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It's really the wrong approach. Supervised learning is the way to go. For example a paper by Kumar et al.[1] shows how to build an "attractive woman" classifier that is 83% accurate.

[1] http://homes.cs.washington.edu/~neeraj/publications/base/pap...



Presumably if they used 310 million faces instead of only 3.1 million it would be even more accurate, which is pretty impressive.


i'm confused - an .edu paper has been already mentioned while "Weird Science" hasn't been yet.




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