I think you nailed it on the head with, it is 'just not a useful thing to do'.
This seems like a clear example for why facial recognition is a technology that is just not 'solved' yet. The appearance of people's faces, especially from similar ethnic backgrounds, is just too similar for a ML model to parse out with any confidence.
I have noticed this in real life. As I get older, I notice it more and more. I'm sure many of you all have too. There are very distinct patterns, or 'buckets', that human faces tend to fall in. I think our brains tend to naturally categorize them accordingly.
It is probably subconscious. I might not be able to articulate it, or put a definite 'name' on a group. But I know I am constantly seeing patterns of faces in public. People I don't know, and have never met, but they remind me of other random people I have seen in public. Or maybe they remind me of a popular celebrity that everyone knows.
Either way, something goes off in my head. I can't help but think to myself, they must have some sort of similar lineage, or genetic background. I subconsciously categorize them into a bucket with others I've seen.
I imagine this is similar to how Rekognition, and other models work. I thought the blog post from the parent commment @bko, is a fantastic example of this. It is actually amazing, when you think about it, that the ML model can match these faces up as well as it does.
To the naked eye, it is clearly not the same person. Rightly so, considering all the images were in the range of 70-80% confidence. But many are remarkably close. I think this illustrates, the concept I am trying to describe. You can notice it, even with the naked eye.
All of this rambling is to say, I agree with Amazon's moratorium on Rekognition.
As impressive as the technology is, it should probably not be used to try pin-point specific individuals yet, or whatever else folks might be erroneously trying to use it for. If we are to trust facial recognition to identify specific individuals, it should probaly be approaching near 100% confidence, and I imagine that level of confidence is a long ways off.
This seems like a clear example for why facial recognition is a technology that is just not 'solved' yet. The appearance of people's faces, especially from similar ethnic backgrounds, is just too similar for a ML model to parse out with any confidence.
I have noticed this in real life. As I get older, I notice it more and more. I'm sure many of you all have too. There are very distinct patterns, or 'buckets', that human faces tend to fall in. I think our brains tend to naturally categorize them accordingly.
It is probably subconscious. I might not be able to articulate it, or put a definite 'name' on a group. But I know I am constantly seeing patterns of faces in public. People I don't know, and have never met, but they remind me of other random people I have seen in public. Or maybe they remind me of a popular celebrity that everyone knows.
Either way, something goes off in my head. I can't help but think to myself, they must have some sort of similar lineage, or genetic background. I subconsciously categorize them into a bucket with others I've seen.
I imagine this is similar to how Rekognition, and other models work. I thought the blog post from the parent commment @bko, is a fantastic example of this. It is actually amazing, when you think about it, that the ML model can match these faces up as well as it does.
To the naked eye, it is clearly not the same person. Rightly so, considering all the images were in the range of 70-80% confidence. But many are remarkably close. I think this illustrates, the concept I am trying to describe. You can notice it, even with the naked eye.
All of this rambling is to say, I agree with Amazon's moratorium on Rekognition.
As impressive as the technology is, it should probably not be used to try pin-point specific individuals yet, or whatever else folks might be erroneously trying to use it for. If we are to trust facial recognition to identify specific individuals, it should probaly be approaching near 100% confidence, and I imagine that level of confidence is a long ways off.