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May 11th, 2017, 12:01 AM   #1
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Face Recognition

Hi,

I'm working on a face recognition software. I'm doing it with ortogonal projection, so I can get the smallest norm between vectors. In this way I get good results, but I would like to know how I could improve the error rate of the recognition using lineal algebra.

Thanks!

PD: I'm programming with MatLab software.

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Last edited by avicarpio; May 11th, 2017 at 12:26 AM.
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May 11th, 2017, 12:19 AM   #2
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Originally Posted by avicarpio View Post
rendiment
Rendiment? Performance?

Matlab has a nice image processing toolbox if you can get it: https://www.mathworks.com/products/image.html
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May 11th, 2017, 12:27 AM   #3
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Quote:
Originally Posted by Joppy View Post
Rendiment? Performance?

Matlab has a nice image processing toolbox if you can get it: https://www.mathworks.com/products/image.html
Sorry I am not english, I corrected it.

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May 11th, 2017, 12:30 AM   #4
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Quote:
Originally Posted by Joppy View Post
Rendiment? Performance?

Matlab has a nice image processing toolbox if you can get it: https://www.mathworks.com/products/image.html
What I want is to minimize the error when I compare the pictures. I guess that there is another way to do it apart from the one I mentioned.

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