2 ms·
Yeah, they simply mean pixels. I failed to mention that in my previous post. It seems odd to me that it works because doesn't representing it as a linear comb
by iamwil 19y ago
Yeah, they simply mean pixels. I failed to mention that in my previous post. It seems odd to me that it works because doesn't representing it as a linear combo of pixels mean they throw out structural information of the image?
I don't know how they came to make that assumptions, but could it be that the linear independence comes about because of the high dimensionality? The higher the dimensions, the harder it is to find a combination of sparse representations (linear comb) that gives you the sparse representation matching the test image. And if you do, and it all comes from the same person, then they say it matches.
There must be some shape or property of the face subspace that allows for this, since potentially, you can match a duck to someone's face.
- pixcavator 19y agoI don’t think they throw out structural information (like adjacency of pixels). Since each pixel corresponds to an independent dimension, the adjacency is still contained in those coordinates: (a,b,...) is not the same as (b,a,...). So, as long as all images have the same dimension, it’s OK. If you had both 100x100 images and 1x10,000 images in the collection, that would mess up everything! As we know from linear algebra, if a collection is linearly dependent, you can find a subcollection that adds up to 0! In other words, everything cancels out and you end up with a blank photo. Is it possible? If the dimension is too low (the images are small), yes. If the collection is large (a lot of images), maybe. What if it is small? (It is in fact!) In a very extreme case you may need the negative for each image to cancel it: same shape with dark vs. light hair, skin, eyes, teeth (?). Interesting stuff...