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The problem is that any non-perfect automated system will wrongly convict so many civilians as being terrorists that nobody will trust it anymore. Let's say th
by gcr 14y ago
The problem is that any non-perfect automated system will wrongly convict so many civilians as being terrorists that nobody will trust it anymore.
Let's say that you want to catch terrorists creeping around in your airport. To do this, you have a magic camera that, when it takes a picture of a civilian, compares it to a picture of a known terrorist. If they're in fact the same person, it will flag security over. Let's say that it is indeed a magic camera and makes the correct decision 99.99% of the time, which is far better than the current state of the art[1].
Do you have any idea how much DHS or DoD would pay for such a magic camera?
But it won't be enough.
Consider a watch list of 100 terrorists that we're looking for in our airport. If our camera compares each civilian's picture to all 100 terrorists in succession, and if we model each comparison as a Bernoulli trial, then each civilian has a 0.9999^100 = 99.004% chance of getting through security unfazed, which means we flag innocent people as terrorists about 1% of the time.
This doesn't work in an airport setting. Assuming 5,000 people pass through your small airport per hour and you have a 14-hour day, that means your security people have to go through 0.01 * 5000 * 14 = 700 false alarms each day. That's about 1.2 false alarms per minute. Your security people will have to go through all their checks each time this happens. Before long, they're going to stop trusting your magic camera.
As you scale your watch list up, it gets worse. How well would it work for 1,000 people on the watch list? 10,000? Note that the current US watch list has 50,000 people on it.
Even if it were a perfect magic camera, it coudln't catch terrorists on their maiden crime who aren't on the watch list at all.
(Most of this post was adapted from: http://vast.uccs.edu/~tboult/tmp/IJCB11-tutorial-boult.ppt http://vast.uccs.edu/~tboult/tmp/IJCB11-tutorial-boult.ppt , which is a really interesting presentation if you're interested in these sorts of things.)
1: http://vis-www.cs.umass.edu/lfw/results.html http://vis-www.cs.umass.edu/lfw/results.html
- stretchwithme 14y agoDo you know how unreliably people recognize strangers? Many people are already wrongly convicted on such evidence. Even perfect automated facial recognition won't convict people. It will merely point out people that should be looked at. If a jury looking at the image and the guy's face think the system is wrong, they can disregard it. As far as prevention goes, what we need are systems that can detect deception reliably, which will help if we ever decide to ask people meaningful questions. Not to convict, but to focus attention on a possible threat.
- gcr 14y agoSure, I should have picked a better word than "Convict", especially since I didn't mean it in a legal sense. Given the context, "flag" or "decide" or "indicate" would have worked better. I only wanted to convey that the system thinks the probe and gallery pictures are the same person
- aidenn0 14y agoThis is a completely different situation; there are 2 people we have pictures of that we know are terrorists; we have a database of suspected terrorists; had we found 100 matches in that database, with one of them being the right one, it would have narrowed the investigation considerably. It was the perfect situation for AFR, had AFR either been more advanced, or the pictures been more amenable to current AFR technology.