Comment by pessimizer | Hacker News Reader

Comment by pessimizer | Hacker News Reader

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Thanks, that's a lot to think about.

>Second, having more images of a person does help, but not nearly as much as you'd hope, because now there's an even bigger chance that you might accidentally match against someone else who happened to have their photo taken in the same pose, lighting conditions, and facial expression as your test photo, and it's very tough for algorithms to discount those confounding factors.

I was thinking that having two sets (for example a set of five and a set of three) of known matching images would mitigate that - in that you'd have 15 pairings to check rather than just one.

>If a program or device or robot can recognize who you are, it can pro-actively change its settings/behavior/performance/etc. to better suit you. A very simple example is the new Kinect, which does some sort of recognition to load your saved profile/controller preferences. But that's just the tip of the iceberg.

Personal devices and systems would rarely if ever be expected to distinguish between 1000s of people, though. Are you thinking more in terms of public devices, such as locks on house or car doors? That would seem as if it would be impossible just because out of thousands, someone could look more like you did when you set it up than you currently do.

The problem with more pairings is that you have a greater chance of identifying the right person because of their additional photos, but that gets offset by the huge number of photos of everyone else in the database that you will now also get lots of "hits" on.

For example, you might get lucky and 2 of the 3 test photos you have match well with 2 of the 5 database photos of the right person. But if you have enough people in the database, you're almost guaranteed to find some random person where all 3 of the test photos match quite well. So it often comes down to how you combine the results from the different test images. Do you take the max? Do you average them? etc. Each has different tradeoffs, and it's a topic under research at the moment ("image set recognition"), but I'd say there are no compelling results yet.

Some personal devices might indeed be tuned for relatively fewer people, but these are precisely the cases that are the toughest: distinguishing between people in the same family, who're all going to look much more similar to each other than random people in a large database.

But there are many scenarios where it might actually be distinguishing between 1000s of people. To take but one example, advertising. On the web, advertising is popular not just because it's "easy" and one of the few ways of successfully monetizing a site, but also because knowing more about a person allows for more targeted advertising which has a much bigger ROI. How would this translate to the "real world?" Personalization is clearly one of the ways to do this, and face recognition is ideal in many respects, given that it works from a distance, is non-invasive, and cheap to implement. (Obviously, there's a separate debate to be had about privacy and other issues, but I think it's hard to deny that many companies are interested in this.)

I'm not sure that face recognition will be used for security purposes like house/car locks, just because it seems like other devices might get you better security sooner and cheaper (like RFID tags used in some car keys now, or some sort of ID from your phone).

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