Talk:Eigenface
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[edit] To do
- handling variations in facial expression and lighting conditions
- other applications of eigenimaging- e.g., handwriting, voice, medical
As a mathematician, I think the first line is nonsense:
"Eigenfaces are eigenvectors in the high-dimensional vector space of possible faces of human beings."
You can't have eigenvectors without first defining an operator for which they are eigenvectors, see eigenvector. Without an operator, saying that a vector is an eigenvector has exactly zero meaning. - Andre Engels 22:01, 17 May 2004 (UTC)
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- I agree with you, eigenfaces are more a basis than eigenvectors.
I hope what I've done with that secon paragraph is ok. I understand eigenvectors and I've heard of eigenfaces before, but I didn't understand that first paragraph at all. My explanation is very hand-wavy, so feel free to make it more accurate (but keep it readable for non-mathematicians). - G 16:10, 20 May 2004 (UTC)
- I agree; the first paragraph as it now appears is somewhat vague. Maybe I'll do something with it at some point. Michael Hardy 21:34, 20 May 2004 (UTC)
[edit] Number of Eigenfaces
Is the following explanation completely right?
- For instance, if we are working with a 100 x 100 image, then this system will create 10,000 eigenfaces. Since most individuals can be identified using a database with a size between 100 and 150, most of the 10,000 can be discarded, and only the most important should remain.
Instead, shouldn't we give an explanation such as:
If we have a set of 100x100 images, and the set contains M such images, we can imagine any other image data as a point in a 100x100=10,000=N dimensional space. Thanks to the eigenface decomposition, we do not need all these dimensions, and we can fully represent data that already belongs to the set in terms of M (=number of images) eigenfaces. We can even further reduce the number of dimensions to M' with a reasonable loss, where M' can be down to 0.1M, that is, if we have 300 images with 10,000 pixels each, we can have reasonable results with only 300/10= 30 eigenvectors.
I am not an expert in the subject, so I did not change the article, but the explanation there looks unclear or incorrect to me.
--spAs 12:41, 17 August 2007 (UTC)