20,240 --> 00:24:23,840 Speaker 1: will not be they will not work together. So it's 426 00:24:23,840 --> 00:24:27,600 Speaker 1: amazing that the valleys, just by being that much further 427 00:24:27,680 --> 00:24:30,639 Speaker 1: away will create a different voltage than the ridges. And 428 00:24:30,680 --> 00:24:33,560 Speaker 1: then having a whole whole set of these cells set 429 00:24:33,600 --> 00:24:37,000 Speaker 1: up next to one another. UM it allows the scanner 430 00:24:37,040 --> 00:24:39,840 Speaker 1: to to sort of make a digital picture of your fingerprint, 431 00:24:39,920 --> 00:24:43,280 Speaker 1: but just using electricity rather than light. The data from 432 00:24:43,280 --> 00:24:46,560 Speaker 1: each one is again compiled and then converted into an 433 00:24:46,560 --> 00:24:49,120 Speaker 1: image of sorts. Yeah. Yeah, you can think of it 434 00:24:49,200 --> 00:24:52,560 Speaker 1: as like an image made with electricity. And this is 435 00:24:52,560 --> 00:24:55,760 Speaker 1: the sort of scanner that the iPhone five S uses, 436 00:24:55,880 --> 00:24:58,600 Speaker 1: so it's not an optical scanner. One of the big 437 00:24:58,640 --> 00:25:01,240 Speaker 1: benefits of this technolog g is that it's easier to 438 00:25:01,280 --> 00:25:04,520 Speaker 1: make it really compact and manaturized, which is why you 439 00:25:04,520 --> 00:25:09,560 Speaker 1: could find it in things like handheld electronics, right sure. Um. However, 440 00:25:09,720 --> 00:25:13,080 Speaker 1: this technology can also be fooled UM sometimes by by 441 00:25:13,200 --> 00:25:16,200 Speaker 1: mold of a finger UM or if someone has gone 442 00:25:16,240 --> 00:25:18,720 Speaker 1: and calibrated the scanner to look for things like like 443 00:25:18,840 --> 00:25:22,439 Speaker 1: heat or a pulse. UM. You can you can use 444 00:25:22,520 --> 00:25:25,040 Speaker 1: one of those movie tricks, like a gelatine or silicone 445 00:25:25,040 --> 00:25:30,200 Speaker 1: mold of a finger UM pasted onto a different different finger. Yeah. 446 00:25:30,440 --> 00:25:35,240 Speaker 1: My favorite version of getting past a UM capacitance or 447 00:25:35,280 --> 00:25:39,479 Speaker 1: an optical scanner is to use the finger that's been 448 00:25:39,520 --> 00:25:43,920 Speaker 1: removed from the person who had the authorization. Yeah, yeah, sniping, 449 00:25:43,960 --> 00:25:46,919 Speaker 1: that's that's that's your favorite. That's my favorite out in 450 00:25:46,920 --> 00:25:50,080 Speaker 1: the field. That's what you like. We had this discussion 451 00:25:50,119 --> 00:25:52,360 Speaker 1: about the born identity before we came into the podcast. 452 00:25:52,440 --> 00:25:55,280 Speaker 1: This is totally true. Uh, I don't like to discuss 453 00:25:55,359 --> 00:25:59,600 Speaker 1: my actual field operative kind of mentality and strategies, but yes, 454 00:25:59,680 --> 00:26:02,920 Speaker 1: I do love doing that. So thermal scanners, well, this 455 00:26:02,920 --> 00:26:05,400 Speaker 1: one's a little different because it's a it's a heat scanner, 456 00:26:05,760 --> 00:26:08,800 Speaker 1: and again it's one of those ideas where it measures 457 00:26:08,800 --> 00:26:13,600 Speaker 1: the differences in heat between ridges and valleys. Once again, 458 00:26:13,960 --> 00:26:16,560 Speaker 1: you're going to get a slightly higher temperature from the 459 00:26:16,600 --> 00:26:18,240 Speaker 1: ridges than you do with the valleys. The valleys that 460 00:26:18,240 --> 00:26:21,640 Speaker 1: are essentially pockets of air. There's some downside with this one. 461 00:26:21,680 --> 00:26:24,760 Speaker 1: One of the problems with thermal scanners is that if 462 00:26:24,800 --> 00:26:26,879 Speaker 1: you if it takes too long to do the scan, 463 00:26:27,359 --> 00:26:31,439 Speaker 1: the temperature differences are going to equalize across the and 464 00:26:32,000 --> 00:26:37,200 Speaker 1: wind up you just get a blank fingerprint, like a 465 00:26:37,240 --> 00:26:40,680 Speaker 1: big blank fingerprint. Not useful. This next one is super cool. 466 00:26:40,760 --> 00:26:45,040 Speaker 1: It's ultrasonic ultrasonic sensors, so right, Yeah, we did a 467 00:26:45,080 --> 00:26:49,640 Speaker 1: whole episode about ultrasound called How Ultrasound Works. Crazily enough 468 00:26:49,680 --> 00:26:53,240 Speaker 1: that published on January if you would like to hear 469 00:26:53,280 --> 00:26:55,840 Speaker 1: all about how this technology works. But um, but it's 470 00:26:55,920 --> 00:26:59,800 Speaker 1: essentially echolocation. Yeah, you're sending out sound signals and then 471 00:26:59,800 --> 00:27:02,160 Speaker 1: you're waiting for them to bounce back, and by measuring 472 00:27:02,200 --> 00:27:04,239 Speaker 1: the amount of time it took to go out and 473 00:27:04,359 --> 00:27:06,800 Speaker 1: come back, you get an idea of how far away 474 00:27:06,880 --> 00:27:09,760 Speaker 1: something is. Well, that works, you know, in lots of 475 00:27:09,760 --> 00:27:13,159 Speaker 1: different ways, including being able to tell a fingerprint, being 476 00:27:13,200 --> 00:27:16,119 Speaker 1: able to read a fingerprint. It can go even deeper 477 00:27:16,119 --> 00:27:18,880 Speaker 1: than that, exactly, it can go into tissues. So if 478 00:27:18,920 --> 00:27:22,080 Speaker 1: you wanted, you could create an ultrasonic fingerprint scanner that 479 00:27:22,200 --> 00:27:26,080 Speaker 1: scan not just the fingerprint itself, but the underlying veins 480 00:27:26,160 --> 00:27:28,080 Speaker 1: that are in your finger which also are going to 481 00:27:28,080 --> 00:27:31,280 Speaker 1: be unique to you. So that's a lot harder to 482 00:27:31,440 --> 00:27:34,400 Speaker 1: fake than a fingerprint. Like you're not going to get 483 00:27:34,400 --> 00:27:38,240 Speaker 1: a really high resolution image of veins and then create 484 00:27:38,280 --> 00:27:41,560 Speaker 1: a fake finger easily. It would be really difficult. I mean, 485 00:27:41,560 --> 00:27:44,600 Speaker 1: but you could use, um your favorite application, which is 486 00:27:44,840 --> 00:27:48,760 Speaker 1: removing someone's finger and that right unless you had also 487 00:27:48,840 --> 00:27:53,680 Speaker 1: built into the software to detect living tissue, because to 488 00:27:53,920 --> 00:27:56,920 Speaker 1: see if blood is moving through the veins the vessel. 489 00:27:57,040 --> 00:27:59,680 Speaker 1: If it's not detecting blood, it's good to say, y'all, 490 00:27:59,720 --> 00:28:03,200 Speaker 1: this is messed up. You need to send someone down 491 00:28:03,280 --> 00:28:06,440 Speaker 1: right now. The fingerprint scanner. Things that are bad are happening. 492 00:28:07,119 --> 00:28:09,520 Speaker 1: That's exactly what the voice it uses too. I I 493 00:28:09,600 --> 00:28:11,920 Speaker 1: assumed I run into it in the field all the time. 494 00:28:12,240 --> 00:28:14,320 Speaker 1: So yeah, I mean this is uh, you know, that's 495 00:28:14,600 --> 00:28:16,800 Speaker 1: we're making light of it because to be serious about 496 00:28:16,840 --> 00:28:20,640 Speaker 1: it is so squeaky. But at any rate, it does 497 00:28:20,760 --> 00:28:22,680 Speaker 1: mean that you can build those sort of parameters in 498 00:28:22,760 --> 00:28:25,200 Speaker 1: so it's not just looking at the fingerprint and the 499 00:28:25,280 --> 00:28:27,880 Speaker 1: veins underneath, but also to make sure it is truly 500 00:28:28,359 --> 00:28:31,800 Speaker 1: a valid uh entry, so that they don't you know, 501 00:28:31,840 --> 00:28:35,880 Speaker 1: you don't end up compromising security. Um and there there's 502 00:28:35,920 --> 00:28:38,280 Speaker 1: also I wanted to mention very briefly the difference between 503 00:28:38,280 --> 00:28:42,080 Speaker 1: those static uh fingerprint scanners, especially the optical ones where 504 00:28:42,120 --> 00:28:43,800 Speaker 1: you just hold your finger down and you wait for it. 505 00:28:43,800 --> 00:28:46,120 Speaker 1: It's kind of like a copier, right right, and the 506 00:28:46,240 --> 00:28:48,560 Speaker 1: swipe style that you were talking about having on your 507 00:28:48,560 --> 00:28:52,400 Speaker 1: home laptop exactly. So if you've ever had a laptop 508 00:28:52,480 --> 00:28:54,840 Speaker 1: or any other device that has like a narrow window 509 00:28:54,960 --> 00:28:57,720 Speaker 1: and you're supposed to swipe your finger across that window. 510 00:28:58,360 --> 00:29:00,760 Speaker 1: The reason for that is that it's actually taking in 511 00:29:00,960 --> 00:29:04,840 Speaker 1: a series of quote unquote images of your finger. However, 512 00:29:04,880 --> 00:29:08,480 Speaker 1: the implementation is actually being used. It's it's doing then 513 00:29:08,640 --> 00:29:11,920 Speaker 1: a quick series. Machines are really fast, so they can 514 00:29:11,920 --> 00:29:14,760 Speaker 1: do this without any real problem. They're looking for those 515 00:29:14,800 --> 00:29:17,840 Speaker 1: minutia that we talked about before, and they're able to, 516 00:29:18,000 --> 00:29:20,440 Speaker 1: uh to use software to compile them. But you know, 517 00:29:20,480 --> 00:29:23,320 Speaker 1: it's it's it's nifty having the smaller form factor because 518 00:29:23,640 --> 00:29:26,520 Speaker 1: like we said, uh, then you can miniaturize, you can 519 00:29:26,560 --> 00:29:29,480 Speaker 1: put them in something like a cell phone and also 520 00:29:29,560 --> 00:29:32,479 Speaker 1: make them cheaper. Exactly. Yeah, you've got this little window 521 00:29:32,520 --> 00:29:34,360 Speaker 1: and your finger moves past the window instead of the 522 00:29:34,360 --> 00:29:38,440 Speaker 1: window having to be big enough to So it's it's 523 00:29:38,480 --> 00:29:42,600 Speaker 1: really uh an interesting development and uh pretty cool. Also, 524 00:29:42,720 --> 00:29:45,920 Speaker 1: we can mention that biometric systems, many of them, not 525 00:29:46,000 --> 00:29:48,240 Speaker 1: necessarily all of them, but many of them end up 526 00:29:48,280 --> 00:29:52,680 Speaker 1: translating your fingerprint into an algorithm that or an algorithm 527 00:29:52,720 --> 00:29:54,480 Speaker 1: rather does the translating that turns it into a bunch 528 00:29:54,480 --> 00:29:56,920 Speaker 1: of ones and zeros, all right, right. A digitization um 529 00:29:56,960 --> 00:29:59,760 Speaker 1: sometimes called a hash. It's like a personal code like 530 00:29:59,800 --> 00:30:02,920 Speaker 1: a really long pen. Yeah. So in this case, what 531 00:30:02,960 --> 00:30:05,240 Speaker 1: you would say is that it's not storing an image 532 00:30:05,240 --> 00:30:06,920 Speaker 1: of your fingerprint. It's not like if you were to 533 00:30:06,960 --> 00:30:10,080 Speaker 1: somehow hack into the computer you would suddenly see a 534 00:30:10,320 --> 00:30:13,560 Speaker 1: on your screen representation of your fingerprint. It would just 535 00:30:13,600 --> 00:30:16,960 Speaker 1: mean that it would take the the pattern of ridges 536 00:30:17,000 --> 00:30:20,160 Speaker 1: and valleys and all the night nutia convert that into 537 00:30:20,200 --> 00:30:24,120 Speaker 1: this this hash, this this long string of ones and zeros, 538 00:30:24,440 --> 00:30:27,200 Speaker 1: and the next time you scan it, if the if 539 00:30:27,200 --> 00:30:29,400 Speaker 1: the same hash comes up, then it as a match 540 00:30:29,400 --> 00:30:32,440 Speaker 1: and it says, all right, identification has been verified. But 541 00:30:32,520 --> 00:30:37,360 Speaker 1: it's not actually like an actual, real image of your fingerprint. 542 00:30:37,680 --> 00:30:40,160 Speaker 1: And the reason why a lot of these companies try 543 00:30:40,200 --> 00:30:43,040 Speaker 1: to talk about you know this as as a big 544 00:30:43,080 --> 00:30:46,680 Speaker 1: selling point is that it doesn't allow you to recreate 545 00:30:47,360 --> 00:30:49,720 Speaker 1: a person's fingerprint if you were to get hold of 546 00:30:49,760 --> 00:30:52,360 Speaker 1: those hashes. So it's not like you would say, oh, 547 00:30:52,400 --> 00:30:55,680 Speaker 1: if I just put this through an image program, I 548 00:30:55,680 --> 00:30:59,360 Speaker 1: suddenly get a picture of that fingerprint. You would just yeah, 549 00:30:59,400 --> 00:31:01,680 Speaker 1: that's that's the way I think that the iPhone five 550 00:31:01,920 --> 00:31:07,880 Speaker 1: S and the Galaxy something something that the latest Samsung 551 00:31:08,160 --> 00:31:11,640 Speaker 1: incorporates it, and um PayPal, these Days even has um 552 00:31:11,880 --> 00:31:15,360 Speaker 1: fingerprint signatures on their app, and any any device that 553 00:31:15,800 --> 00:31:19,240 Speaker 1: allows you to in to scan in your fingerprint will 554 00:31:20,000 --> 00:31:22,320 Speaker 1: let you pay for stuff on PayPal with that signature. 555 00:31:22,400 --> 00:31:23,680 Speaker 1: I'm sure we're going to see a lot of that 556 00:31:23,760 --> 00:31:27,640 Speaker 1: kind of stuff incorporate with things like the NFC technology 557 00:31:27,720 --> 00:31:31,400 Speaker 1: or even the low Bluetooth energy low energy Bluetooth. Rather, 558 00:31:31,440 --> 00:31:34,480 Speaker 1: I should say, uh, implementations where your fingerprint instead of 559 00:31:34,520 --> 00:31:36,400 Speaker 1: having to put in a pen, you just swipe your 560 00:31:36,440 --> 00:31:38,720 Speaker 1: finger Yeah, you know, I don't know. I kind of 561 00:31:38,760 --> 00:31:42,080 Speaker 1: hope that it's an additional safety feature, not a standalone 562 00:31:42,120 --> 00:31:45,320 Speaker 1: safety feature, because you know, unlike a password or a pin, 563 00:31:45,440 --> 00:31:48,200 Speaker 1: you can't just change your fingerprint if it gets stolen, 564 00:31:48,600 --> 00:31:52,680 Speaker 1: and this hash issue adds security to to the whole thing. 565 00:31:53,040 --> 00:31:55,680 Speaker 1: It's harder to but I'm sure that someone if they 566 00:31:55,720 --> 00:31:58,680 Speaker 1: really wanted to, could decode a hash and figure out 567 00:31:58,720 --> 00:32:01,480 Speaker 1: what that scan looks like. Yuh maybe, I mean, they 568 00:32:01,520 --> 00:32:03,400 Speaker 1: would have to have a lot of information, but it 569 00:32:03,480 --> 00:32:06,280 Speaker 1: is It is important to say that there is no 570 00:32:06,480 --> 00:32:12,320 Speaker 1: security feature out there that's going to be perfect, right right, 571 00:32:12,360 --> 00:32:15,800 Speaker 1: There's nothing out there, so having it as an additional tool, 572 00:32:15,920 --> 00:32:18,320 Speaker 1: I agree, Lauren, that's that's the best way of looking 573 00:32:18,360 --> 00:32:20,719 Speaker 1: at it. I think anytime we decide that we're going 574 00:32:20,760 --> 00:32:25,200 Speaker 1: to rely on a specific implementation and that's it and 575 00:32:25,240 --> 00:32:28,640 Speaker 1: we're done, we're good, then we're pretty much dooming ourselves 576 00:32:28,640 --> 00:32:31,040 Speaker 1: to getting hacked in some way down the line because 577 00:32:31,040 --> 00:32:33,720 Speaker 1: people are smart and they figure out ways around rules 578 00:32:33,800 --> 00:32:35,320 Speaker 1: they do, and I really don't want to have to 579 00:32:35,400 --> 00:32:38,880 Speaker 1: change my fingerprints, not again. All right, So that wraps 580 00:32:38,960 --> 00:32:41,640 Speaker 1: up this episode of tex Stuff. Folks, if you have 581 00:32:41,800 --> 00:32:45,080 Speaker 1: suggestions for future topics, you should let us know because 582 00:32:45,160 --> 00:32:48,160 Speaker 1: otherwise we're just gonna keep doing what we're doing. Let 583 00:32:48,240 --> 00:32:50,240 Speaker 1: us know by sending us an email. Our address it's 584 00:32:50,280 --> 00:32:52,760 Speaker 1: text Stuff at Discovery dot com, or drop us a 585 00:32:52,800 --> 00:32:56,840 Speaker 1: line on Facebook, Twitter or Tumbler. Our handle is tech 586 00:32:56,880 --> 00:32:59,920 Speaker 1: Stuff hs W, and we will talk to you again 587 00:33:00,480 --> 00:33:06,040 Speaker 1: really soon for more on this and thousands of other 588 00:33:06,120 --> 00:33:17,800 Speaker 1: topics because it how stuff works dot Com