# [Numpy-discussion] PCA on set of face images

Peter Skomoroch peter.skomoroch@gmail....
Fri Feb 29 13:50:14 CST 2008

```RoyG,

The timing of your question couldn't be better, I just did an blog post on
this (I also plugged scipy and the EPD):

http://www.datawrangling.com/python-montage-code-for-displaying-arrays.html

The code basically replicates the matlab montage() function and approach to
handling grayscale images using matplotlib.

-Pete

On Fri, Feb 29, 2008 at 2:15 PM, devnew@gmail.com <devnew@gmail.com> wrote:

> hi guys
> I have a set of face images with which i want to do face recognition
> using Petland's PCA method.I gathered these steps from their docs
>
> 1.represent  matrix of face images data
> 2.find the adjusted matrix by substracting the mean face
> 3.calculate covariance matrix (cov=A* A_transpose)  where A is from
> step2
> 4.find eigenvectors and select those with highest eigenvalues
> 5.calculate facespace=eigenvectors*A
>
>
> when it comes to implementation i have doubts as to how  i should
> represent the matrix of face images?
> using PIL image.getdata() i can make an array of each greyscale image.
> Should the matrix be like each row contains an array representing an
> image? That will make a matrix with rows=numimages and
> columns=numpixels
>
> cavariancematrix =A *A_transpose will create a square matrix of
> shape(numimages,numimages)
> Using numpy.linalg.eigh(covariancematrix) will give eigenvectors of
> same shape as the covariance matrix.
>
> I would like to know if this is the correct way to do this..I have no
> big expertise in linear algebra  so i would be grateful if someone can
> confirm the right way of doing this
>
> RoyG
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>

--
Peter N. Skomoroch
peter.skomoroch@gmail.com
http://www.datawrangling.com
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