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Perceptual hashing is a method to generate a hash of an image which allows multiple images to be compared by an index of similarity. You can find out more at The Hacker Factor and phash.org.

I've extended this basic method into a class which can also compare images which have been rotated or flipped (but only in 90 degree increments.) It can also match images which have been color corrected (to a degree) or altered. This flexibility, though, does lend itself to false positives and it's ridiculously slow - currently it takes about a second to hash and compare images -- in production you would want to generate a cache of hashes for existing images, perhaps, and then compare the hash of an image to the hashes.

PHasher is available under the MIT license.
Merge pull request #4 from ImageSearch/patch-1

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