5 ms·

On the flip side, I wonder how much of face recognition failures by humans would be considered "huge glaring faults" to machines? For a famous example, take th

by rawnlq 10y ago

On the flip side, I wonder how much of face recognition failures by humans would be considered "huge glaring faults" to machines? For a famous example, take the https://en.wikipedia.org/wiki/Thatcher_effect https://en.wikipedia.org/wiki/Thatcher_effect (where a face will look normal upside down even if you individually flip the eyes and mouth). Or the https://en.wikipedia.org/wiki/Hybrid_image https://en.wikipedia.org/wiki/Hybrid_image (where you see two different images depending on how far you are standing). I bet machines can or eventually handle those cases better than us. Is trying to emulate our own recognition system the best possible approach? It might end up inheriting all our edge cases as well. (For another example relevant to halloween, with a bit of face paint you can fool any human: http://gfycat.com/DeliriousEdibleAustraliansilkyterrier http://gfycat.com/DeliriousEdibleAustraliansilkyterrier. But you can totally imagine a sufficiently advanced algorithm that is capable of ignoring color information and reconstruct a 3d surface for comparison instead.)

unabridged 10y ago
>Is trying to emulate our own recognition system the best possible approach? It might end up inheriting all our edge cases as well. I agree, I wouldn't want to emulate human recognition too closely. But I think humans have a bunch of simple heuristics that have yet to be modeled, that can sit in front of heavy ML algorithms. First I think there should be a garbage/unreal filter that all images have to pass through, this would eliminate all those static-y looking adversarial images that can pass for pandas or whatever.
dom0 10y ago
> with a bit of face paint you can fool any human While impressive makeup it's still fairly obvious - perhaps not on a still image, but on a video it is, and in real life it will be more obvious still. It took me maybe half a second in the video to notice that it's not a doubled face, but painted on. Hmm... maybe that's not so bad for a metric - the "fooling duration".
TrainedMonkey 10y ago
Regarding Thatcher effect, one of the Hinton lectures from https://www.coursera.org/learn/neural-networks https://www.coursera.org/learn/neural-networks talks about difficulties in recognition of reflected/upside down objects. I can't easily look it up, so here is my paraphrasing: Humans exhibit a comparable delay when recognizing and reasoning normal and reflected objects. He then gives a few examples such as answering "is this a left or a right foot shoe" and suggests that brains do rotational transformations as part of the object recognition. The machine solution is probably similar to a human solution - do a bunch of transformations and pick highest confidence one. The interesting part that is humans can answer "this is a shoe" question immediately, but take longer to reason about reflected images.

by rawnlq 10y ago

unabridged 10y ago

>Is trying to emulate our own recognition system the best possible approach? It might end up inheriting all our edge cases as well. I agree, I wouldn't want to emulate human recognition too closely. But I think humans have a bunch of simple heuristics that have yet to be modeled, that can sit in front of heavy ML algorithms. First I think there should be a garbage/unreal filter that all images have to pass through, this would eliminate all those static-y looking adversarial images that can pass for pandas or whatever.

dom0 10y ago

> with a bit of face paint you can fool any human While impressive makeup it's still fairly obvious - perhaps not on a still image, but on a video it is, and in real life it will be more obvious still. It took me maybe half a second in the video to notice that it's not a doubled face, but painted on. Hmm... maybe that's not so bad for a metric - the "fooling duration".

TrainedMonkey 10y ago

Regarding Thatcher effect, one of the Hinton lectures from https://www.coursera.org/learn/neural-networks https://www.coursera.org/learn/neural-networks talks about difficulties in recognition of reflected/upside down objects. I can't easily look it up, so here is my paraphrasing: Humans exhibit a comparable delay when recognizing and reasoning normal and reflected objects. He then gives a few examples such as answering "is this a left or a right foot shoe" and suggests that brains do rotational transformations as part of the object recognition. The machine solution is probably similar to a human solution - do a bunch of transformations and pick highest confidence one. The interesting part that is humans can answer "this is a shoe" question immediately, but take longer to reason about reflected images.